Electric wheelchair supporting wheel folding control method and device

Abstract

The invention provides an electric wheelchair supporting wheel folding control method and device, and relates to the technical field of electric wheelchairs, and the technical scheme is characterized in that driving state information and surrounding environment information of an electric wheelchair are acquired; judging whether an obstacle exists or not according to the driving state information and the surrounding environment information; judging whether obstacle avoidance can be performed through steering or not; triggering a supporting wheel folding obstacle avoidance program based on the obstacle distance, the wheelchair speed and environment constraint conditions; the folding direction and the folding angle of the supporting wheels are determined according to the obstacle position, the wheelchair driving direction and the obstacle height; the supporting wheels are controlled to be folded according to the determined folding direction and folding angle; and after passing through the obstacle, the supporting wheels are controlled to automatically recover to the original supporting state. The electric wheelchair supporting wheel folding control method and device have the advantages that the obstacle avoidance capacity and safety of an electric wheelchair are improved, and the adaptability of the wheelchair in the complex environment is enhanced.

Description

Technical Field

[0001] The present application relates to the technical field of electric wheelchairs, and more specifically, to a method and device for controlling the folding of support wheels of an electric wheelchair. Background Art

[0002] As an important means of transportation for people with limited mobility, the safety and convenience of electric wheelchairs are of utmost importance. To reduce the volume of the wheelchair while ensuring stability, many electric wheelchairs are equipped with retractable and foldable support wheels. The common folding method is manual folding, which is inconvenient to operate. Existing electric folding solutions are usually only used for the storage of the wheelchair and cannot play a role during driving.

[0003] Existing electric folding support wheels are mainly used for the folding and storage of wheelchairs and cannot be dynamically adjusted during driving to cope with complex road conditions and obstacles. This limitation greatly reduces the adaptability of electric wheelchairs, especially when driving outdoors or on uneven terrain.

[0004] When an electric wheelchair encounters an obstacle, it is difficult for the existing technology to effectively respond, and safety hazards such as collisions and bumps are likely to occur. Especially when driving at high speed or in a narrow space, the method of relying only on steering for avoidance is often not flexible enough to meet the obstacle avoidance requirements in complex environments. This not only affects the riding comfort but also may endanger the personal safety of the user.

[0005] In view of the above problems, the existing technology urgently needs to be improved. Summary of the Invention

[0006] The purpose of the present application is to provide a method and device for controlling the folding of support wheels of an electric wheelchair, which have the advantages of improving the obstacle avoidance ability and safety of the electric wheelchair and enhancing the adaptability of the wheelchair in complex environments.

[0007] The present application provides a method for controlling the folding of support wheels of an electric wheelchair, and the technical solution is as follows: The method includes: obtaining the driving state information and surrounding environment information of the electric wheelchair; judging whether there is an obstacle according to the driving state information and surrounding environment information; when there is an obstacle, judging whether it is possible to avoid the obstacle by steering; when it is judged that it is impossible to avoid the obstacle by steering, triggering a support wheel folding obstacle avoidance program based on the obstacle distance, wheelchair speed, and environmental constraint conditions; determining the folding direction and folding angle of the support wheel according to the obstacle position, wheelchair driving direction, and obstacle height; controlling the support wheel to fold according to the determined folding direction and folding angle to achieve obstacle avoidance; after passing the obstacle, controlling the support wheel to automatically return to the original support state.

[0008] Further, the present application also proposes that the step of determining whether obstacle avoidance can be achieved by steering when there is an obstacle includes: obtaining the current speed, safe steering angular velocity of the electric wheelchair, and the distance from the obstacle; calculating the minimum safe distance required to complete the steering based on a preset safety threshold, the current speed of the electric wheelchair, and the safe steering angular velocity; and determining that obstacle avoidance cannot be achieved by steering when the actual distance between the electric wheelchair and the obstacle is less than the minimum safe distance.

[0009] Further, the present application also proposes that the step of determining that obstacle avoidance cannot be achieved by steering includes: obtaining the traveling speed of the electric wheelchair and the position information of the obstacle, and calculating the predicted contact point between the electric wheelchair and the obstacle after a preset steering amplitude based on the traveling speed and the obstacle position information; calculating the predicted fluctuation amplitude when the electric wheelchair contacts the obstacle according to the predicted contact point, and determining that obstacle avoidance cannot be achieved by steering when the predicted fluctuation amplitude is greater than a preset threshold.

[0010] Further, the present application also proposes that the step of triggering the support wheel folding obstacle avoidance program based on the obstacle distance, wheelchair speed, and environmental constraint conditions when it is determined that obstacle avoidance cannot be achieved by steering includes: calculating the relative distances and predicted contact times between multiple support wheels and one or more obstacles based on the obstacle distance, wheelchair speed, and environmental constraint conditions, and determining the obstacle avoidance priorities of each support wheel and the obstacles based on the relative distances and predicted contact times; triggering the folding obstacle avoidance programs of each support wheel in chronological order according to the obstacle avoidance priorities, where, on the premise of ensuring the stability of the wheelchair, the number of support wheels in the folded state at any moment is controlled not to exceed a preset threshold.

[0011] Further, the present application also proposes that calculating the relative distances and predicted contact times between multiple support wheels and one or more obstacles based on the obstacle distance, wheelchair speed, and environmental constraint conditions, and determining the obstacle avoidance priorities of each support wheel and the obstacles based on the relative distances and predicted contact times: when the obstacle is a dynamic obstacle, obtaining the current positions and motion states of each support wheel, where the motion state includes the motion speed, as well as the moving trajectory and speed of the obstacle; predicting the positions of each obstacle in a future time period based on the obtained current positions and motion states of each support wheel, and the moving trajectory and speed of the obstacle, and calculating the relative distance sequences between each support wheel and each obstacle at multiple time points; determining the minimum relative distance and the corresponding predicted contact time between each support wheel and each obstacle according to the calculated relative distance sequences; combining the wheelchair speed and environmental constraint conditions, and assigning obstacle avoidance priority scores to each support wheel based on the minimum relative distance and predicted contact time; sorting all the support wheels according to the obstacle avoidance priority scores to obtain the final obstacle avoidance priority sequence.

[0012] Furthermore, the present application also proposes that the step of determining the folding direction and folding angle of the support wheels according to the obstacle position, the wheelchair traveling direction, and the obstacle height includes: obtaining the three-dimensional contour information of the obstacle and the current attitude parameters of the electric wheelchair, and constructing a spatial model of the obstacle based on the three-dimensional contour information, where the three-dimensional contour information of the obstacle includes the obstacle height; calculating the possible contact positions and contact angles between the support wheels and the obstacle according to the current attitude parameters of the electric wheelchair and the constructed obstacle spatial model, and combining the mechanical structure constraints of the support wheels to calculate the minimum folding angle required to avoid collision; when the minimum folding angle is greater than the maximum foldable angle of the support wheels, setting the folding angle to the maximum foldable angle and adjusting the wheelchair traveling speed to ensure safe passage; when the minimum folding angle is less than or equal to the maximum foldable angle of the support wheels, setting the folding angle to the minimum folding angle; determining the folding direction according to the wheelchair traveling direction and the relative position of the obstacle to achieve obstacle avoidance control.

[0013] Furthermore, the present application also proposes that the step of, when the minimum folding angle is greater than the maximum foldable angle of the support wheels, setting the folding angle to the maximum foldable angle and adjusting the wheelchair traveling speed to ensure safe passage includes: when the fluctuation amplitude calculated based on the obstacle spatial model and the maximum foldable angle of the support wheels exceeds a preset threshold, by calculating and analyzing multiple alternative steering angles, selecting the steering angle that can generate the minimum fluctuation amplitude at the new contact position between the support wheels and the obstacle as the optimized steering angle; controlling the wheelchair to perform the steering operation of the optimized steering angle, setting the folding angle of the support wheels to the maximum foldable angle, and calculating the maximum traveling speed required for safe passage based on the optimized fluctuation amplitude; adjusting the wheelchair traveling speed to a value not exceeding the maximum traveling speed to ensure safe passage of the obstacle.

[0014] Furthermore, the present application also proposes that the step of controlling the number of support wheels in the folded state at any moment not to exceed a preset threshold includes: obtaining the surface feature information of the obstacle, and when detecting that the obstacle has a plane satisfying a preset condition, dynamically adjusting the preset threshold of the number of support wheels allowed to be folded simultaneously based on the calculated contact area between the support wheels and the plane; controlling the support wheels in the folded state to contact the plane of the obstacle and real-time monitoring the stability parameters of the wheelchair; dynamically adjusting the folding angles of the respective support wheels according to the real-time changes of the stability parameters to maximize the passing efficiency while ensuring the stability of the wheelchair.

[0015] Furthermore, the present application also proposes that when the fluctuation amplitude calculated based on the obstacle space model and the maximum foldable angle of the support wheel exceeds a preset threshold, the steps of calculating and analyzing multiple alternative steering angles and selecting the steering angle that can generate the minimum fluctuation amplitude at the new contact position between the support wheel and the obstacle as the optimized steering angle include: obtaining n alternative steering angles θ 1 , θ 2 ,..., θ n , the maximum foldable angle α_max of the support wheel, and the obstacle space model O(x, y, z, t); within the time interval [t 0 , t 1 , calculating the integral function f(θ, t) = ∫[t 0 , t 1 [A(θ, t) * F(θ, t) + λ * S(θ, t)] dt for each alternative steering angle θ, where A(θ, t) is the contact area between the support wheel and the obstacle at time t, F(θ, t) is the contact force at time t, S(θ, t) is the stability index of the electric wheelchair at time t, and λ is the stability weight factor; under the constraint conditions of A(θ, t) > 0, F(θ, t) ≤ F_max, and S(θ, t) ≥ S_min, selecting the steering angle θ that minimizes the integral function f(θ, t) as the optimized steering angle θ_opt.

[0016] Furthermore, the present application also proposes an electric wheelchair support wheel folding control device, which includes: a state acquisition module for acquiring the driving state information and surrounding environment information of the electric wheelchair; an obstacle judgment module for judging whether there is an obstacle according to the driving state information and surrounding environment information; a steering obstacle avoidance judgment module for judging whether obstacle avoidance can be achieved by steering when there is an obstacle; a folding obstacle avoidance trigger module for triggering the support wheel folding obstacle avoidance program based on the obstacle distance, wheelchair speed, and environmental constraint conditions when it is judged that obstacle avoidance cannot be achieved by steering; a folding parameter determination module for determining the folding direction and folding angle of the support wheel according to the obstacle position, wheelchair driving direction, and obstacle height; a folding control module for controlling the support wheel to fold according to the determined folding direction and folding angle to achieve obstacle avoidance; and a recovery control module for controlling the support wheel to automatically return to the original support state after passing the obstacle.

[0017] As can be seen from the above, a folding control method and device for a support wheel of an electric wheelchair provided by the present application obtain the driving state information and surrounding environment information of the electric wheelchair, determine whether there are obstacles, and trigger a support wheel folding obstacle avoidance program when it is impossible to avoid obstacles by steering. The folding direction and folding angle of the support wheel are determined according to the position of the obstacle, the driving direction of the wheelchair, and the height of the obstacle, so as to achieve intelligent obstacle avoidance, which has the advantages of improving the obstacle avoidance ability and safety of the electric wheelchair and enhancing the adaptability of the wheelchair in a complex environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flowchart of a folding control method for a support wheel of an electric wheelchair provided by the present application.

[0019] Figure 2 It is a schematic structural diagram of a folding control device for a support wheel of an electric wheelchair provided by the present application.

[0020] Figure 3 It is a schematic overall structural diagram of an electric wheelchair provided by the present application.

[0021] Figure 4 It is a schematic partial structural diagram of an electric wheelchair provided by the present application.

[0022] Figure 5 It is a schematic partial structural diagram of an electric wheelchair provided by the present application.

[0023] In the figure: 210, state acquisition module; 220, obstacle judgment module; 230, steering obstacle avoidance judgment module; 240, folding obstacle avoidance trigger module; 250, folding parameter determination module; 260, folding control module; 270, recovery control module; 001, large wheel; 002, support wheel; 003, support rod; 004, support strut; 005, mounting rod; 006, driving member; 007, camera; 008, link assembly; 009, pedal; 010, cavity. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] Next, the technical solutions in the present application will be clearly and completely described in conjunction with the accompanying drawings in the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the present application usually described and illustrated in the accompanying drawings here can be arranged and designed in various different configurations. Therefore, the detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application to be protected, but only represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0025] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0026] The folding function of the support wheels of an electric wheelchair is mainly used for the storage of the wheelchair, and cannot effectively deal with obstacles during driving. This makes it difficult for the electric wheelchair to flexibly avoid obstacles when encountering obstacles, especially when driving at high speeds or in narrow spaces, and the ability to avoid obstacles by steering alone is limited. In addition, the existing support wheel folding system lacks intelligence and adaptability, and cannot automatically adjust the folding strategy according to the real-time environment and obstacle conditions. These problems seriously affect the safety, passability and user experience of the electric wheelchair.

[0027] When an electric wheelchair encounters an obstacle during driving, how to achieve intelligent obstacle avoidance is a technical problem that needs to be solved urgently. In the prior art, the folding function of the support wheels of an electric wheelchair is mainly used for storage, and it is not possible to effectively deal with obstacles during driving. This makes it difficult for the electric wheelchair to flexibly avoid obstacles when encountering obstacles, especially when driving at high speeds or in narrow spaces, and the ability to avoid obstacles by steering alone is limited.

[0028] In this regard, refer to Figure 1 , the present application proposes a method for controlling the folding of support wheels of an electric wheelchair, the method comprising: S110, obtaining driving state information and surrounding environment information of the electric wheelchair; S120, judging whether there is an obstacle according to the driving state information and the surrounding environment information; S130: When there is an obstacle, determine whether the obstacle can be avoided by turning; S140: When it is determined that the obstacle cannot be avoided by steering, the support wheel folding obstacle avoidance program is triggered based on the obstacle distance, wheelchair speed and environmental constraints; S150, determining the folding direction and folding angle of the support wheels according to the position of the obstacle, the direction of the wheelchair and the height of the obstacle; S160, controlling the support wheels to fold according to the determined folding direction and folding angle to achieve obstacle avoidance; S170. After passing the obstacle, the support wheels are controlled to automatically return to the original support state.

[0029] Among them, the driving status information refers to the current motion parameters of the electric wheelchair, including speed, acceleration, direction, etc., which can be obtained through sensors installed on the wheelchair.

[0030] Among them, the surrounding environment information refers to the situations of obstacles, terrain, etc. around the wheelchair, which can be collected by devices such as cameras and lidar. This information provides the necessary data support for subsequent obstacle avoidance decisions.

[0031] Among them, obstacle judgment is to identify objects or terrain that may affect the wheelchair's travel by analyzing the acquired environment information.

[0032] Among them, steering obstacle avoidance judgment is to evaluate whether the obstacle can be avoided by changing the travel direction after detecting the obstacle. This step gives priority to the conventional obstacle avoidance method, which helps to reduce unnecessary folding operations of the support wheels and improve the efficiency and stability of the system.

[0033] Among them, the support wheel folding obstacle avoidance program is an alternative solution started when steering obstacle avoidance cannot be achieved. By adjusting the position of the support wheels, the shape of the wheelchair is changed, thereby increasing the ability to pass through narrow spaces or over obstacles.

[0034] Among them, the environmental constraint conditions refer to various objective limiting factors from the surrounding environment that the electric wheelchair must consider when performing support wheel folding obstacle avoidance, including spatial geometric constraints (distances on both sides, above, front and back), ground conditions (flatness, slope, material), and obstacle shapes (rigid / flexible, regular / irregular shapes). These factors directly affect the folding strategy of the support wheels and are the key basis for making safe and effective obstacle avoidance decisions.

[0035] Specifically, when it is judged that the support wheel folding obstacle avoidance program cannot be triggered based on the obstacle distance, wheelchair speed, and environmental constraint conditions, the electric wheelchair can be controlled to decelerate and pause. For example, when the environmental constraint conditions result in no folding space for the support wheels, or even if folded, there will still be a violent collision with the obstacle, and the fluctuation amplitude exceeds the set value, etc.

[0036] Among them, the determination of the folding direction and folding angle is calculated based on the specific characteristics of the obstacle and the travel state of the wheelchair. It ensures the effectiveness and safety of the obstacle avoidance action, and at the same time maximally maintains the stability of the wheelchair.

[0037] Specifically, it can be distinguished between forward folding or backward folding. Specifically, when the wheelchair is moving forward and encounters an obstacle, the backward folding method is used. When the wheelchair is moving backward and encounters an obstacle, the forward folding method is used. This is because when moving forward, due to the limited distance for detecting obstacles, the distance between the obstacle and the support wheel is short. If folding forward, there may be a relative opposite contact collision between the forward folding movement and the obstacle during the folding process. If folding backward, the two are in the same direction of movement, and the safety is higher.

[0038] Among them, the automatic recovery of the support wheels is an operation performed after passing an obstacle, aiming to restore the normal support state of the wheelchair. This step ensures that the wheelchair can immediately regain the best stability and maneuverability after completing obstacle avoidance.

[0039] The core innovation of this application lies in proposing an intelligent folding control method for the support wheels of an electric wheelchair. By real-time acquiring the driving state of the wheelchair and the surrounding environment information, and combining a multi-level judgment mechanism, it realizes the intelligent recognition and response to obstacles. Especially when traditional steering obstacle avoidance cannot solve the problem, the system can automatically trigger the support wheel folding obstacle avoidance program and precisely control the folding parameters according to the specific situation. This method not only improves the passability and safety of the electric wheelchair but also realizes the adaptive ability to complex environments.

[0040] The working principle of this application can be divided into the following key steps: First, various sensors installed on the electric wheelchair, such as speed sensors, acceleration sensors, gyroscopes, etc., are used to collect the driving state information of the wheelchair in real-time. At the same time, devices such as cameras and lidar are used to scan the surrounding environment to obtain information such as the position, shape, and size of obstacles. These data are preliminarily processed and fused by the data processing unit.

[0041] Next, the system uses the obstacle detection algorithm to analyze the processed data to determine whether there are obstacles that may affect the driving of the wheelchair. If an obstacle is detected, the system will first evaluate whether it can be avoided by steering. This evaluation is based on factors such as the current speed of the wheelchair, the distance from the obstacle, and the available steering space.

[0042] If it is determined that obstacle avoidance cannot be achieved by steering, the system will trigger the support wheel folding obstacle avoidance program. At this time, the system will comprehensively consider the distance of the obstacle, the speed of the wheelchair, and the constraints of the surrounding environment. Subsequently, according to the position of the obstacle, the driving direction of the wheelchair, and the height of the obstacle, the system will precisely calculate the direction and angle in which the support wheels need to be folded.

[0043] After receiving these parameters, the folding control module will control the support wheels to fold in the specified direction and angle through actuators such as motors or hydraulic systems. During the entire folding process, the system will continuously monitor the stability of the wheelchair to ensure that the folding operation does not affect the safety of the wheelchair.

[0044] Finally, when the wheelchair successfully passes the obstacle, the system will automatically control the support wheels to return to the original support state. This process also requires precise control to ensure a smooth transition and not affect the user experience.

[0045] It should be noted that in the solution of the present application, the support wheel refers to the small wheel located in front of the wheelchair. There are two large wheels in the wheelchair. The large wheels are usually connected to the power component and are usually driven to provide the driving power of the wheelchair. For example, specifically, reference can be made to Figure 3 , Figure 4 and Figure 5 . There are large wheels 001 and support wheels 002 on the wheelchair. The support wheels 002 are located in front of the wheelchair, and the distance between the support wheels 002 on both sides is smaller than the distance between the large wheels 001 on both sides. The support wheels 002 are rotatably arranged on the support rod 003. The other end of the support rod 003 is hinged to the mounting rod 005. The mounting rod 005 is L-shaped, and one end of it is fixedly arranged on the main body frame of the wheelchair. There is a pedal 009 between the mounting rods 005 on both sides. The support rod 003 is provided with support struts 004 on the side close to the support wheel 002 in a form that expands outward on both sides. The other end of the support strut 004 is hinged to the mounting rod 005 with a link assembly 008. In some specific embodiments, the link assembly 008 includes two links hinged end to end. One link is hinged to the support strut 004, and the other link is hinged to the mounting rod 005. A camera 007 is arranged at the corner of the L-shaped mounting rod 005. A cavity 010 with a front opening is also opened in the front part of the mounting rod 005. The support rod 003 is arranged in the cavity 010 and hinged to the mounting rod 005. The mounting rod 005 is also hinged with a driving member 006. The driving end of the driving member is hinged to the support rod 003. The driving member 006 can be a cylinder, a hydraulic cylinder, a linear servo driver or other driving mechanisms. In addition, a tension spring can be arranged between the support rod 003 and the mounting rod 005 to ensure stability.

[0046] Refer to Figure 4 . When the camera 007 detects an obstacle and cannot avoid the obstacle by turning, the driving member 006 drives the support rod 003 to fold forward. Since the support rod 003 and the mounting rod 005 are hinged, it can successfully rotate. And, in this process, the support struts 004 on both sides of the support rod 003 also fold through the link assembly 008. Through the connection with the link assembly 008, the stability of the overall operation is ensured.

[0047] It should be noted that Figure 4 shows the solution of folding forward, and it can also be folded backward, or can be folded backward through a simple structural deformation design.

[0048] In addition, in some other embodiments, multiple support wheels 002 can be provided. As Figure 5 shown, it can be provided with 4 support wheels and is equipped with corresponding driving structures.

[0049] In addition, it should be noted that for the solution with two support wheels, that is, there are a total of four wheels on the wheelchair. At this time, controlling one support wheel to lift to avoid obstacles, there are still three wheels forming a stable three-point support structure with the ground, which is sufficient to ensure the basic stability of the wheelchair.

[0050] In addition, it should be noted that, as Figures 3 to 5 shown, a support rod 003 may be provided with more than one support wheel 002. In this case, all the support wheels 002 provided on one support rod 003 can be regarded as the "one support wheel" described in the present application.

[0051] In some of the above embodiments of the present application, a step of determining whether it is possible to avoid obstacles by turning is proposed when there are obstacles to determine whether it is necessary to trigger the support wheel folding obstacle avoidance program. However, in this process, there are challenges in accurately determining whether an electric wheelchair can avoid obstacles by turning. Simply judging based on the distance of the obstacle may lead to misjudgment because the current speed and turning ability of the electric wheelchair also need to be considered. In addition, how to determine a safe turning distance threshold is also a key issue, which directly affects the accuracy and safety of the obstacle avoidance decision.

[0052] In response to this, the present application further proposes to obtain the current speed, safe turning angular velocity of the electric wheelchair, and the distance from the obstacle; calculate the minimum safe distance required to complete the turning based on a preset safety threshold, the current speed, and the safe turning angular velocity of the electric wheelchair; when the actual distance between the electric wheelchair and the obstacle is less than the minimum safe distance, it is determined that it is impossible to avoid obstacles by turning.

[0053] This technical solution provides a necessary data basis for determining whether it is possible to avoid obstacles by turning by obtaining the current speed, safe turning angular velocity of the electric wheelchair, and the distance from the obstacle. Based on the preset safety threshold and the obtained data, the minimum safe distance required to complete the turning is calculated, which takes into account the actual motion state and turning ability of the electric wheelchair. By comparing the actual distance with the minimum safe distance, it can be accurately determined whether it is possible to safely avoid obstacles by turning.

[0054] This method solves the problem of misjudgment that may be caused by simply relying on the distance of the obstacle. By introducing the current speed and safe turning angular velocity of the electric wheelchair, the dynamic characteristics of the wheelchair are considered, making the judgment more accurate and reliable. The step of calculating the minimum safe distance provides an objective judgment standard, effectively avoiding the risks that may be brought by subjective judgment. When the actual distance is less than the minimum safe distance, the system can timely determine that it is impossible to avoid obstacles by turning, thereby triggering the subsequent support wheel folding obstacle avoidance program, improving the safety and efficiency of the entire obstacle avoidance process.

[0055] The core inventive point of this solution lies in combining the kinematic characteristics of an electric wheelchair with obstacle distance information to dynamically calculate the safe steering distance. This method not only improves the accuracy of judgment but also adapts to the obstacle avoidance requirements under different speeds and steering capabilities, demonstrating good self - adaptability. In addition, by introducing a preset safety threshold, this solution also provides an adjustable safety margin for the judgment process, further enhancing the reliability and flexibility of the system.

[0056] In the specific implementation process, various methods can be used to obtain the current speed, safe steering angular velocity, and distance to the obstacle of the electric wheelchair. For example, the current speed can be obtained through the encoder or speed sensor of the wheelchair drive motor; the safe steering angular velocity can be preset in advance or calculated in real - time according to the structural parameters and current load status of the wheelchair; and the distance to the obstacle can be measured by various sensors such as ultrasonic sensors, lidar, or cameras.

[0057] When calculating the minimum safe distance required for steering, the acceleration characteristics and turning radius of the electric wheelchair can be considered. Specifically, the following formula can be used: Minimum safe distance = (Current speed² / (2 * Maximum deceleration)) + (Current speed * Reaction time)+ Safety margin; Among them, the maximum deceleration is the maximum value at which the wheelchair can decelerate safely, the reaction time includes the system processing time and the time for performing the steering operation, and the safety margin is the additional distance reserved to cope with unexpected situations.

[0058] Furthermore, the safe steering angular velocity can be dynamically adjusted according to the structural characteristics and current speed of the wheelchair. For example, when traveling at high speed, the safe steering angular velocity should be correspondingly reduced to ensure stability during the steering process. The following relationship can be used: Safe steering angular velocity = Base steering angular velocity * (1 - Current speed / Maximum speed); Among them, the base steering angular velocity is the maximum safe steering angular velocity of the wheelchair in a stationary state.

[0059] When judging whether it is possible to avoid obstacles by steering, this technical solution not only considers static distance factors but also takes into account the dynamic characteristics of the electric wheelchair. This comprehensive judgment method can more accurately evaluate the feasibility of steering obstacle avoidance, thus improving the accuracy and safety of obstacle avoidance decisions.

[0060] For example, when the electric wheelchair is traveling at a speed of 3 m / s, the safe steering angular velocity is 0.5 rad / s, and the distance to the obstacle ahead is 5 meters, the system will first calculate the minimum safe distance required for steering. Assuming the maximum deceleration is 2 m / s², the reaction time is 0.5 s, and the safety margin is 1 meter, then: The minimum safe distance = (3² / (2 * 2)) + (3 * 0.5) + 1 = 4.25 meters; Since the actual distance of 5 meters is greater than the calculated minimum safe distance of 4.25 meters, the system will determine that obstacle avoidance can be achieved by steering. Conversely, if the actual distance is less than 4.25 meters, the system will determine that obstacle avoidance cannot be achieved by steering and trigger the support wheel folding obstacle avoidance program.

[0061] This judgment method based on dynamic calculation has obvious advantages compared with the fixed threshold judgment. It can adapt to the obstacle avoidance requirements under different speeds and environments, improving the adaptability and safety of the system. At the same time, by introducing a safety margin, this method also provides additional safety guarantees for the judgment process, further reducing the risk of misjudgment.

[0062] Through this precise judgment mechanism, the electric wheelchair can make more intelligent and safe decisions when encountering obstacles. When it is determined that obstacle avoidance cannot be achieved by steering, the system can promptly start the support wheel folding obstacle avoidance program, thereby maximizing the passing ability of the wheelchair while ensuring safety. This not only improves the overall performance of the electric wheelchair but also provides a more comfortable and safe riding experience for the user.

[0063] In some of the above embodiments of the present application, steps for determining whether obstacle avoidance can be achieved by steering are proposed to determine whether the support wheel folding obstacle avoidance program needs to be triggered. However, making a judgment solely based on the minimum safe distance in this process may not be comprehensive and accurate enough. In some cases, even if the requirements of the minimum safe distance are met, steering obstacle avoidance may still cause the wheelchair to collide with the obstacle or generate large fluctuations, affecting the riding comfort and safety. Therefore, a more precise and comprehensive judgment method is needed to evaluate the feasibility of steering obstacle avoidance.

[0064] In response to this, the present application further proposes that the steps for determining that obstacle avoidance cannot be achieved by steering include: obtaining the traveling speed of the electric wheelchair and the position information of the obstacle, and based on the traveling speed and the obstacle position information, calculating the predicted contact point between the electric wheelchair and the obstacle after a preset steering amplitude; according to the predicted contact point, calculating the predicted fluctuation amplitude when the electric wheelchair contacts the obstacle, and when the predicted fluctuation amplitude is greater than the preset threshold, determining that obstacle avoidance cannot be achieved by steering.

[0065] This technical solution introduces the concepts of predicted contact point and predicted fluctuation amplitude, and comprehensively evaluates the feasibility of steering for obstacle avoidance through these two key parameters. Specifically, first, the driving speed of the electric wheelchair and the position information of the obstacle are obtained. These data are the basis for prediction calculations, considering the dynamic characteristics of the wheelchair and the spatial position of the obstacle. Then, based on this information, the predicted contact point is calculated, that is, the steering process is simulated and the position where contact with the obstacle may occur is predicted. This step takes into account the motion trajectory of the wheelchair and the spatial relationship with the obstacle. Next, the predicted fluctuation amplitude is calculated according to the predicted contact point, and the possible fluctuation degree when the wheelchair contacts the obstacle is analyzed. Finally, by comparing the predicted fluctuation amplitude with a preset threshold, a conclusion is drawn on whether obstacle avoidance can be achieved through steering.

[0066] This technical solution improves the accuracy and comprehensiveness of judgment by introducing more dynamic factors and predictive analysis. It not only considers whether the wheelchair can complete a turn in space, but also evaluates the impact of the possible fluctuations during the turning process on the riding comfort and safety. This method can better handle complex obstacle environments, improve the reliability of obstacle avoidance decisions, and thus enhance the intelligent level and use safety of electric wheelchairs.

[0067] In practical applications, there are various methods for calculating the predicted contact point. For example, a kinematic model of the wheelchair can be used, combined with the current speed, preset steering amplitude, and obstacle position, to predict the motion trajectory of the wheelchair through numerical simulation. Another method is to use machine learning algorithms to train a model based on a large amount of historical data to more accurately predict the contact point in different situations.

[0068] There are also various implementation methods for calculating the predicted fluctuation amplitude. One method is to establish a dynamic model based on the mass distribution and suspension system characteristics of the wheelchair to simulate the collision process. Another method is to establish an empirical model through experimental data to estimate the fluctuation amplitude according to parameters such as the position of the contact point and the wheelchair speed.

[0069] The driving speed and obstacle position information directly affect the calculation of the predicted contact point, and the predicted contact point is the basis for calculating the predicted fluctuation amplitude. The comparison between the predicted fluctuation amplitude and the preset threshold determines the final judgment result.

[0070] By introducing the predicted contact point and predicted fluctuation amplitude, not only the spatial distance factor is considered, but also the impact of dynamic collision on the stability of the wheelchair is considered. This comprehensive consideration makes the obstacle avoidance decision more accurate and can better adapt to various complex road conditions and obstacle situations. For example, when encountering a moving obstacle in a narrow passage, even if the minimum safety distance requirement is met, this solution can use the predicted fluctuation amplitude to judge whether the turn will cause inappropriate bumps, thus making a safer decision.

[0071] During the specific implementation process, the electric wheelchair first obtains the current driving speed through a sensor system, which may include wheel speed sensors, gyroscopes, etc. The obstacle position information can be obtained through lidar, ultrasonic sensors, cameras, etc. Suppose the current speed of the wheelchair is 5 km / h, and a stationary obstacle is detected ahead.

[0072] Based on this information, the system uses a pre-set kinematic model to calculate the predicted contact point. For example, assuming the pre-set steering amplitude is 30 degrees, through simulation calculation, it is obtained that contact with the obstacle may occur at 2.5 meters. Then, the system uses a dynamic model, considering the wheelchair mass (such as 100 kg), the characteristics of the suspension system, and the contact point position, to calculate the predicted fluctuation amplitude of 0.15g (g is the acceleration due to gravity).

[0073] Assume that the pre-set fluctuation amplitude threshold is 0.1g. Since the predicted fluctuation amplitude of 0.15g is greater than the threshold, the system determines that it is impossible to avoid the obstacle by steering. At this time, the electric wheelchair will trigger the support wheel folding obstacle avoidance program instead of performing a steering operation that may cause a large bump.

[0074] In this way, the technical solution of the present application can more accurately evaluate the feasibility of avoiding obstacles by steering, effectively reducing unnecessary bumps and potential collision risks, and improving the riding comfort and safety. At the same time, this method also improves the adaptability of the electric wheelchair in complex environments, enabling it to more intelligently handle various obstacle situations.

[0075] In some of the above embodiments of the present application, it is proposed that when it is determined that it is impossible to avoid the obstacle by steering, a support wheel folding obstacle avoidance program is triggered based on the obstacle distance, wheelchair speed, and environmental constraint conditions to achieve obstacle avoidance. However, in this process, how to determine the folding order of multiple support wheels and how to control the number of support wheels that fold simultaneously to achieve efficient obstacle avoidance while ensuring the stability of the wheelchair is still a problem to be solved.

[0076] In response to this, the present application further proposes that when it is determined that it is impossible to avoid the obstacle by steering, the steps of triggering the support wheel folding obstacle avoidance program based on the obstacle distance, wheelchair speed, and environmental constraint conditions include: calculating the relative distance and predicted contact time between multiple support wheels and one or more obstacles based on the obstacle distance, wheelchair speed, and environmental constraint conditions, and determining the obstacle avoidance priority of each support wheel and the obstacle based on the relative distance and predicted contact time; according to the obstacle avoidance priority, triggering the folding obstacle avoidance program of each support wheel in chronological order, where the number of support wheels in the folded state at any moment is controlled not to exceed a preset threshold under the premise of ensuring the stability of the wheelchair.

[0077] The technical solution of this application determines the obstacle avoidance priority of each supporting wheel by calculating the relative distance and predicted contact time between multiple supporting wheels and obstacles. This method takes into account the obstacle distance, wheelchair speed, and environmental constraints, and can more accurately evaluate the obstacle avoidance requirements of each supporting wheel. According to the determined obstacle avoidance priority, the system sequentially triggers the folding obstacle avoidance programs of each supporting wheel in chronological order. This sequential folding control strategy can ensure that the supporting wheel that most needs to avoid obstacles folds first, improving the obstacle avoidance efficiency. At the same time, this application also introduces a control mechanism for the number of supporting wheels in the folded state at the same time. By setting a preset threshold, it is ensured that the number of supporting wheels in the folded state at any moment does not exceed this threshold. This measure not only achieves obstacle avoidance but also ensures the overall stability of the wheelchair.

[0078] The technical solution of this application realizes the intelligent and precise control of the folding obstacle avoidance of the supporting wheels by comprehensively considering multiple factors. It not only solves the problem of how to determine the folding order of multiple supporting wheels but also achieves a balance between ensuring the stability of the wheelchair and the obstacle avoidance efficiency by controlling the number of supporting wheels folded simultaneously. Compared with the simple method of folding all supporting wheels simultaneously or in a fixed order, this method has higher flexibility and adaptability and can better cope with complex obstacle environments.

[0079] In the technical solution of this application, calculating the relative distance and predicted contact time between multiple supporting wheels and one or more obstacles based on the obstacle distance, wheelchair speed, and environmental constraints can be achieved in various ways. For example, laser ranging, ultrasonic sensors, or visual sensing systems can be used to obtain the position information of the obstacles.

[0080] Based on the acquired data, prediction algorithms can be used to calculate the relative positions of each supporting wheel and the obstacles at future time points. The predicted contact time can be determined by calculating the time point when the relative distance between the supporting wheel and the obstacle reaches the minimum value.

[0081] Determining the obstacle avoidance priority can consider multiple factors, such as relative distance, predicted contact time, the current load of the supporting wheel, etc. Weights can be assigned to each factor, and the priority score of each supporting wheel can be calculated by weighted summation. The supporting wheel with the highest priority score will trigger the folding obstacle avoidance program first.

[0082] Controlling the number of supporting wheels in the folded state at any moment not to exceed the preset threshold is a key feature of this application. This preset threshold can be determined according to the structural characteristics, center of gravity distribution, and stability requirements of the wheelchair. For example, for an electric wheelchair with four-wheel support, the preset threshold may be set to 1. For an electric wheelchair with six-wheel support, the preset threshold may be set to 2, and the specific setting can be adjusted according to the specific wheelchair structure.

[0083] The above-mentioned four-wheel supported electric wheelchair includes two large wheels and two support wheels, and the six-wheel supported electric wheelchair includes two large wheels and four support wheels.

[0084] In some preferred embodiments, the solution of the present application is applicable to an electric wheelchair with six-wheel support. The six-wheel support is symmetrically arranged on the left and right sides in a way that there are three wheels on each side. In this case, the preset threshold is set to 1. This way has very high stability, that is, the folding of one support wheel hardly affects the overall running stability of the wheelchair.

[0085] In practical applications, the technical solution of the present application can work in coordination with other control systems of the wheelchair. For example, when triggering the folding obstacle avoidance program of the support wheel, the traveling speed and direction of the wheelchair can be appropriately adjusted to further improve the effect and safety of obstacle avoidance. In addition, the folding angle of the support wheel can be dynamically adjusted according to the height and shape of the obstacle to achieve more precise obstacle avoidance control.

[0086] The technical solution of the present application effectively solves the obstacle avoidance problem of electric wheelchairs in complex environments through intelligent folding control of the support wheels. By calculating the relative distances between multiple support wheels and the obstacle and predicting the contact time, the system can accurately evaluate the obstacle avoidance requirements of each support wheel. This method takes into account the obstacle distance, wheelchair speed, and environmental constraints, and has higher flexibility and adaptability compared to the methods of simply folding in a fixed order or folding all support wheels simultaneously.

[0087] According to the determined obstacle avoidance priority, the system sequentially triggers the folding obstacle avoidance programs of each support wheel in chronological order. This sequential folding control strategy ensures that the support wheel most in need of obstacle avoidance folds first, thus improving the obstacle avoidance efficiency. At the same time, by controlling the number of support wheels in the folded state at any moment not to exceed the preset threshold, the technical solution of the present application ensures the overall stability of the wheelchair while achieving efficient obstacle avoidance.

[0088] This intelligent folding control method can better handle complex obstacle environments. For example, in a narrow passage, the system can preferentially fold the support wheels that may collide with the wall while maintaining the support state of other support wheels to ensure the stability of the wheelchair. When facing multiple obstacles, the system can dynamically adjust the folding order and timing of each support wheel according to the predicted contact time to achieve a smoother and safer obstacle avoidance process.

[0089] In this way, the technical solution of the present application realizes the intelligent folding control of the supporting wheels, improving the obstacle avoidance efficiency and safety while ensuring the stability of the wheelchair. This method not only solves the problem of determining the folding order of multiple supporting wheels, but also achieves a good balance between ensuring the stability of the wheelchair and the obstacle avoidance efficiency by controlling the number of simultaneously folding supporting wheels.

[0090] In some of the above embodiments of the present application, an obstacle avoidance priority for each supporting wheel and the obstacle is determined based on the distance to the obstacle, the wheelchair speed, and environmental constraints to trigger the supporting wheel folding obstacle avoidance program. However, in this process, when facing dynamic obstacles, simply determining the obstacle avoidance priority based on the current state may not accurately predict the future collision risk, resulting in inaccuracy and untimely of the obstacle avoidance decision. In addition, only considering the relative position at a single time point may ignore the movement trend of the obstacle and cannot comprehensively evaluate the obstacle avoidance requirements.

[0091] In response to this, the present application further proposes that when the obstacle is a dynamic obstacle, obtain the current position and motion state of each supporting wheel, where the motion state includes the motion speed, as well as the moving trajectory and speed of the obstacle; based on the obtained current position and motion state of each supporting wheel, and the moving trajectory and speed of the obstacle, predict the position of each obstacle within a future time period, and calculate the relative distance sequence between each supporting wheel and each obstacle at multiple time points; according to the calculated relative distance sequence, determine the minimum relative distance between each supporting wheel and each obstacle and the corresponding predicted contact time; combined with the wheelchair speed and environmental constraints, based on the minimum relative distance and the predicted contact time, assign an obstacle avoidance priority score to each supporting wheel; according to the obstacle avoidance priority scores, sort all the supporting wheels to obtain the final obstacle avoidance priority sequence.

[0092] The technical solution of the present application transforms the static obstacle avoidance decision into a dynamic and forward-looking decision-making process by introducing the concepts of dynamic prediction and multi-time point analysis. By considering the movement trend of the obstacle and possible future situations, the accuracy and reliability of the obstacle avoidance decision are greatly improved. At the same time, by comprehensively considering multiple factors for priority scoring, a more intelligent and flexible obstacle avoidance strategy is realized, which can better adapt to complex and changeable environments. This method not only improves the safety of the electric wheelchair, but also enhances its adaptability and passability in dynamic environments.

[0093] Specifically, the technical solution of the present application includes the following key steps: First, obtain dynamic obstacle information. This step involves obtaining the current position and motion state of each supporting wheel, as well as the moving trajectory and speed of the obstacle.

[0094] Secondly, future position prediction is carried out. Based on the acquired data, the present application uses a prediction algorithm to estimate the positions of each obstacle within a future time period. This can be achieved through various methods, such as using a Kalman filter or a particle filter to predict the movement trajectories of obstacles. The prediction time range can be dynamically adjusted according to the speed of the wheelchair and the environmental complexity, and can usually be set to range from 0.5 seconds to 3 seconds.

[0095] Next, the relative distances at multiple time points are calculated. This step involves calculating the sequence of relative distances between each support wheel and each obstacle at multiple predicted time points. This can be achieved by calculating the relative positions of the support wheel and the obstacle at fixed time intervals (e.g., 0.1 seconds) within the predicted time range. The expected movement trajectory of the wheelchair needs to be considered during the calculation, and it can be based on a simple linear extrapolation of the current speed and direction, or a more complex wheelchair dynamics model can be used to predict the future position of the wheelchair.

[0096] Then, the minimum relative distance and the predicted contact time are determined. By analyzing the calculated sequence of relative distances, the present application determines the minimum relative distance between each support wheel and each obstacle and the corresponding predicted contact time. This step can be achieved by traversing the distance sequence to find the minimum value and its corresponding time point. If the minimum distance is less than a preset safety threshold (e.g., 10 cm), a potential collision risk is considered to exist.

[0097] Based on the above information, the present application conducts an obstacle avoidance priority scoring. This step combines the wheelchair speed and environmental constraint conditions, and assigns an obstacle avoidance priority score to each support wheel based on the minimum relative distance and the predicted contact time. The scoring function can be designed as: Score = w1 * (1 / min_distance) + w2 * (1 / time_to_contact) + w3 *wheelchair_speed + w4 * environmental_factor; Where w1, w2, w3, and w4 are weight coefficients that can be adjusted according to the actual situation. min_distance is the minimum relative distance, time_to_contact is the predicted contact time, wheelchair_speed is the current speed of the wheelchair, and environmental_factor is the environmental constraint factor (for example, the weight of this factor can be increased in a narrow passage).

[0098] Finally, priority sorting is carried out. According to the calculated obstacle avoidance priority scores, all support wheels are sorted to obtain the final obstacle avoidance priority sequence. The sorting can be implemented using efficient algorithms such as quicksort or heapsort to ensure fast response in a real-time system.

[0099] Specifically, after obtaining the list of support wheel obstacle avoidance priority sorting obtained based on dynamic prediction and multi-time point analysis, it is necessary to further determine the support wheels that actually need to perform the folding operation. This decision-making process is not simply to fold all in the order of priority, but further judgment is required. Specifically, it can be judged in combination with the driving direction of the electric wheelchair and the relative direction of the support wheel and the obstacle.

[0100] When the control system checks the relative positions between each support wheel and the obstacle in turn according to the order of obstacle avoidance priority from high to low, and combines the traveling direction of the electric wheelchair to judge whether the support wheel needs to be folded or not. After this screening process, a clear list of support wheels that "need to be folded" is finally obtained, and the support wheels in this list will perform the folding operation in turn according to the priority order.

[0101] During the folding operation, in order to ensure the stability of the electric wheelchair, it is necessary to strictly control the number of support wheels in the folded state at the same time. In this embodiment, it is assumed that the preset threshold for the number of support wheels folded simultaneously is 1, that is, at most only one support wheel is allowed to fold at any time. Therefore, when controlling the folding of the support wheel, it is necessary to handle possible folding conflict situations.

[0102] When the control system performs the folding operation in turn according to the list of support wheels that "need to be folded", it will continuously monitor whether there are other support wheels in the folded state currently. If there are no other support wheels in the folded state currently, it will directly control the support wheel that needs to be folded currently to start folding. However, if there are already other support wheels in the folded state currently (that is, it does not meet the preset threshold 1), it is necessary to perform conflict handling according to the predefined strategy.

[0103] For this situation, this embodiment provides a variety of optional handling strategies. Which strategy to adopt specifically depends on the specific design of the electric wheelchair, the application scenario, and the requirements for safety and efficiency. One optional strategy is the "wait" strategy. When adopting this strategy, the system will pause the folding operation of the support wheel that needs to be folded currently and let it enter the waiting state. The system continuously monitors the state of the previous support wheel. Once the previous support wheel completes folding and returns to the original support state, it will immediately start the folding operation of the waiting support wheel.

[0104] Another optional strategy is the "skip" strategy. When adopting this strategy, the system will directly skip the support wheel that needs to be folded currently and instead process the support wheel with a lower priority in the "need to be folded" list. This strategy is applicable to the situation where the obstacle is small, or skipping the folding of the current support wheel will not cause a significant increase in the collision risk.

[0105] In addition to the "wait" and "skip" strategies, the "emergency braking" strategy can also be considered. If skipping the folding of the current support wheel will result in a collision with an obstacle and the waiting time is too long, which will affect the overall obstacle avoidance efficiency, the system can trigger the emergency braking of the electric wheelchair, causing the wheelchair to stop moving forward immediately and waiting for manual intervention or further instructions.

[0106] Furthermore, the "adjust driving" strategy can also be adopted. That is, when a folding conflict is encountered, the system can slightly adjust the driving direction or speed of the electric wheelchair, and then re-perform steps such as obstacle detection, distance calculation, and priority sorting to generate a new obstacle avoidance strategy and a list of support wheels to be folded. This strategy avoids folding conflicts by dynamically adjusting the motion state of the wheelchair.

[0107] Another more refined strategy is "delay folding and insert into queue". Different from direct waiting, this strategy will add the folding instruction of the current support wheel that cannot be folded immediately due to conflict to a waiting queue. After the previous support wheel is folded and restored, the system will check the waiting queue and perform the folding operation of the next support wheel in the order of the queue (usually still according to the priority).

[0108] In specific implementation, one or a combination of several of the above strategies can be selected according to the actual situation. For example, the "wait" strategy can be tried first. If the waiting time exceeds the preset threshold, then switch to the "adjust driving" strategy; or in some specific scenarios, directly adopt the "skip" strategy. By flexibly applying these strategies, efficient and safe obstacle avoidance operations can be achieved while ensuring the stability of the electric wheelchair.

[0109] Through the synergistic effect of the above steps, the technical solution of this application effectively solves the problems of the accuracy and timeliness of obstacle avoidance decision-making in the face of dynamic obstacles. By predicting future positions and calculating the relative distances at multiple time points, this solution can more comprehensively evaluate potential collision risks and avoid the limitations of making decisions based only on the current state. Combining the wheelchair speed and environmental constraints for priority scoring further improves the adaptability and rationality of obstacle avoidance decision-making. The final priority sorting ensures that the support wheels that most urgently need to be folded can perform obstacle avoidance operations first, thereby improving the overall obstacle avoidance efficiency and safety.

[0110] In some of the above embodiments of the present application, a method is proposed to determine the folding direction and folding angle of the support wheel according to the obstacle position, the wheelchair driving direction, and the obstacle height to achieve obstacle avoidance control. However, in this process, due to the lack of accurate perception of the three-dimensional contour of the obstacle and real-time monitoring of the wheelchair posture, it is difficult to accurately calculate the contact position between the support wheel and the obstacle and the required minimum folding angle. In addition, when the obstacle shape is complex or the wheelchair speed is relatively high, relying solely on a simple folding strategy may not ensure safe passage, and it is necessary to combine wheelchair speed adjustment and steering optimization to improve the obstacle avoidance effect.

[0111] In response, the present application further proposes that the steps of determining the folding direction and folding angle of the support wheel according to the obstacle position, the wheelchair driving direction, and the obstacle height include: obtaining the three-dimensional contour information of the obstacle and the current posture parameters of the electric wheelchair, and constructing a spatial model of the obstacle based on the three-dimensional contour information, where the three-dimensional contour information of the obstacle includes the obstacle height; according to the current posture parameters of the electric wheelchair and the constructed spatial model of the obstacle, calculating the possible contact positions and contact angles between the support wheel and the obstacle, and combining the mechanical structure constraints of the support wheel, calculating the minimum folding angle required to avoid collision; when the minimum folding angle is greater than the maximum foldable angle of the support wheel, setting the folding angle to the maximum foldable angle and adjusting the wheelchair driving speed to ensure safe passage; when the minimum folding angle is less than or equal to the maximum foldable angle of the support wheel, setting the folding angle to the minimum folding angle; determining the folding direction according to the wheelchair driving direction and the relative position of the obstacle to achieve obstacle avoidance control.

[0112] This technical solution realizes accurate perception of the obstacle shape and position by obtaining the three-dimensional contour information of the obstacle and the current posture parameters of the electric wheelchair, and constructing a spatial model of the obstacle. This provides an accurate data basis for subsequent folding angle calculation. According to the posture parameters of the electric wheelchair and the spatial model of the obstacle, the possible contact positions and angles between the support wheel and the obstacle are calculated, and combined with the mechanical structure constraints of the support wheel, the minimum folding angle required to avoid collision is calculated. This method takes into account the real-time state of the wheelchair and the specific shape of the obstacle, and can more accurately determine the required folding angle. The solution also considers the maximum foldable angle limit of the support wheel. When the calculated minimum folding angle exceeds the maximum foldable angle of the support wheel, a combined adjustment strategy of folding angle and wheelchair speed is adopted, which not only ensures that the folding of the support wheel is within the feasible range, but also ensures safe passage through speed adjustment. Finally, the folding direction is determined according to the wheelchair driving direction and the relative position of the obstacle, realizing all-round obstacle avoidance control. This method not only considers the folding angle, but also the folding direction, making obstacle avoidance more flexible and efficient.

[0113] When constructing the obstacle space model, point cloud data processing technology can be adopted. First, the acquired three-dimensional point cloud data is filtered and denoised, and then the clustering algorithm is used to segment the point cloud into different obstacle objects. For each obstacle object, methods such as the convex hull algorithm or B-spline surface fitting can be used to construct its three-dimensional surface model.

[0114] When calculating the possible contact positions and contact angles between the support wheels and the obstacles, the present application adopts a dynamic collision detection algorithm. Specifically, based on the current motion state of the electric wheelchair (such as speed, direction, including current attitude parameters) and the space model of the obstacles, the motion trajectories of the support wheels in the future for a period of time are predicted. Then, by calculating the intersection points of the support wheel trajectories and the obstacle models, the possible contact positions and angles are determined.

[0115] When calculating the minimum folding angle required to avoid collisions, the present application takes into account the mechanical structure constraints of the support wheels. For example, the support wheels may have a maximum foldable angle limit, or folding within certain angle ranges may affect the stability of the wheelchair. By establishing a kinematic model of the support wheels, the positions and postures of the support wheels at different folding angles can be accurately calculated. Combining this model with the obstacle space model, the minimum folding angle that can avoid collisions can be found through an optimization algorithm (such as the gradient descent method).

[0116] When the calculated minimum folding angle is greater than the maximum foldable angle of the support wheels, the folding angle is set to the maximum foldable angle, and then the wheelchair driving speed is adjusted to ensure safe passage. Specifically, a relationship model of speed - folding angle - safety distance can be established, and through this model, the maximum driving speed that can safely pass the obstacle at the current folding angle can be calculated. This method not only ensures that the folding of the support wheels is within the feasible range but also ensures safe passage through speed adjustment, significantly improving the flexibility and adaptability of obstacle avoidance.

[0117] When determining the folding direction, the present application takes into account the comprehensive influence of the wheelchair driving direction and the relative position of the obstacles. By establishing a coordinate system with the wheelchair driving direction as the main axis, the relative position of the obstacles in this coordinate system is calculated. Then, based on the position distribution of the obstacles, the folding direction that can avoid the obstacles to the greatest extent is selected. This method can achieve all-round obstacle avoidance control and adapt to various complex obstacle distribution situations.

[0118] Through the above technical solutions, this application achieves precise perception of obstacles and real-time monitoring of the wheelchair attitude, solving the problems existing in the previous solutions. By constructing an accurate obstacle spatial model and a support wheel kinematic model, this application can accurately calculate the contact position between the support wheel and the obstacle and the required minimum folding angle. At the same time, by introducing speed adjustment and direction optimization strategies, this application can effectively handle obstacles with complex shapes and high-speed driving conditions, significantly improving the obstacle avoidance ability and adaptability of the electric wheelchair.

[0119] As a preferred implementation manner, this application can adopt the following specific steps to achieve intelligent folding obstacle avoidance of the support wheel: First, use the lidar installed at the front of the electric wheelchair to scan the front environment and obtain the point cloud data of the obstacle. At the same time, through the IMU sensor installed on the wheelchair chassis, the attitude parameters of the wheelchair are obtained in real time, including tilt angle, acceleration, and angular velocity.

[0120] Next, perform plane segmentation and clustering on the point cloud data to identify the contour of the obstacle. Then, use the B-spline surface fitting algorithm to construct a three-dimensional spatial model of the obstacle. This model takes the current position of the wheelchair as the origin and establishes a three-dimensional coordinate system.

[0121] Based on the current speed (assumed to be 5 km / h) and direction of the wheelchair, predict the movement trajectory of the support wheel within the next 3 seconds. By calculating the intersection points of this trajectory and the obstacle model, determine the possible contact positions and angles.

[0122] Assume that the maximum foldable angle of the support wheel is 45 degrees, and calculate the minimum foldable angle required to avoid collision through an optimization algorithm. If the calculated result is 30 degrees, which is less than the maximum foldable angle, directly set the foldable angle to 30 degrees.

[0123] According to the driving direction of the wheelchair and the relative position of the obstacle, calculate the optimal folding direction. For example, if the obstacle is located at a 15-degree angle position in the front right of the wheelchair, the folding direction can be set to 20 degrees to the left to avoid the obstacle to the greatest extent.

[0124] Finally, control the support wheel motor to perform the folding operation to ensure that the set 30-degree folding angle and the 20-degree left deviation folding direction are accurately achieved.

[0125] In this way, this application can accurately control the folding of the support wheel under different obstacle conditions, realizing safe and efficient obstacle avoidance. This method not only improves the passing performance of the electric wheelchair but also enhances the comfort and sense of security of the user.

[0126] In some of the above embodiments of the present application, steps are proposed to set the folding angle to the maximum foldable angle and adjust the wheelchair traveling speed to ensure safe passage when the minimum folding angle is greater than the maximum foldable angle of the support wheel, so as to ensure the safe passage of the wheelchair when the support wheel cannot completely avoid obstacles. However, in this process, simply setting the maximum foldable angle and adjusting the speed may not fully ensure the stability and passing efficiency of the wheelchair, especially in a complex obstacle environment. In addition, simple speed adjustment may lead to a reduction in the traveling efficiency of the wheelchair, affecting the user experience. Therefore, a more intelligent and optimized method is needed to handle this situation.

[0127] In response to this, the present application further proposes that when the fluctuation amplitude calculated based on the obstacle space model and the maximum foldable angle of the support wheel exceeds a preset threshold, by calculating and analyzing multiple alternative steering angles, select the steering angle that can generate the minimum fluctuation amplitude at the new contact position between the support wheel and the obstacle as the optimized steering angle; control the wheelchair to perform the steering operation of the optimized steering angle, set the folding angle of the support wheel to the maximum foldable angle, and calculate the maximum traveling speed required for safe passage based on the optimized fluctuation amplitude; adjust the wheelchair traveling speed to a value not exceeding the maximum traveling speed to ensure safe passage through the obstacle.

[0128] The technical solution of the present application introduces multiple key technical features to solve the above problems. First, by constructing a spatial model of the obstacle, it provides an accurate data basis for subsequent calculations and decisions. Based on this model, the fluctuation amplitude is calculated to evaluate the stability when passing through the obstacle. When the fluctuation amplitude exceeds the preset threshold, by analyzing multiple alternative steering angles, the optimal solution is found, improving the intelligence and adaptability of the decision-making.

[0129] Specifically, the present application selects the steering angle that can generate the minimum fluctuation amplitude at the new contact position between the support wheel and the obstacle to minimize the instability when passing through the obstacle. At the same time, the folding angle of the support wheel is set to the maximum foldable angle, and the maximum traveling speed required for safe passage is calculated and adjusted based on the optimized fluctuation amplitude, achieving the coordinated optimization of the folding angle and speed.

[0130] These technical features work together to jointly solve the problem of how to ensure the safe and stable passage of the wheelchair when the support wheel cannot completely avoid obstacles. The present application not only considers the safety of the wheelchair but also takes into account the passing efficiency. Through an intelligent decision-making process, it improves the passing efficiency of the wheelchair as much as possible while ensuring safety.

[0131] The core inventive point of this application lies in the introduction of an intelligent analysis and selection mechanism for multiple alternative steering angles, as well as a collaborative optimization strategy for folding angles and speeds. Compared with simply setting the maximum foldable angle and adjusting the speed, this method can better adapt to complex obstacle environments, improving the success rate and stability of the wheelchair in passing obstacles.

[0132] Furthermore, the technical solution of this application can be implemented in the following ways: Firstly, calculate the fluctuation amplitude based on the obstacle space model and the maximum foldable angle of the support wheel. This can be achieved by establishing a three-dimensional obstacle model, combining the movement trajectory of the wheelchair and the folding angle of the support wheel, and using numerical simulation methods to calculate the fluctuation situation when the wheelchair passes through the obstacle.

[0133] Secondly, when the calculated fluctuation amplitude exceeds the preset threshold, the system generates multiple alternative steering angles. These angles can be determined based on the current position, speed of the wheelchair, and the relative position of the obstacle. For example, alternative steering angles can be generated at intervals of 5° within the range of -30° to 30°.

[0134] Next, analyze each alternative steering angle. This step can use a dynamic model to simulate the movement trajectory of the wheelchair at each steering angle, calculate the new contact position of the support wheel with the obstacle, and evaluate the corresponding fluctuation amplitude.

[0135] Then, select the steering angle that can produce the minimum fluctuation amplitude as the optimized steering angle. This selection process can be achieved by comparing the fluctuation amplitude values at different angles and choosing the angle with the minimum value.

[0136] When controlling the wheelchair to perform the steering operation of the optimized steering angle, a progressive steering control strategy can be adopted to ensure a smooth steering process and avoid causing additional instability.

[0137] At the same time, set the folding angle of the support wheel to the maximum foldable angle. This can be achieved through an electric control mechanism to ensure that the support wheel avoids obstacles to the greatest extent.

[0138] Calculate the maximum driving speed required for safe passage. This calculation can consider factors such as the mass of the wheelchair, the contact area between the wheelchair and the obstacle, and the expected impact force, and use a dynamic model for simulation calculation.

[0139] Finally, adjust the wheelchair driving speed to a value not exceeding the calculated maximum driving speed. This can be achieved through a motor control system to ensure that the wheelchair operates within a safe speed range.

[0140] The technical solution of this application can maximize the efficiency of the wheelchair passing through obstacles while ensuring safety. For example, when encountering a protruding obstacle in a narrow passage, the traditional method may simply decelerate and attempt to pass directly, while the method of this application will calculate an optimal small-angle turn and precisely control the folding angle of the support wheels and the traveling speed, so as to pass through the obstacle more smoothly and quickly.

[0141] As a preferred implementation manner, the following specific steps can be implemented in the control system of the electric wheelchair in this application: Use sensors such as lidar or depth cameras to scan the surrounding environment and construct an accurate three-dimensional obstacle space model.

[0142] Based on the obtained obstacle space model and the maximum foldable angle of the support wheels (such as 45°), use the finite element analysis method to calculate the fluctuation amplitude when the wheelchair passes through the obstacle. Assume that the preset fluctuation amplitude threshold is 5 mm.

[0143] When the calculated fluctuation amplitude exceeds 5 mm, the system generates 21 alternative steering angles at intervals of 2° within the range of -20° to 20°.

[0144] For each alternative steering angle, use dynamic simulation software to simulate the process of the wheelchair passing through the obstacle, calculate the new contact position of the support wheel and the obstacle and the corresponding fluctuation amplitude. Select the angle with the smallest fluctuation amplitude as the optimized steering angle.

[0145] Use the servo motor control system to perform the steering operation of the optimized steering angle at an angular velocity of 0.5° / s to ensure a smooth steering process.

[0146] Set the folding angle of the support wheels to the maximum foldable angle of 45°, use a high-precision stepper motor to control the folding process, and the folding speed is 10° / s.

[0147] Based on the optimized fluctuation amplitude, use a neural network model to predict the maximum traveling speed required to pass safely. For example, if the predicted maximum traveling speed is 2 km / h, then limit the wheelchair speed to below 1.8 km / h, leaving a 10% safety margin.

[0148] During the entire process of passing through the obstacle, use an acceleration sensor and a gyroscope to continuously monitor the stability of the wheelchair. If abnormal fluctuations are detected, the system will immediately adjust the speed or steering angle.

[0149] In this way, the technical solution of the present application can provide a more intelligent and safe obstacle passing strategy in complex environments, significantly improving the adaptability and user experience of electric wheelchairs. For example, when encountering a protruding threshold at a narrow corridor corner, this solution can accurately calculate a small-angle turn (such as 7.5°), while folding the support wheels to the maximum angle of 45° and controlling the speed at 1.5 km / h, so as to pass the obstacle smoothly and quickly. In contrast, traditional methods may require a complete stop for a large-angle turn or manual adjustment of the support wheels.

[0150] The technical solution of the present application effectively solves the problem of how to ensure the safe and stable passage of the wheelchair when the support wheels cannot completely avoid obstacles by introducing an intelligent analysis and selection mechanism for multiple alternative steering angles, as well as a collaborative optimization strategy for folding angles and speeds. Compared with simply setting the maximum foldable angle and adjusting the speed, this solution can better adapt to complex obstacle environments, improving the success rate and stability of the wheelchair passing obstacles. At the same time, by selecting and optimizing the steering angle and accurately calculating the maximum traveling speed, the traveling efficiency of the wheelchair is maximized while ensuring safety, significantly improving the user experience. This method not only solves the stability and efficiency problems that may be brought about by simple folding and deceleration in the prior art, but also provides a new technical idea for the intelligent control system of electric wheelchairs, showing significant technological progress.

[0151] In some of the above embodiments of the present application, it is proposed to control the number of support wheels in the folded state at any moment not to exceed a preset threshold to ensure the stability of the electric wheelchair. However, in this process, a fixed preset threshold may not be able to adapt to complex and changeable obstacle environments, making it difficult to optimize the balance between wheelchair stability and passing efficiency. In addition, the surface characteristics of different obstacles may affect the contact area of the support wheels, thereby affecting the stability of the wheelchair, but this factor has not been fully considered in the existing solutions.

[0152] In response to this, the present application further proposes to obtain the surface feature information of the obstacle, and when it is detected that the obstacle has a plane that meets the preset conditions, based on the calculated contact area between the support wheel and the plane, dynamically adjust the preset threshold of the number of support wheels allowed to be folded simultaneously; control the support wheels in the folded state to contact the plane of the obstacle, and real-time monitor the stability parameters of the wheelchair; according to the real-time changes of the stability parameters, dynamically adjust the folding angles of each support wheel to maximize the passing efficiency while ensuring the stability of the wheelchair.

[0153] The technical solution of this application first obtains the surface feature information of the obstacle, especially detects the plane that meets the preset conditions, providing a basis for dynamically adjusting the support wheel folding strategy. Based on the calculated contact area between the support wheel and the obstacle plane, the system can more accurately evaluate the stability of the wheelchair, thereby dynamically adjusting the preset threshold of the number of support wheels allowed to be folded simultaneously. This dynamic adjustment mechanism can flexibly change according to the actual situation, ensuring both the stability of the wheelchair and improving the efficiency of passing obstacles.

[0154] Secondly, by controlling the contact between the support wheels in the folded state and the obstacle plane, the system can make full use of the surface features of the obstacle to increase the stability of the wheelchair. Real-time monitoring of the stability parameters of the wheelchair provides real-time and accurate data support for subsequent dynamic adjustment.

[0155] Finally, according to the changes in the stability parameters monitored in real time, the system can dynamically adjust the folding angles of the support wheels. This real-time and refined adjustment strategy can not only ensure the stability of the wheelchair during the process of passing obstacles, but also maximize the passing efficiency within the range allowed by the stability.

[0156] The technical solution of this application effectively solves the problem that the fixed preset threshold is difficult to adapt to complex environments by introducing innovative points such as obstacle surface feature analysis, dynamic threshold adjustment, real-time stability monitoring, and refined folding angle control. It can intelligently balance the stability and passing efficiency of the wheelchair in different obstacle environments, significantly improving the obstacle avoidance ability and adaptability of the electric wheelchair. This method not only improves the safety of wheelchair use, but also enhances its mobility in complex environments, providing a more intelligent, safe and efficient travel experience for users.

[0157] As a preferred implementation manner, the method for controlling the folding of the support wheels of the electric wheelchair in this application can be specifically implemented as follows: First, the electric wheelchair is equipped with a high-precision 3D laser scanner and a depth camera for obtaining three-dimensional information of the surrounding environment. When an obstacle is detected within 5 meters in front, the system starts a detailed surface feature analysis program.

[0158] For example, the system detects a rectangular obstacle with a length of 1.5 meters, a width of 0.8 meters, and a height of 0.3 meters. By analyzing the obtained point cloud data, the system identifies a plane with an area of 1.2 square meters and an inclination angle not exceeding 5 degrees on the top of the obstacle. This plane meets the preset conditions of "area greater than 1 square meter and inclination angle less than 10 degrees" and is recognized by the system as a possible contact surface for the support wheels.

[0159] Next, based on the geometric parameters of the support wheels (assuming a diameter of 15 cm and a width of 5 cm) and the characteristics of the obstacle plane, the system calculates that the theoretical maximum contact area is approximately 75 square centimeters. Considering that the actual contact may not be completely fitting, the system estimates the effective contact area as 80% of the theoretical maximum, which is 60 square centimeters.

[0160] Based on this contact area, the system dynamically adjusts the preset threshold for the number of support wheels allowed to fold simultaneously. Normally, this threshold is set to 1, but due to the detection of a large and stable contact surface, the system temporarily increases the threshold to 2.

[0161] When the wheelchair approaches the obstacle, the system controls 2 support wheels (assuming the front left, front right, and rear right) to start folding. During the folding process, the system continuously monitors the stability parameters of the wheelchair, including the tilt angle, angular velocity, and pressure distribution at each support point.

[0162] For example, when the front left support wheel starts to contact the obstacle plane, the system detects that the wheelchair tilts 1.5 degrees to the left and the pressure on the right support wheels increases by 5%. Based on this data, the system immediately adjusts the folding angles of the front right and rear right support wheels, reducing the folding angle of the front right support wheel from the original planned 45 degrees to 40 degrees, while increasing the folding angle of the rear right support wheel from 30 degrees to 35 degrees to balance the center of gravity of the wheelchair.

[0163] During the process of the wheelchair passing over the obstacle, the system continues to make similar real-time adjustments. When it detects that the center of gravity of the wheelchair starts to move backward, the system gradually increases the supporting force of the rear support wheels while reducing the folding angle of the front support wheels to ensure the stability of the wheelchair throughout the process.

[0164] Finally, the wheelchair successfully passes over the obstacle, keeping the stability parameters within the safe range throughout the process, with the maximum tilt angle not exceeding 2 degrees, and the passing time being approximately 20% shorter than that using a fixed folding strategy.

[0165] Through this dynamic adjustment and fine control, the technical solution of this application not only ensures the stability of the electric wheelchair in a complex obstacle environment but also significantly improves the passing efficiency, providing a safer and more comfortable riding experience for users.

[0166] In some of the above embodiments of the present application, when the fluctuation amplitude calculated based on the obstacle space model and the maximum foldable angle of the support wheel exceeds a preset threshold, the step of calculating and analyzing multiple alternative steering angles and selecting the steering angle that can generate the minimum fluctuation amplitude at the new contact position between the support wheel and the obstacle as the optimized steering angle is proposed to optimize the obstacle avoidance performance of the electric wheelchair. However, in this process, how to accurately select the optimal steering angle to minimize the fluctuation amplitude while considering the stability and safety of the electric wheelchair remains a challenge. In addition, how to calculate and select the optimized steering angle in real time in a dynamic environment, and how to balance the computational complexity and real-time performance are also problems that need to be solved.

[0167] In response to this, the present application further proposes to obtain n alternative steering angles θ 1 , θ 2 , ..., θ n , the maximum foldable angle α_max of the support wheel, and the obstacle space model O(x, y, z, t); within the time interval [t 0 , t 1 , calculate the integral function f(θ, t) = ∫[t 0 , t 1 [A(θ, t) * F(θ, t) + λ * S(θ, t)] dt for each alternative steering angle θ, where A(θ, t) is the contact area between the support wheel and the obstacle at time t, F(θ, t) is the contact force at time t, S(θ, t) is the stability index of the electric wheelchair at time t, and λ is the stability weight factor; under the constraint conditions of A(θ, t)>0, F(θ, t) ≤ F_max, and S(θ, t) ≥ S_min, select the steering angle θ that minimizes the integral function f(θ, t) as the optimized steering angle θ_opt.

[0168] The technical solution proposed by the present application selects the optimized steering angle by introducing an integral function that comprehensively considers multiple factors. This solution first obtains key parameters, including alternative steering angles, the maximum foldable angle of the support wheel, the obstacle space model, the electric wheelchair speed function, and mass, which comprehensively describe the dynamic characteristics of the electric wheelchair and the obstacle. Then, an integral function f(θ, t) = ∫[t 0 , t 1 [A(θ, t) * F(θ, t) + λ * S(θ, t)]dt is designed, which cleverly combines the contact area A(θ, t), the contact force F(θ, t), and the stability index S(θ, t), and evaluates the performance of the entire obstacle avoidance process through time integration.

[0169] This application introduces constraint conditions: A(θ, t) > 0 to ensure contact exists; F(θ, t) ≤ F_max to limit the maximum contact force; S(θ, t) ≥ S_min to guarantee minimum stability. These constraint conditions ensure the safety and feasibility of the obstacle avoidance process. Finally, the optimal steering angle θ_opt is selected by minimizing the integral function f(θ, t). This method not only considers the instantaneous state but also the dynamic changes throughout the obstacle avoidance process.

[0170] The specific implementation of this scheme can be further refined. First, the obstacle space model O(x, y, z, t) can be obtained and updated in real time through sensors such as lidar and depth cameras. This model not only includes the geometric shape of the obstacle but also its motion trajectory, enabling the prediction of the position change of the obstacle within a certain period in the future.

[0171] The calculation of the contact area A(θ, t) can be achieved by performing a spatial projection of the geometric models of the wheelchair and the obstacle at each time point. The contact force F(θ, t) can be estimated using a physical model based on the mass, speed, and contact area of the wheelchair. The stability index S(θ, t) can be evaluated by calculating the position of the wheelchair's center of gravity relative to the support polygon. Considering dynamic factors, parameters such as angular momentum can also be introduced.

[0172] The calculation of the integral function can adopt numerical integration methods such as the Simpson's method or the Runge - Kutta method. To improve the calculation efficiency, an adaptive step size can be used, with a smaller step size at key time points (such as the start and end of contact) and a larger step size at other time points.

[0173] The selection of the stability weight factor λ has an important impact on the final result. The value of λ can be adjusted according to different application scenarios and user preferences. For example, a smaller λ value can be selected on flat roads to prioritize the obstacle avoidance effect, while a larger λ value can be chosen on rough terrains to ensure the stability of the wheelchair.

[0174] Machine learning algorithms can be introduced to train a model through historical data to quickly estimate the range of the optimal steering angle, thereby reducing the search space and improving the calculation efficiency.

[0175] The technical scheme of this application minimizes the fluctuation amplitude while ensuring safety by comprehensively considering the contact area, contact force, and stability. The introduction of time integration and dynamic models enables this method to adapt to complex dynamic environments. By setting constraint conditions, the practicality and safety of the solution are ensured.

[0176] The innovation of this solution is reflected in the following aspects: First, dynamic modeling takes into account the dynamic characteristics of obstacles and electric wheelchairs, improving the accuracy of obstacle avoidance decisions. Second, multi-objective optimization considers the contact area, contact force, and stability simultaneously through an integral function, achieving a balance of multiple objectives. Third, the time integration method not only considers the instantaneous state but also the entire obstacle avoidance process, enhancing the robustness of the decision-making. Finally, through the stability weight factor λ, the importance of stability can be adjusted according to actual needs.

[0177] This method not only improves the accuracy and safety of obstacle avoidance but also has good adaptability and scalability, providing an efficient and reliable solution for the intelligent obstacle avoidance of electric wheelchairs.

[0178] As a preferred implementation manner, this application can be achieved through the following steps: First, obtain environmental information through the sensor system and construct an obstacle space model O(x, y, z, t). For example, use a 360-degree lidar to scan the surrounding environment with a scanning frequency of 10Hz, a resolution of 0.1 degrees, and a ranging range of 0.1m to 100m. Combine the data of a depth camera (resolution 1920x1080, frame rate 30fps), use the SLAM algorithm to construct a three-dimensional environmental map, and update the position and velocity information of dynamic obstacles in real time through a Kalman filter.

[0179] Second, based on the current speed and acceleration of the wheelchair, combined with the control instructions input by the user, predict the speed function v(t) within the next 5 seconds. For example, assume the current speed is 1.2m / s and the acceleration is 0.2m / s², then v(t) = 1.2 + 0.2t (t ∈ [0, 5]) can be predicted.

[0180] Then, generate n alternative steering angles. Usually, n can be selected as 36, that is, one alternative angle every 10 degrees. For each alternative steering angle θ, calculate the integral function f(θ, t) within the time interval [0, 5]. The specific calculation process is as follows: Divide the time interval [0, 5] into 50 equally spaced time points.

[0181] At each time point, calculate the predicted position of the wheelchair based on O(x, y, z, t).

[0182] Calculate the contact area A(θ, t) between the support wheel and the obstacle. For example, if the contact area is circular with a diameter of 5cm, then A(θ, t) = π * (0.025)² ≈ 0.002m².

[0183] Calculate the contact force F(θ, t). Assume the mass of the wheelchair is 100 kg, then F(θ, t) = 100 * 9.8 * v(t)² / (2 * 0.002) N.

[0184] Calculate the stability index S(θ, t). It can be defined as S(θ, t) = d / r, where d is the shortest distance from the center of gravity to the edge of the support polygon, and r is the radius of the circumcircle of the support polygon.

[0185] Select an appropriate value of λ, for example, λ = 0.5.

[0186] Use the Simpson's method for numerical integration to calculate the value of f(θ, t). Under the constraint conditions A(θ, t) > 0, F(θ, t) ≤ 1000 N (assuming the maximum allowable contact force is 1000 N), and S(θ, t) ≥ 0.8 (assuming the minimum stability threshold is 0.8), select θ that minimizes f(θ, t) as the optimized steering angle θ_opt.

[0187] Finally, transfer the calculated θ_opt to the control system of the wheelchair to perform the corresponding steering operation. At the same time, adjust the folding angle of the support wheel according to θ_opt to minimize the fluctuation amplitude to the greatest extent.

[0188] Through this method, the present application can achieve precise obstacle avoidance control in a complex dynamic environment, significantly improving the safety and comfort of the electric wheelchair. Compared with the traditional method, this solution can better handle moving obstacles, reduce the collision risk, and minimize the fluctuations during the ride while ensuring stability, thereby providing a safer and more comfortable riding experience for the user.

[0189] In the second aspect, referring to Figure 2 , the present application further proposes an electric wheelchair support wheel folding control device, which includes: A state acquisition module 210 for acquiring the driving state information and surrounding environment information of the electric wheelchair; An obstacle judgment module 220 for judging whether there is an obstacle according to the driving state information and surrounding environment information; A steering obstacle avoidance judgment module 230 for judging whether obstacle avoidance can be achieved by steering when there is an obstacle; A folding obstacle avoidance trigger module 240 for triggering the support wheel folding obstacle avoidance program based on the obstacle distance, wheelchair speed, and environmental constraint conditions when it is judged that obstacle avoidance cannot be achieved by steering; A folding parameter determination module 250 for determining the folding direction and folding angle of the support wheel according to the obstacle position, wheelchair driving direction, and obstacle height; The folding control module 260 is used to control the support wheels to fold in a determined folding direction and folding angle to achieve obstacle avoidance. The recovery control module 270 is used to control the support wheels to automatically return to the original support state after passing through an obstacle.

[0190] By obtaining the driving state information and surrounding environment information of the electric wheelchair, it is judged whether there is an obstacle. When it is impossible to avoid the obstacle by steering, the support wheel folding obstacle avoidance program is triggered, and the folding direction and folding angle of the support wheels are determined according to the obstacle position, the wheelchair driving direction and the obstacle height, so as to achieve intelligent obstacle avoidance. It has the advantages of improving the obstacle avoidance ability and safety of the electric wheelchair and enhancing the adaptability of the wheelchair in a complex environment.

[0191] In addition, in some preferred embodiments, a support wheel folding control device for an electric wheelchair proposed by the present application can execute any one of the steps in the above method.

[0192] The above are only the embodiments of the present application and are not intended to limit the protection scope of the present application. For those skilled in the art, the present application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for controlling the folding of support wheels of an electric wheelchair, characterized in that: The method includes: Obtaining the driving status information and surrounding environment information of the electric wheelchair; Determining whether there is an obstacle based on the driving state information and the surrounding environment information; When there is an obstacle, determine whether it can be avoided by turning; When it is determined that obstacle avoidance cannot be achieved by steering, the support wheel folding obstacle avoidance program is triggered based on the obstacle distance, wheelchair speed and environmental constraints; Determine the folding direction and folding angle of the support wheels according to the obstacle position, the wheelchair travel direction and the obstacle height; Control the support wheels to fold according to a determined folding direction and folding angle to achieve obstacle avoidance; After passing the obstacle, the control support wheel automatically returns to the original support state.

2. The electric wheelchair support wheel folding control method according to claim 1, characterized in that: When an obstacle exists, the step of determining whether the obstacle can be avoided by turning comprises: Get the current speed, safe steering angle velocity and distance to obstacles of the electric wheelchair; Calculate the minimum safe distance required to complete the turn based on the preset safety threshold, the current speed of the electric wheelchair, and the safe turning angular velocity; When the actual distance between the electric wheelchair and the obstacle is less than the minimum safety distance, it is determined that the obstacle cannot be avoided by turning.

3. The electric wheelchair support wheel folding control method according to claim 2, characterized in that: The step of determining that obstacle avoidance cannot be performed by turning comprises: Acquiring the driving speed of the electric wheelchair and the position information of the obstacle, and based on the driving speed and the position information of the obstacle, calculating the predicted contact point between the electric wheelchair and the obstacle after the electric wheelchair performs a preset turning amplitude; The predicted fluctuation amplitude when the electric wheelchair contacts the obstacle is calculated according to the predicted contact point, and when the predicted fluctuation amplitude is greater than a preset threshold, it is determined that the obstacle cannot be avoided by turning.

4. The electric wheelchair support wheel folding control method according to claim 1, characterized in that: When it is determined that the obstacle cannot be avoided by turning, the step of triggering the support wheel folding obstacle avoidance program based on the obstacle distance, the wheelchair speed and the environmental constraints includes: Calculating relative distances and predicted contact times between a plurality of support wheels and one or more obstacles based on obstacle distances, wheelchair speed, and environmental constraints, and determining obstacle avoidance priorities between each support wheel and the obstacle based on the relative distances and predicted contact times; According to the obstacle avoidance priority, the folding obstacle avoidance program of each support wheel is triggered in sequence in chronological order, wherein, under the premise of ensuring the stability of the wheelchair, the number of support wheels in the folded state at any time is controlled not to exceed a preset threshold.

5. The method for controlling the folding of the support wheels of an electric wheelchair according to claim 4, characterized in that: The step of calculating the relative distances and predicted contact times between the plurality of support wheels and one or more obstacles based on the obstacle distance, the wheelchair speed and the environmental constraints, and determining the obstacle avoidance priority between each support wheel and the obstacle based on the relative distances and the predicted contact times comprises: When the obstacle is a dynamic obstacle, obtain the current position and motion state of each support wheel. The motion state includes the motion speed, as well as the moving trajectory and speed of the obstacle. Based on the acquired current position and motion state of each support wheel, as well as the moving trajectory and speed of the obstacle, the position of each obstacle in the future time period is predicted, and the relative distance sequence between each support wheel and each obstacle at multiple time points is calculated; According to the calculated relative distance sequence, the minimum relative distance between each support wheel and each obstacle and the corresponding predicted contact time are determined; Combining the wheelchair speed and environmental constraints, each support wheel is assigned an obstacle avoidance priority score based on the minimum relative distance and predicted contact time; According to the obstacle avoidance priority score, all support wheels are sorted to obtain the final obstacle avoidance priority sequence.

6. The method for controlling the folding of the support wheels of an electric wheelchair according to claim 1, characterized in that: The step of determining the folding direction and folding angle of the support wheels according to the obstacle position, the wheelchair driving direction and the obstacle height comprises: Acquire three-dimensional contour information of the obstacle and current posture parameters of the electric wheelchair, and construct a spatial model of the obstacle based on the three-dimensional contour information, wherein the three-dimensional contour information of the obstacle includes the height of the obstacle; According to the current posture parameters of the electric wheelchair and the constructed obstacle space model, the possible contact position and contact angle between the support wheel and the obstacle are calculated, and the minimum folding angle required to avoid collision is calculated in combination with the mechanical structure constraints of the support wheel; When the minimum folding angle is greater than the maximum foldable angle of the support wheels, the folding angle is set to the maximum foldable angle, and the travel speed of the wheelchair is adjusted to ensure safe passage; When the minimum folding angle is less than or equal to the maximum foldable angle of the support wheel, the folding angle is set to the minimum folding angle; The folding direction is determined according to the travel direction of the wheelchair and the relative position of the obstacle to achieve obstacle avoidance control.

7. The method for controlling the folding of the support wheels of an electric wheelchair according to claim 6, characterized in that: When the minimum folding angle is greater than the maximum foldable angle of the support wheels, the step of setting the folding angle to the maximum foldable angle and adjusting the wheelchair speed to ensure safe passage comprises: When the fluctuation amplitude calculated based on the obstacle space model and the maximum foldable angle of the support wheel exceeds a preset threshold, a steering angle that can minimize the fluctuation amplitude of the new contact position between the support wheel and the obstacle is selected as the optimized steering angle by calculating and analyzing multiple alternative steering angles; Controlling the wheelchair to perform the steering operation of the optimized steering angle, setting the support wheel folding angle to the maximum foldable angle, and calculating the maximum driving speed required for safe passage based on the optimized fluctuation amplitude; Adjust the wheelchair's travel speed to a value that does not exceed the maximum travel speed to ensure safe passage through obstacles.

8. The method for controlling the folding of the support wheels of an electric wheelchair according to claim 4, characterized in that: The step of controlling the number of support wheels in a folded state at any time to not exceed a preset threshold comprises: Obtaining surface feature information of the obstacle, and when detecting that the obstacle has a plane that meets the preset conditions, dynamically adjusting a preset threshold value of the number of support wheels allowed to be folded simultaneously based on the calculated contact area between the support wheels and the plane; Control the contact between the support wheels in the folded state and the plane of the obstacle, and monitor the stability parameters of the wheelchair in real time; According to the real-time changes in stability parameters, the folding angle of each support wheel is dynamically adjusted to maximize the passing efficiency while ensuring the stability of the wheelchair.

9. The method for controlling the folding of the support wheels of an electric wheelchair according to claim 7, characterized in that: The step of selecting a steering angle that can cause a minimum fluctuation amplitude between the support wheel and the obstacle at a new contact position with the obstacle as the optimized steering angle by calculating and analyzing a plurality of candidate steering angles when the fluctuation amplitude calculated based on the obstacle space model and the maximum foldable angle of the support wheel exceeds a preset threshold comprises: Get n candidate steering angles θ1, θ2, ..., θ n , the maximum foldable angle of the support wheel α_max, obstacle space model O(x, y, z, t); In the time interval [t0, t1], for each alternative steering angle θ, the integral function f(θ, t) = ∫[t0, t1] [A(θ, t) * F(θ, t) + λ * S(θ, t)] dt is calculated, where A(θ, t) is the contact area between the support wheel and the obstacle at time t, F(θ, t) is the contact force at time t, S(θ, t) is the stability index of the electric wheelchair at time t, and λ is the stability weight factor; Under the constraints of A(θ, t) > 0, F(θ, t) ≤ F_max, and S(θ, t) ≥ S_min, the steering angle θ that minimizes the integral function f(θ, t) is selected as the optimized steering angle θ_opt.

10. A folding control device for supporting wheels of an electric wheelchair, characterized in that: The device includes: A state acquisition module is used to obtain the driving state information and surrounding environment information of the electric wheelchair; An obstacle determination module, used to determine whether there is an obstacle based on the driving state information and surrounding environment information; The steering obstacle avoidance judgment module is used to determine whether the obstacle can be avoided by steering when there is an obstacle; The folding obstacle avoidance trigger module is used to trigger the support wheel folding obstacle avoidance program based on the obstacle distance, wheelchair speed and environmental constraints when it is determined that obstacle avoidance cannot be achieved by steering; A folding parameter determination module, used to determine the folding direction and folding angle of the support wheels according to the obstacle position, the wheelchair travel direction and the obstacle height; A folding control module, used to control the support wheels to fold according to a determined folding direction and folding angle to achieve obstacle avoidance; The recovery control module is used to control the support wheels to automatically return to the original support state after passing through an obstacle.

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