A method and device for controlling the folding of support wheels of an electric wheelchair

Abstract

This application provides a method and device for controlling the folding of support wheels on an electric wheelchair, relating to the field of electric wheelchair technology. The key technical points are: acquiring the driving status information and surrounding environment information of the electric wheelchair; determining the presence of obstacles based on the driving status information and surrounding environment information; determining whether obstacle avoidance can be achieved by steering; triggering a support wheel folding obstacle avoidance program based on obstacle distance, wheelchair speed, and environmental constraints; determining the folding direction and folding angle of the support wheels based on the obstacle position, wheelchair driving direction, and obstacle height; controlling the support wheels to fold according to the determined folding direction and folding angle; and controlling the support wheels to automatically return to their original support state after passing the obstacle. The electric wheelchair support wheel folding control method and device provided in this application has the advantages of improving the obstacle avoidance ability and safety of electric wheelchairs, and enhancing the adaptability of wheelchairs in complex environments.

Description

Technical Field

[0001] This application relates to the field of electric wheelchair technology, and more specifically, to a method and device for controlling the folding of the support wheels of an electric wheelchair. Background Technology

[0002] As an essential mobility aid for people with mobility impairments, the safety and convenience of electric wheelchairs are paramount. To reduce wheelchair size while maintaining stability, many electric wheelchairs are equipped with retractable and foldable support wheels. The most common folding method is manual folding, which is inconvenient. Existing electric folding solutions are typically only used for wheelchair storage and cannot be used while the wheelchair is in motion.

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

[0004] When electric wheelchairs encounter obstacles, current technology struggles to effectively address them, easily leading to collisions, bumps, and other safety hazards. Especially at high speeds or in confined spaces, relying solely on steering to avoid obstacles is often insufficiently flexible and cannot meet the obstacle avoidance needs in complex environments. This not only affects riding comfort but may also endanger the user's personal safety.

[0005] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0006] The purpose of this application is to provide a method and device for controlling the folding of the support wheels of an electric wheelchair, which has 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] This application provides a method for controlling the folding of support wheels on an electric wheelchair, the technical solution of which is as follows:

[0008] The method includes: acquiring the driving status information and surrounding environment information of the electric wheelchair; determining whether there is an obstacle based on the driving status information and surrounding environment information; when there is an obstacle, determining whether obstacle avoidance can be achieved by steering; when it is determined that obstacle avoidance cannot be achieved by steering, triggering a support wheel folding obstacle avoidance program based on obstacle distance, wheelchair speed, and environmental constraints; determining the folding direction and folding angle of the support wheel based on 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; and controlling the support wheel to automatically return to its original support state after passing the obstacle.

[0009] Furthermore, this application also proposes that the step of determining whether obstacle avoidance can be achieved by turning when an obstacle exists includes: obtaining the current speed, safe turning angular velocity, and distance to the obstacle of the electric wheelchair; calculating the minimum safe distance required to complete the turn based on a preset safety threshold and the current speed and safe turning angular velocity of the electric wheelchair; and determining that obstacle avoidance cannot be achieved by turning when the actual distance between the electric wheelchair and the obstacle is less than the minimum safe distance.

[0010] Furthermore, this application also proposes that the step of determining that obstacle avoidance cannot be achieved by steering includes: acquiring the driving 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 the electric wheelchair makes a preset steering amplitude based on the driving speed and the obstacle position information; calculating the predicted fluctuation amplitude when the electric wheelchair contacts the obstacle based on 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.

[0011] Furthermore, this application also proposes that when it is determined that obstacle avoidance cannot be achieved by steering, the step of triggering the support wheel folding obstacle avoidance procedure based on obstacle distance, wheelchair speed, and environmental constraints includes: calculating the relative distance and predicted contact time between multiple support wheels and one or more obstacles based on obstacle distance, wheelchair speed, and environmental constraints; determining the obstacle avoidance priority of each support wheel and obstacle based on the relative distance and predicted contact time; and triggering the folding obstacle avoidance procedure of each support wheel in chronological order according to the obstacle avoidance priority, wherein, under the premise of ensuring wheelchair stability, the number of support wheels in the folded state at any given time is controlled not to exceed a preset threshold.

[0012] Furthermore, this application proposes to calculate the relative distances and predicted contact times between multiple support wheels and one or more obstacles based on obstacle distance, wheelchair speed, and environmental constraints, and to determine the obstacle avoidance priority of each support wheel and obstacle based on the relative distances and predicted contact times: when the obstacle is a dynamic obstacle, the current position and motion state of each support wheel are obtained, including the motion speed, as well as the movement trajectory and speed of the obstacle; based on the obtained current position and motion state of each support wheel, as well as the movement trajectory and speed of the obstacle, the position of each obstacle in a future time period is predicted, and the relative distance sequence between each support wheel and each obstacle at multiple time points is calculated; based on 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 wheelchair speed and environmental constraints, based on the minimum relative distance and predicted contact time, an obstacle avoidance priority score is assigned to each support wheel; based on the obstacle avoidance priority scores, all support wheels are sorted to obtain the final obstacle avoidance priority sequence.

[0013] Furthermore, this application proposes that the step of determining the folding direction and folding angle of the support wheel based on the obstacle position, the wheelchair's driving direction, and the obstacle height includes: acquiring 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, wherein the three-dimensional contour information of the obstacle includes the obstacle height; calculating the possible contact position and contact angle between the support wheel and the obstacle based on the current posture parameters of the electric wheelchair and the constructed obstacle spatial model, and calculating the minimum folding angle required to avoid collision in combination with the mechanical structural constraints of the support wheel; 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's 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; and determining the folding direction based on the wheelchair's driving direction and the relative position of the obstacle to achieve obstacle avoidance control.

[0014] Furthermore, this application also proposes that the step of setting the folding angle to the maximum foldable angle and adjusting the wheelchair speed to ensure safe passage when the minimum folding angle is greater than the maximum foldable angle of the support wheel includes: 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, selecting the steering angle that minimizes the fluctuation amplitude at the new contact position between the support wheel 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 wheel to the maximum foldable angle, calculating the maximum travel speed required for safe passage based on the optimized fluctuation amplitude; and adjusting the wheelchair speed to a value not exceeding the maximum travel speed to ensure safe passage through the obstacle.

[0015] Furthermore, this application also proposes that the step of controlling the number of support wheels in the folded state at any given time to not exceed a preset threshold includes: acquiring surface feature information of the obstacle, and when the obstacle is detected to have a plane that meets preset conditions, dynamically adjusting the preset threshold for the number of support wheels that can 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 monitoring the stability parameters of the wheelchair in real time; and dynamically adjusting the folding angle of each support wheel according to the real-time changes in the stability parameters, so as to maximize the passage efficiency while ensuring the stability of the wheelchair.

[0016] Furthermore, this 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 step of selecting the steering angle that minimizes the fluctuation amplitude at the new contact position between the support wheel and the obstacle by calculating and analyzing multiple alternative steering angles includes: obtaining n alternative steering angles θ1, θ2, ..., θ n The maximum foldable angle of the support wheel is α_max, and the obstacle space model is O(x, y, z, t). Within the time interval [t0, t1], for each candidate 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 optimal steering angle θ_opt.

[0017] Furthermore, this application also proposes an electric wheelchair support wheel folding control device, which includes: a status acquisition module for acquiring the driving status information and surrounding environment information of the electric wheelchair; an obstacle judgment module for judging whether there is an obstacle based on the driving status information and surrounding environment information; a steering obstacle avoidance judgment module for judging whether the obstacle can be avoided by steering when there is an obstacle; a folding obstacle avoidance triggering module for triggering the support wheel folding obstacle avoidance program based on obstacle distance, wheelchair speed and environmental constraints 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 based on 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.

[0018] As can be seen from the above, the electric wheelchair support wheel folding control method and device provided in this application obtains the driving status information and surrounding environment information of the electric wheelchair, determines whether there is an obstacle, and triggers the support wheel folding obstacle avoidance program when it is impossible to avoid the obstacle by turning. The folding direction and folding angle of the support wheel are determined according to the obstacle position, the driving direction of the wheelchair and the height of the obstacle, thereby realizing 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 complex environments. Attached Figure Description

[0019] Figure 1 A flowchart of a method for controlling the folding of support wheels in an electric wheelchair provided in this application.

[0020] Figure 2 This is a schematic diagram of the structure of an electric wheelchair support wheel folding control device provided in this application.

[0021] Figure 3 This is a schematic diagram of the overall structure of an electric wheelchair provided in this application.

[0022] Figure 4 This is a partial structural diagram of an electric wheelchair provided in this application.

[0023] Figure 5 This is a partial structural diagram of an electric wheelchair provided in this application.

[0024] In the diagram: 210, Status Acquisition Module; 220, Obstacle Judgment Module; 230, Steering and 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 Support Rod; 005, Mounting Rod; 006, Drive Component; 007, Camera; 008, Linkage Assembly; 009, Pedal; 010, Cavity. Detailed Implementation

[0025] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0026] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0027] The folding function of the support wheels in electric wheelchairs is primarily for storage, and is ineffective in handling obstacles during operation. This makes it difficult for electric wheelchairs to maneuver around obstacles, especially at high speeds or in confined spaces, where their ability to avoid obstacles by steering alone is limited. Furthermore, existing support wheel folding systems lack intelligence and adaptability, failing to automatically adjust the folding strategy based on real-time environmental and obstacle conditions. These issues severely impact the safety, maneuverability, and user experience of electric wheelchairs.

[0028] How to achieve intelligent obstacle avoidance when electric wheelchairs encounter obstacles during operation is a pressing technical problem that needs to be solved. In existing technologies, the folding function of the support wheels in electric wheelchairs is mainly for storage and cannot effectively cope with obstacles while in motion. This makes it difficult for electric wheelchairs to flexibly avoid obstacles, especially at high speeds or in confined spaces, where their ability to avoid obstacles by relying solely on steering is limited.

[0029] In this regard, refer to Figure 1 This application proposes a method for controlling the folding of support wheels on an electric wheelchair, the method comprising:

[0030] S110. Obtain the driving status information and surrounding environment information of the electric wheelchair;

[0031] S120. Determine whether there are obstacles based on driving status information and surrounding environment information;

[0032] S130. When there is an obstacle, determine whether the obstacle can be avoided by turning.

[0033] S140. When it is determined that obstacle avoidance cannot be achieved by steering, the support wheel folding obstacle avoidance procedure is triggered based on the obstacle distance, wheelchair speed and environmental constraints.

[0034] S150. Determine the folding direction and folding angle of the support wheels based on the location of the obstacle, the direction of wheelchair travel, and the height of the obstacle.

[0035] S160: Control the support wheels to fold according to the determined folding direction and folding angle to achieve obstacle avoidance;

[0036] S170. After passing through an obstacle, control the support wheel to automatically return to its original support state.

[0037] Among them, 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.

[0038] The surrounding environment information refers to the obstacles and terrain around the wheelchair, which can be collected using devices such as cameras and lidar. This information provides necessary data support for subsequent obstacle avoidance decisions.

[0039] Obstacle detection involves analyzing environmental information to identify objects or terrain that may affect wheelchair movement.

[0040] The steering obstacle avoidance judgment step involves assessing whether an obstacle can be avoided by changing the driving direction after it is detected. This step prioritizes conventional obstacle avoidance methods, helping to reduce unnecessary support wheel folding operations and improving the system's efficiency and stability.

[0041] The folding support wheels obstacle avoidance system is an alternative solution that is activated when steering obstacle avoidance is not feasible. By adjusting the position of the support wheels, the shape of the wheelchair is altered, thereby increasing its ability to navigate narrow spaces or overcome obstacles.

[0042] Among these, environmental constraints refer to the various objective limiting factors from the surrounding environment that electric wheelchairs must consider when folding their support wheels to avoid obstacles. These include spatial geometric constraints (distance to the sides, above, and front and back), ground conditions (flatness, slope, and material), and obstacle shapes (rigid / flexible, regular / irregular). These factors directly affect the folding strategy of the support wheels and are key to making safe and effective obstacle avoidance decisions.

[0043] Specifically, when it is determined that the obstacle avoidance program of folding the support wheels cannot be triggered based on the distance to the obstacle, the wheelchair speed and environmental constraints, the electric wheelchair can be controlled to slow down and stop. For example, when environmental constraints cause the support wheels to have no folding space, or when even if they are folded, they will still collide violently with the obstacle, causing the fluctuation amplitude to exceed the set value.

[0044] The determination of the folding direction and angle is based on calculations performed using the specific characteristics of the obstacle and the wheelchair's movement. This ensures the effectiveness and safety of the obstacle avoidance maneuvers while maximizing the stability of the wheelchair.

[0045] Specifically, it can be distinguished whether it folds forward or backward. When the wheelchair is moving forward and encounters an obstacle, it folds backward; when the wheelchair is moving backward and encounters an obstacle, it folds forward. This is because when moving forward, the detection distance for obstacles is limited, and the distance between the obstacle and the support wheels is short. If it folds forward, there is a possibility that the forward folding motion may cause a relative collision with the obstacle. However, if it folds backward, the two move in the same direction, which is safer.

[0046] The automatic recovery of the support wheels is an operation performed after the obstacle has been overcome, aiming to restore the wheelchair to its normal support state. This step ensures that the wheelchair can immediately regain optimal stability and maneuverability after completing obstacle avoidance.

[0047] The core innovation of this application lies in proposing an intelligent method for controlling the folding of the support wheels of an electric wheelchair. This method acquires real-time information on the wheelchair's driving status and the surrounding environment, and combines this with a multi-level judgment mechanism to achieve intelligent obstacle recognition and response. Especially when traditional steering obstacle avoidance methods fail, the system can automatically trigger a support wheel folding obstacle avoidance program and precisely control the folding parameters according to the specific situation. This method not only improves the maneuverability and safety of electric wheelchairs but also enables them to adapt to complex environments.

[0048] The working principle of this application can be divided into the following key steps:

[0049] First, various sensors installed on the electric wheelchair, such as speed sensors, accelerometers, and gyroscopes, collect real-time information about the wheelchair's movement. Simultaneously, cameras and lidar are used to scan the surrounding environment, acquiring information such as the location, shape, and size of obstacles. This data undergoes preliminary processing and fusion by a data processing unit.

[0050] Next, the system uses obstacle detection algorithms to analyze the processed data to determine if there are any obstacles that may affect the wheelchair's movement. If an obstacle is detected, the system first assesses whether it can be avoided by turning. This assessment is based on factors such as the wheelchair's current speed, the distance to the obstacle, and available turning space.

[0051] If the system determines that obstacle avoidance by steering is not possible, it will trigger a support wheel folding obstacle avoidance procedure. At this time, the system will comprehensively consider the distance to the obstacle, the speed of the wheelchair, and the constraints of the surrounding environment. Subsequently, based on the position of the obstacle, the direction of travel of the wheelchair, and the height of the obstacle, the system will accurately calculate the direction and angle in which the support wheels need to be folded.

[0052] After receiving these parameters, the folding control module controls the support wheels to fold in the specified direction and angle via actuators such as motors or hydraulic systems. Throughout the folding process, the system continuously monitors the wheelchair's stability to ensure that the folding operation does not compromise the wheelchair's safety.

[0053] Finally, once the wheelchair has successfully navigated the obstacle, the system automatically returns the support wheels to their original support position. This process also requires precise control to ensure a smooth transition and to avoid impacting the user experience.

[0054] It is worth noting that, in the scheme of this application, the support wheel refers to the small wheel located at the front of the wheelchair. The wheelchair is equipped with two large wheels, which are usually connected to the power unit and are typically driven to provide the wheelchair with propulsion. For example, see the following for details. Figure 3 , Figure 4 as well as Figure 5The wheelchair is equipped with large wheels 001 and support wheels 002. The support wheels 002 are located at the front of the wheelchair, and the distance between the two support wheels 002 is smaller than the distance between the two large wheels 001. The support wheels 002 are rotatably mounted on support rods 003. The other end of the support rods 003 is hinged to a mounting rod 005. The mounting rod 005 is L-shaped, with one end fixed to the main frame of the wheelchair. A footrest 009 is provided between the two mounting rods 005. Support rods 004 are provided on the side of the support rod 003 that expand outwards from the support wheels 002. The other end of the support rod 004 is hinged to the mounting rod 005 via a connecting rod assembly 008. In some specific embodiments... In the middle, the linkage assembly 008 includes two connecting rods that are hinged end to end. One connecting rod is hinged to the support rod 004, and the other connecting rod is hinged to the mounting rod 005. The mounting rod 005 has a camera 007 at the L-shaped corner. The front part of the mounting rod 005 also has a cavity 010 with a front opening. The support rod 003 is disposed in the cavity 010 and is hinged to the mounting rod 005. The mounting rod 005 is also hinged to 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 mechanism. In addition, a tension spring can be provided between the support rod 003 and the mounting rod 005 to ensure stability.

[0055] Reference Figure 4 When the camera 007 detects an obstacle and cannot avoid it by steering, the drive unit 006 drives the support rod 003 to fold forward. Since the support rod 003 and the mounting rod 005 are hinged, they can successfully rotate. In this process, the support rods 004 located on both sides of the support rod 003 also fold forward with the help of the connecting rod assembly 008. The connection with the connecting rod assembly 008 ensures the stability of the overall operation.

[0056] It is important to note that Figure 4 The design shown is a forward-folding scheme, but it can also be folded backward, or it can be achieved through a simple structural deformation design.

[0057] Furthermore, in some other embodiments, multiple support wheels 002 may be provided, such as... Figure 5 As shown, it can be equipped with 4 support wheels and a corresponding drive structure.

[0058] In addition, it should be noted that for the scheme with two support wheels, that is, the wheelchair is equipped with a total of four wheels, in this case, controlling one support wheel to lift up to avoid obstacles, the three wheels still form a stable three-point support structure with the ground, which is sufficient to ensure the basic stability of the wheelchair.

[0059] In addition, it should be noted that, as Figures 3 to 5 As 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 a support rod 003 can be regarded as "a support wheel" as described in this application.

[0060] In some embodiments described above, a step is proposed to determine whether obstacle avoidance can be achieved by steering when an obstacle is present, in order to determine whether the support wheel folding obstacle avoidance procedure needs to be triggered. However, accurately determining whether the electric wheelchair can avoid the obstacle by steering presents a challenge in this process. Simply judging based on the obstacle distance may lead to misjudgment, as the current speed and steering ability of the electric wheelchair also need to be considered. Furthermore, determining a safe steering distance threshold is also a critical issue, as it directly affects the accuracy and safety of the obstacle avoidance decision.

[0061] In response, this application further proposes to obtain the current speed, safe turning angular velocity, and distance to the obstacle of the electric wheelchair; calculate the minimum safe distance required to complete the turn based on a preset safety threshold and the current speed and safe turning angular velocity of the electric wheelchair; and determine that obstacle avoidance cannot be achieved by turning when the actual distance between the electric wheelchair and the obstacle is less than the minimum safe distance.

[0062] This technical solution provides the necessary data foundation for determining whether obstacle avoidance is possible by acquiring the electric wheelchair's current speed, safe turning angular velocity, and distance to the obstacle. Based on a preset safety threshold and the acquired data, the minimum safe distance required to complete the turn is calculated, taking into account the electric wheelchair's actual motion state and turning capability. By comparing the actual distance with the minimum safe distance, it is possible to accurately determine whether it is safe to avoid the obstacle by turning.

[0063] This method solves the problem of misjudgment that may result from simply relying on obstacle distance. By incorporating the current speed and safe steering angular velocity of the electric wheelchair, the dynamic characteristics of the wheelchair are taken into account, 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 arise from subjective judgment. When the actual distance is less than the minimum safe distance, the system can promptly determine that it cannot avoid the obstacle by turning, thereby triggering the subsequent support wheel folding obstacle avoidance procedure, improving the safety and efficiency of the entire obstacle avoidance process.

[0064] The core invention of this solution lies in combining the kinematic characteristics of the electric wheelchair with obstacle distance information to dynamically calculate the safe turning distance. This method not only improves the accuracy of judgment but also adapts to obstacle avoidance requirements at different speeds and turning capabilities, demonstrating excellent adaptability. Furthermore, by introducing a preset safety threshold, this solution provides an adjustable safety margin for the judgment process, further enhancing the system's reliability and flexibility.

[0065] In practice, various methods can be used to obtain the current speed, safe steering angular velocity, and distance to obstacles of the electric wheelchair. For example, the current speed can be obtained through the encoder or speed sensor of the wheelchair's drive motor; the safe steering angular velocity can be preset or calculated in real time based on the wheelchair's structural parameters and current load status; and the distance to obstacles can be measured using various sensors such as ultrasonic sensors, lidar, or cameras.

[0066] When calculating the minimum safe distance required to complete a turn, the acceleration characteristics and turning radius of the electric wheelchair can be taken into account. Specifically, the following formula can be used:

[0067] Minimum safe distance = (current speed² / (2 * maximum deceleration)) + (current speed * reaction time) + safety margin;

[0068] Among them, the maximum deceleration is the maximum value at which the wheelchair can safely decelerate, the reaction time includes the system processing time and the time to perform steering operations, and the safety margin is the extra distance reserved to deal with unexpected situations.

[0069] Furthermore, the safe steering angular velocity can be dynamically adjusted based on the wheelchair's structural characteristics and the current speed. For example, at high speeds, the safe steering angular velocity should be reduced accordingly to ensure stability during steering. This can be achieved using the following relationship:

[0070] Safe steering angular velocity = Base steering angular velocity * (1 - Current speed / Maximum speed);

[0071] The base steering angular velocity is the maximum safe steering angular velocity of a wheelchair when it is stationary.

[0072] When determining whether obstacle avoidance can be achieved by steering, this technical solution considers not only static distance factors but also the dynamic characteristics of the electric wheelchair. This comprehensive judgment method can more accurately assess the feasibility of steering for obstacle avoidance, thereby improving the accuracy and safety of obstacle avoidance decisions.

[0073] For example, when an electric wheelchair is traveling at 3 m / s, with a safe turning angular velocity of 0.5 rad / s and a distance of 5 meters from an obstacle in front, the system will first calculate the minimum safe distance required to complete the turn. Assuming a maximum deceleration of 2 m / s², a reaction time of 0.5 s, and a safety margin of 1 meter, then:

[0074] Minimum safe distance = (3² / (2 * 2)) + (3 * 0.5) + 1 = 4.25 meters;

[0075] 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 turning. Conversely, if the actual distance is less than 4.25 meters, the system will determine that obstacle avoidance cannot be achieved by turning and will trigger the support wheel folding obstacle avoidance procedure.

[0076] This dynamic calculation-based judgment method has significant advantages over fixed threshold judgment. It can adapt to obstacle avoidance requirements under different speeds and environments, improving the system's adaptability and safety. Furthermore, by introducing a safety margin, this method provides additional safety assurance for the judgment process, further reducing the risk of misjudgment.

[0077] Through this precise judgment mechanism, electric wheelchairs can make smarter and safer decisions when encountering obstacles. When it is determined that turning to avoid an obstacle is not an option, the system can promptly activate the support wheel folding obstacle avoidance program, thereby maximizing the wheelchair's maneuverability while ensuring safety. This not only improves the overall performance of the electric wheelchair but also provides users with a more comfortable and safer riding experience.

[0078] In some embodiments described above, a step is proposed to determine whether obstacle avoidance can be achieved through steering to ascertain whether the support wheel folding obstacle avoidance procedure needs to be triggered. However, relying solely on the minimum safe distance for this determination may not be comprehensive or accurate enough. In some cases, even if the minimum safe distance requirement is met, steering obstacle avoidance may still result in a collision between the wheelchair and the obstacle or significant swaying, affecting ride comfort and safety. Therefore, a more precise and comprehensive method is needed to assess the feasibility of steering obstacle avoidance.

[0079] In response, this application further proposes a step for determining that obstacle avoidance cannot be achieved by turning, which includes: obtaining the driving 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 the electric wheelchair makes a preset turning amplitude based on the driving speed and the obstacle position information; calculating the predicted fluctuation amplitude when the electric wheelchair contacts the obstacle based on the predicted contact point; and determining that obstacle avoidance cannot be achieved by turning when the predicted fluctuation amplitude is greater than a preset threshold.

[0080] This technical solution introduces the concepts of predicted contact point and predicted fluctuation amplitude, using these two key parameters to more comprehensively evaluate the feasibility of steering obstacle avoidance. Specifically, it first acquires the electric wheelchair's speed and the obstacle's location information; this data forms the basis for predictive calculations, considering both the wheelchair's dynamic characteristics and the obstacle's spatial position. Then, based on this information, the predicted contact point is calculated, simulating the steering process and predicting the possible point of contact with the obstacle. This step considers the wheelchair's trajectory and the spatial relationship with the obstacle. Next, the predicted fluctuation amplitude is calculated based on the predicted contact point, analyzing the potential fluctuation level when the wheelchair contacts the obstacle. Finally, by comparing the predicted fluctuation amplitude with a preset threshold, a conclusion is drawn regarding whether obstacle avoidance can be achieved through steering.

[0081] 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 maneuver in space, but also assesses the impact of potential fluctuations during maneuvering on ride comfort and safety. This approach can better cope with complex obstacle environments, improve the reliability of obstacle avoidance decisions, and thus enhance the intelligence level and safety of electric wheelchairs.

[0082] In practical applications, various methods can be used to predict the contact point. For example, a kinematic model of the wheelchair can be used, combined with the current speed, preset steering angle, and obstacle position, to predict the wheelchair's trajectory through numerical simulation. Another approach is to utilize machine learning algorithms, training the model based on a large amount of historical data to more accurately predict the contact point under different conditions.

[0083] The calculation of predicted fluctuation amplitude can also be achieved in several ways. One approach is to establish a dynamic model based on the mass distribution of the wheelchair and the characteristics of the suspension system to simulate the collision process. Another approach is to establish an empirical model using experimental data, estimating the fluctuation amplitude based on parameters such as the location of the contact point and the wheelchair speed.

[0084] The vehicle speed and obstacle location information directly affect the calculation of the predicted contact point, which in turn forms the basis for calculating the predicted fluctuation amplitude. The comparison between the predicted fluctuation amplitude and the preset threshold determines the final judgment result.

[0085] By introducing predicted contact points and predicted fluctuation amplitudes, this approach considers not only spatial distance but also the impact of dynamic collisions on wheelchair stability. This comprehensive consideration makes obstacle avoidance decisions more accurate and better adaptable to various complex road conditions and obstacles. For example, when encountering a moving obstacle in a narrow passage, even if the minimum safe distance requirement is met, this solution can predict the fluctuation amplitude to determine whether steering will cause inappropriate bumps, thus making a safer decision.

[0086] In practice, the electric wheelchair first obtains its current speed through a sensor system, which may include wheel speed sensors and gyroscopes. Obstacle location information can be obtained through lidar, ultrasonic sensors, or cameras. Assume the wheelchair's current speed is 5 km / h, and a stationary obstacle is detected ahead.

[0087] Based on this information, the system uses a pre-defined kinematic model to calculate the predicted contact point. For example, assuming a preset turning radius of 30 degrees, simulations show that contact with the obstacle is likely to occur at a distance of 2.5 meters. Then, using a dynamic model that considers the wheelchair's mass (e.g., 100 kg), suspension system characteristics, and contact point location, the system calculates a predicted fluctuation amplitude of 0.15 g (where g is the acceleration due to gravity).

[0088] Assuming the preset fluctuation threshold is 0.1g, and the predicted fluctuation amplitude of 0.15g exceeds the threshold, the system determines that obstacle avoidance cannot be achieved by steering. In this case, the electric wheelchair will trigger the support wheel folding obstacle avoidance procedure instead of performing a steering operation that might cause significant jolts.

[0089] In this way, the technical solution can more accurately assess the feasibility of steering and obstacle avoidance, effectively reducing unnecessary bumps and potential collision risks, and improving ride comfort and safety. At the same time, this method also improves the adaptability of electric wheelchairs in complex environments, enabling them to more intelligently handle various obstacle situations.

[0090] In some embodiments described above, when it is determined that obstacle avoidance cannot be achieved by steering, a support wheel folding obstacle avoidance procedure is triggered based on obstacle distance, wheelchair speed, and environmental constraints to achieve obstacle avoidance. However, determining the folding sequence of multiple support wheels and controlling the number of support wheels folded simultaneously to achieve efficient obstacle avoidance while ensuring wheelchair stability remains a problem that needs to be solved.

[0091] In response, this application further proposes a step for triggering a support wheel folding obstacle avoidance procedure based on obstacle distance, wheelchair speed, and environmental constraints when it is determined that obstacle avoidance cannot be achieved by steering. This step includes: calculating the relative distance and predicted contact time between multiple support wheels and one or more obstacles based on obstacle distance, wheelchair speed, and environmental constraints; determining the obstacle avoidance priority of each support wheel and obstacle based on the relative distance and predicted contact time; and triggering the folding obstacle avoidance procedure of each support wheel in chronological order according to the obstacle avoidance priority, wherein, while ensuring wheelchair stability, the number of support wheels in the folded state at any given time is controlled to not exceed a preset threshold.

[0092] The technical solution of this application determines the obstacle avoidance priority of each support wheel by calculating the relative distances between multiple support wheels and obstacles and predicting the contact time. This method considers obstacle distance, wheelchair speed, and environmental constraints, enabling a more accurate assessment of the obstacle avoidance needs of each support wheel. Based on the determined obstacle avoidance priority, the system sequentially triggers the folding obstacle avoidance procedure of each support wheel in chronological order. This sequential folding control strategy ensures that the support wheels most in need of obstacle avoidance fold first, improving obstacle avoidance efficiency. Simultaneously, this application also introduces a control mechanism for the number of support wheels simultaneously in a folded state. By setting a preset threshold, it ensures that the number of support wheels in a folded state at any given time does not exceed this threshold. This measure ensures the overall stability of the wheelchair while achieving obstacle avoidance.

[0093] The technical solution of this application achieves intelligent and precise control of the folding obstacle avoidance of the support wheels by comprehensively considering multiple factors. It not only solves the problem of determining the folding sequence of multiple support wheels, but also achieves a balance between ensuring wheelchair stability and obstacle avoidance efficiency by controlling the number of support wheels folding simultaneously. Compared to simply folding all support wheels simultaneously or in a fixed sequence, this method offers greater flexibility and adaptability, and is better able to cope with complex obstacle environments.

[0094] In the technical solution of this application, calculating the relative distance between multiple support wheels and one or more obstacles and predicting the contact time based on obstacle distance, wheelchair speed, and environmental constraints can be achieved in various ways. For example, laser rangefinders, ultrasonic sensors, or visual sensing systems can be used to obtain obstacle location information.

[0095] Based on the acquired data, a predictive algorithm can be used to calculate the relative position of each support wheel to the obstacle at a future time point. The predicted contact time can be determined by calculating the time when the relative distance between the support wheel and the obstacle reaches its minimum.

[0096] Determining obstacle avoidance priorities can consider multiple factors, such as relative distance, predicted contact time, and the current load on the support wheels. Weights can be assigned to each factor, and a priority score for each support wheel can be calculated using a weighted summation method. The support wheel with the highest priority score will trigger the folding obstacle avoidance procedure first.

[0097] A key feature of this application is controlling the number of support wheels in the folded state at any given time to not exceed a preset threshold. This preset threshold can be determined based on the wheelchair's structural characteristics, center of gravity distribution, and stability requirements. For example, for a four-wheeled electric wheelchair, the preset threshold might be set to 1. For a six-wheeled electric wheelchair, the preset threshold might be set to 2, and the specific setting can be adjusted according to the specific wheelchair structure.

[0098] The aforementioned four-wheeled electric wheelchair includes two large wheels and two support wheels, while the six-wheeled electric wheelchair includes two large wheels and four support wheels.

[0099] In some preferred embodiments, the solution of this application is applicable to electric wheelchairs with six-wheel support, wherein the six-wheel support is symmetrically arranged on the left and right sides with three wheels on each side. In this case, the preset threshold is set to 1. This method has very high stability, that is, the folding of one support wheel will hardly affect the overall operational stability of the wheelchair.

[0100] In practical applications, the technical solution of this application can work in conjunction with other control systems of the wheelchair. For example, while triggering the obstacle avoidance program by folding the support wheels, the wheelchair's speed and direction can be adjusted appropriately to further improve the obstacle avoidance effect and safety. Furthermore, the folding angle of the support wheels can be dynamically adjusted according to the height and shape of the obstacle to achieve more precise obstacle avoidance control.

[0101] The technical solution of this application effectively solves the obstacle avoidance problem of electric wheelchairs in complex environments through intelligent support wheel folding control. By calculating the relative distances between multiple support wheels and obstacles and predicting the contact time, the system can accurately assess the obstacle avoidance requirements of each support wheel. This method takes into account obstacle distance, wheelchair speed, and environmental constraints, and has greater flexibility and adaptability compared to simple fixed-sequence folding or simultaneous folding of all support wheels.

[0102] Based on the determined obstacle avoidance priority, the system triggers the folding obstacle avoidance program of each support wheel sequentially in chronological order. This sequential folding control strategy ensures that the support wheels most in need of obstacle avoidance are folded first, thereby improving obstacle avoidance efficiency. At the same time, by controlling the number of support wheels in the folded state at any given time to not exceed a preset threshold, the technical solution of this application achieves efficient obstacle avoidance while also ensuring the overall stability of the wheelchair.

[0103] This intelligent folding control method can better cope with complex obstacle environments. For example, in narrow passages, the system can prioritize folding support wheels that may collide with the wall, while maintaining the support of other support wheels to ensure the stability of the wheelchair. When facing multiple obstacles, the system can dynamically adjust the folding sequence and timing of each support wheel based on the predicted contact time, achieving a smoother and safer obstacle avoidance process.

[0104] In this way, the technical solution of this application achieves intelligent folding control of the support wheels, improving obstacle avoidance efficiency and safety while ensuring wheelchair stability. This method not only solves the problem of determining the folding sequence of multiple support wheels, but also achieves a good balance between ensuring wheelchair stability and obstacle avoidance efficiency by controlling the number of support wheels folding simultaneously.

[0105] In some embodiments described above, a method is proposed to determine the obstacle avoidance priority of each support wheel and obstacle based on obstacle distance, wheelchair speed, and environmental constraints to trigger a support wheel folding obstacle avoidance procedure. However, in this process, when facing dynamic obstacles, simply determining the obstacle avoidance priority based on the current state may not accurately predict future collision risks, leading to inaccurate and untimely obstacle avoidance decisions. Furthermore, considering only the relative position at a single point in time may ignore the movement trend of the obstacle and fail to comprehensively assess the obstacle avoidance requirements.

[0106] To address this, this application further proposes the following steps when the obstacle is dynamic: First, the current position and motion state of each support wheel are obtained, including its speed, as well as the obstacle's trajectory and speed. Based on the obtained current position and motion state of each support wheel, and the obstacle's trajectory and speed, the position of each obstacle within a future time period is predicted, and a relative distance sequence between each support wheel and each obstacle at multiple time points is calculated. Based on 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 wheelchair speed and environmental constraints, an obstacle avoidance priority score is assigned to each support wheel based on the minimum relative distance and predicted contact time. Finally, all support wheels are sorted according to their obstacle avoidance priority scores to obtain the final obstacle avoidance priority sequence.

[0107] The technical solution of this application transforms static obstacle avoidance decision-making 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 trends of obstacles and possible future scenarios, the accuracy and reliability of obstacle avoidance decisions are greatly improved. Simultaneously, by comprehensively considering multiple factors for priority scoring, a more intelligent and flexible obstacle avoidance strategy is achieved, enabling better adaptation to complex and changing environments. This method not only improves the safety of electric wheelchairs but also enhances their adaptability and maneuverability in dynamic environments.

[0108] Specifically, the technical solution of this application includes the following key steps:

[0109] First, obtain dynamic obstacle information. This step involves acquiring the current position and motion state of each support wheel, as well as the movement trajectory and speed of the obstacle.

[0110] Secondly, future position prediction is performed. Based on the acquired data, this application employs a prediction algorithm to estimate the position 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 obstacle's trajectory. The prediction time range can be dynamically adjusted according to the wheelchair's speed and environmental complexity, typically ranging from 0.5 seconds to 3 seconds.

[0111] 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 wheels and obstacles at fixed time intervals (e.g., 0.1 seconds) within the predicted time range. The expected trajectory of the wheelchair needs to be considered during the calculation, which can be done through simple linear extrapolation based on the current speed and direction, or by using a more complex wheelchair dynamics model to predict the future position of the wheelchair.

[0112] Next, the minimum relative distance and predicted contact time are determined. By analyzing the calculated relative distance sequence, this 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.

[0113] Based on the above information, this application performs obstacle avoidance priority scoring. This step combines wheelchair speed and environmental constraints, assigning an obstacle avoidance priority score to each support wheel based on the minimum relative distance and predicted contact time. The scoring function can be designed as follows:

[0114] Score = w1 * (1 / min_distance) + w2 * (1 / time_to_contact) + w3 *wheelchair_speed + w4 * environmental_factor;

[0115] Among them, w1, w2, w3, and w4 are weighting 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 narrow passages).

[0116] Finally, priority sorting is performed. Based on the calculated obstacle avoidance priority scores, all support wheels are sorted to obtain the final obstacle avoidance priority sequence. Sorting can be implemented using efficient algorithms such as quicksort or heapsort to ensure rapid response in real-time systems.

[0117] Specifically, after obtaining the obstacle avoidance priority list of the support wheels based on dynamic prediction and multi-time point analysis, it is necessary to further determine which support wheels actually need to be folded. This decision-making process is not simply to fold all of them according to priority, but requires further judgment, specifically considering the direction of travel of the electric wheelchair and the relative directions of the support wheels and obstacles.

[0118] The control system checks the relative position of each support wheel to the obstacle in descending order of obstacle avoidance priority. Combined with the direction of travel of the electric wheelchair, it determines 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. The support wheels in this list will be folded in order of priority.

[0119] During the folding process, to ensure the stability of the electric wheelchair, it is necessary to strictly control the number of support wheels in the folded state at any given time. In this embodiment, it is assumed that the preset threshold for the number of support wheels that can be folded simultaneously is 1, meaning that at any given time, only one support wheel is allowed to fold. Therefore, when controlling the folding of the support wheels, it is necessary to handle possible folding conflicts.

[0120] When the control system executes the folding operation sequentially according to the list of support wheels that "need to be folded," it monitors in real time whether any other support wheels are currently folded. If no other support wheels are currently folded, it directly controls the support wheel that needs to be folded to begin folding. However, if other support wheels are already folded (i.e., the preset threshold 1 is not met), conflict handling is required according to a predefined strategy.

[0121] To address this situation, this embodiment provides several optional processing strategies. The specific strategy adopted depends on the specific design of the electric wheelchair, the application scenario, and the requirements for safety and efficiency. One optional strategy is the "waiting" strategy. When using this strategy, the system pauses the folding operation of the support wheel that currently needs to be folded and puts it into a waiting state. The system continuously monitors the status of the previous support wheel, and once the previous support wheel has completed folding and returned to its original support state, the folding operation of the waiting support wheel is immediately initiated.

[0122] Another alternative strategy is the "skip" strategy. With this strategy, the system skips the support wheel that currently needs to be folded and proceeds to the next lower-priority support wheel in the "needs to be folded" list. This strategy is suitable when the obstacle is small, or when skipping the folding of the current support wheel will not significantly increase the risk of a collision.

[0123] In addition to the "wait" and "skip" strategies, an "emergency braking" strategy can also be considered. If skipping the folding of the current support wheel would lead to a collision with the obstacle, and the waiting time is too long, it would affect the overall obstacle avoidance efficiency. The system can trigger the emergency braking of the electric wheelchair, causing the wheelchair to stop immediately and wait for manual intervention or further instructions.

[0124] Furthermore, a "driving adjustment" strategy can be employed. That is, when encountering a folding conflict, the system can slightly adjust the electric wheelchair's direction or speed, then re-perform obstacle detection, distance calculation, and priority ranking steps to generate a new obstacle avoidance strategy and a list of support wheels that need to be folded. This strategy avoids folding conflicts by dynamically adjusting the wheelchair's motion.

[0125] Another more refined strategy is "delayed folding and insertion into a queue." Unlike direct waiting, this strategy adds the folding instructions of support wheels that cannot be folded immediately due to conflicts to a waiting queue. Once the previous support wheel has been folded and restored, the system checks the waiting queue and executes the folding operation of the next support wheel according to the order in the queue (usually still according to priority).

[0126] In practice, one or more of the above strategies can be selected based on the actual situation. For example, the "wait" strategy can be tried first, and if the waiting time exceeds a preset threshold, the "adjust driving" strategy can be switched to; or in certain specific scenarios, the "skip" strategy can be used directly. By flexibly applying these strategies, efficient and safe obstacle avoidance operations can be achieved while ensuring the stability of the electric wheelchair.

[0127] The technical solution of this application effectively addresses the issues of accuracy and timeliness in obstacle avoidance decision-making when facing dynamic obstacles through the synergistic effect of the aforementioned steps. By predicting future positions and calculating relative distances at multiple time points, this solution can more comprehensively assess potential collision risks, avoiding the limitations of making decisions solely based on the current state. Combining wheelchair speed and environmental constraints for priority scoring further improves the adaptability and rationality of obstacle avoidance decisions. The final priority ranking ensures that the support wheels that most urgently need to be folded can perform obstacle avoidance operations first, thereby improving overall obstacle avoidance efficiency and safety.

[0128] In some embodiments described above, the folding direction and angle of the support wheels are determined based on the obstacle's location, the wheelchair's direction of travel, and the obstacle's height to achieve obstacle avoidance control. However, in this process, due to the lack of precise perception of the obstacle's three-dimensional contour and real-time monitoring of the wheelchair's posture, it is difficult to accurately calculate the contact position between the support wheels and the obstacle and the required minimum folding angle. Furthermore, when the obstacle has a complex shape or the wheelchair is traveling at a high speed, relying solely on a simple folding strategy may not guarantee safe passage; it is necessary to combine wheelchair speed adjustment and steering optimization to improve obstacle avoidance performance.

[0129] To address this, this application further proposes a step-by-step approach to determine the folding direction and angle of the support wheels based on the obstacle's location, the wheelchair's travel direction, and the obstacle's height. This includes: acquiring 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, which includes the obstacle's height; calculating the possible contact positions and angles between the support wheels and the obstacle based on the current attitude parameters of the electric wheelchair and the constructed obstacle spatial model, and calculating the minimum folding angle required to avoid collision, considering the mechanical structural constraints of the support wheels; 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's travel 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; and determining the folding direction based on the wheelchair's travel direction and the relative position of the obstacle to achieve obstacle avoidance control.

[0130] This technical solution constructs a spatial model of the obstacle by acquiring its 3D contour information and the current posture parameters of the electric wheelchair, thereby achieving precise perception of the obstacle's shape and position. This provides an accurate data foundation for subsequent calculation of the folding angle. Based on the electric wheelchair's posture parameters and the obstacle's spatial model, the possible contact positions and angles between the support wheels and the obstacle are calculated. Combined with the mechanical structural constraints of the support wheels, the minimum folding angle required to avoid a collision is calculated. This method considers the real-time state of the wheelchair and the specific shape of the obstacle, enabling a more accurate determination of the required folding angle. The solution also considers the maximum foldable angle limit of the support wheels. When the calculated minimum folding angle exceeds the maximum foldable angle of the support wheels, a joint adjustment strategy of folding angle and wheelchair speed is adopted, ensuring that the folding of the support wheels is within a feasible range while ensuring safe passage through speed adjustment. Finally, the folding direction is determined based on the wheelchair's travel direction and the relative position of the obstacle, achieving all-around obstacle avoidance control. This method considers not only the folding angle but also the folding direction, making obstacle avoidance more flexible and efficient.

[0131] Point cloud data processing techniques can be used when constructing obstacle spatial models. First, the acquired 3D point cloud data is filtered and denoised. Then, clustering algorithms are used to segment the point cloud into different obstacle objects. For each obstacle object, its 3D surface model can be constructed using methods such as convex hull algorithms or B-spline surface fitting.

[0132] This application employs a dynamic collision detection algorithm to calculate the possible contact positions and angles between the support wheels and obstacles. Specifically, based on the current motion state of the electric wheelchair (such as speed and direction, including current posture parameters) and the spatial model of the obstacle, the motion trajectory of the support wheels over a future period is predicted. Then, by calculating the intersection points of the support wheel trajectory and the obstacle model, the possible contact positions and angles are determined.

[0133] In calculating the minimum folding angle required to avoid a collision, this application considers the mechanical structural constraints of the support wheel. For example, the support wheel may have a maximum folding angle limit, or folding within certain angle ranges may affect the stability of the wheelchair. By establishing a kinematic model of the support wheel, the position and attitude of the support wheel at different folding angles can be accurately calculated. Combining this model with an obstacle space model, the minimum folding angle that can avoid a collision can be found using optimization algorithms (such as gradient descent).

[0134] When the calculated minimum folding angle exceeds the maximum foldable angle of the support wheels, the folding angle is set to the maximum foldable angle. Then, the wheelchair's speed is adjusted to ensure safe passage. Specifically, a relationship model between speed, folding angle, and safe distance can be established. This model calculates the maximum speed at which the wheelchair can safely pass through the obstacle at the current folding angle. This method ensures that the folding of the support wheels remains within a feasible range while also ensuring safe passage through speed adjustment, significantly improving obstacle avoidance flexibility and adaptability.

[0135] When determining the folding direction, this application considers the combined influence of the wheelchair's travel direction and the relative position of obstacles. A coordinate system is established, with the wheelchair's travel direction as the principal axis, and the relative position of obstacles within this coordinate system is calculated. Then, based on the obstacle distribution, the folding direction that best avoids obstacles is selected. This method enables omnidirectional obstacle avoidance control and adapts to various complex obstacle distributions.

[0136] Through the above technical solutions, this application achieves accurate obstacle perception and real-time monitoring of wheelchair posture, solving the problems existing in 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. Simultaneously, by introducing speed adjustment and direction optimization strategies, this application can effectively cope with obstacles of complex shapes and high-speed driving conditions, significantly improving the obstacle avoidance ability and adaptability of the electric wheelchair.

[0137] As a preferred embodiment, this application can achieve intelligent folding obstacle avoidance of the support wheels using the following specific steps:

[0138] First, a LiDAR scanner mounted on the front of the electric wheelchair scans the surrounding environment to acquire point cloud data of obstacles. Simultaneously, IMU sensors mounted on the wheelchair's chassis acquire the wheelchair's attitude parameters in real time, including tilt angle, acceleration, and angular velocity.

[0139] Next, the point cloud data is segmented and clustered to identify the outlines of obstacles. Then, a B-spline surface fitting algorithm is used to construct a three-dimensional spatial model of the obstacles. This model establishes a three-dimensional coordinate system with the current position of the wheelchair as the origin.

[0140] Based on the wheelchair's current speed (assumed to be 5 km / h) and orientation, predict the trajectory of the support wheels over the next 3 seconds. By calculating the intersection of this trajectory with the obstacle model, determine the possible contact point and angle.

[0141] Assuming the maximum folding angle of the support wheel is 45 degrees, the minimum folding angle required to avoid a collision is calculated using an optimization algorithm. If the calculated result is 30 degrees, which is less than the maximum folding angle, then the folding angle is directly set to 30 degrees.

[0142] The optimal folding direction is calculated based on the wheelchair's direction of travel and the relative position of the obstacle. For example, if the obstacle is located at a 15-degree angle to the right front of the wheelchair, the folding direction can be set to 20 degrees to the left to avoid the obstacle as much as possible.

[0143] Finally, control the support wheel motor to perform the folding operation, ensuring that the set folding angle of 30 degrees and the folding direction of 20 degrees to the left are accurately achieved.

[0144] In this way, this application enables precise control of the folding of the support wheels under different obstacle conditions, achieving safe and efficient obstacle avoidance. This method not only improves the maneuverability of electric wheelchairs but also enhances the user's comfort and sense of security.

[0145] In some embodiments described above, a step is proposed whereby, 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 wheelchair speed is adjusted to ensure safe passage. This is intended to guarantee the safe passage of the wheelchair when the support wheels cannot completely avoid obstacles. However, simply setting the maximum foldable angle and adjusting the speed may not adequately guarantee the stability and passage efficiency of the wheelchair, especially in complex obstacle environments. Furthermore, simple speed adjustments may reduce wheelchair travel efficiency, impacting the user experience. Therefore, a more intelligent and optimized method is needed to address this situation.

[0146] In response, this 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, the optimal steering angle is selected by calculating and analyzing multiple alternative steering angles to minimize the fluctuation amplitude at the new contact position between the support wheel and the obstacle; the wheelchair is controlled to perform a steering operation with the optimized steering angle, and the folding angle of the support wheel is set to the maximum foldable angle; the maximum travel speed required for safe passage is calculated based on the optimized fluctuation amplitude; and the wheelchair's travel speed is adjusted to a value that does not exceed the maximum travel speed to ensure safe passage through the obstacle.

[0147] The technical solution of this application introduces several key technical features to address the aforementioned problems. First, by constructing a spatial model of the obstacles, an accurate data foundation is provided for subsequent calculations and decisions. Based on this model, the fluctuation amplitude is calculated to assess stability when passing through obstacles. When the fluctuation amplitude exceeds a preset threshold, the optimal solution is found by analyzing multiple alternative turning angles, thus improving the intelligence and adaptability of the decision-making process.

[0148] Specifically, this application selects a steering angle that minimizes the fluctuation at the new contact point between the support wheel and the obstacle, thereby reducing instability when passing over obstacles. Simultaneously, the folding angle of the support wheel is set to the maximum foldable angle, and the maximum travel speed required for safe passage is calculated and adjusted based on the optimized fluctuation range, achieving coordinated optimization of the folding angle and speed.

[0149] These technical features work together to solve the problem of ensuring the safe and stable passage of a wheelchair when the support wheels cannot completely avoid obstacles. This application not only considers the safety of the wheelchair but also takes into account the efficiency of passage. Through an intelligent decision-making process, it maximizes the efficiency of wheelchair passage while ensuring safety.

[0150] The core invention 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 angle and speed. Compared to 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 wheelchairs in overcoming obstacles.

[0151] Furthermore, the technical solution of this application can be implemented in the following ways:

[0152] First, the amplitude of the undulation is calculated based on the obstacle spatial model and the maximum folding angle of the support wheels. This can be achieved by establishing a three-dimensional obstacle model, combining the wheelchair's motion trajectory and the folding angle of the support wheels, and using numerical simulation methods to calculate the undulation of the wheelchair when passing over the obstacle.

[0153] Secondly, when the calculated fluctuation amplitude exceeds a preset threshold, the system generates multiple alternative steering angles. These angles can be determined based on the wheelchair's current position, speed, and the relative position of the obstacle. For example, alternative steering angles can be generated in 5° intervals within the range of -30° to 30°.

[0154] Next, each alternative steering angle is analyzed. This step uses a dynamic model to simulate the wheelchair's trajectory at each steering angle, calculate the new contact position between the support wheels and the obstacle, and evaluate the corresponding fluctuation amplitude.

[0155] Then, the steering angle that produces the minimum fluctuation amplitude is selected as the optimal steering angle. This selection process can be achieved by comparing the fluctuation amplitude values ​​at different angles and selecting the angle with the smallest value.

[0156] When controlling a wheelchair to perform steering operations with optimized steering angles, a progressive steering control strategy can be used to ensure a smooth steering process without causing additional instability.

[0157] At the same time, the folding angle of the support wheels is set to the maximum folding angle. This can be achieved through an electric control mechanism, ensuring that the support wheels avoid obstacles to the greatest extent possible.

[0158] Calculate the maximum speed required for safe passage. This calculation can take into account factors such as the wheelchair's mass, the contact area between the wheelchair and the obstacle, and the expected impact force, using a dynamic model for simulation.

[0159] Finally, the wheelchair's speed is adjusted to not exceed the calculated maximum speed. This can be achieved through the motor control system, ensuring the wheelchair operates within a safe speed range.

[0160] The technical solution of this application can maximize the efficiency of wheelchairs in passing through obstacles while ensuring safety. For example, when encountering a protruding obstacle in a narrow passage, the traditional method may simply slow down and try to pass directly, while the method of this application calculates an optimal small-angle steering and precisely controls the folding angle and speed of the support wheels, thereby passing through obstacles more smoothly and quickly.

[0161] In a preferred embodiment, the present application can implement the following specific steps in the control system of an electric wheelchair:

[0162] Using sensors such as LiDAR or depth cameras, the surrounding environment is scanned to construct an accurate three-dimensional obstacle space model.

[0163] Based on the acquired obstacle space model and the maximum foldable angle of the support wheels (e.g., 45°), the finite element analysis method is used to calculate the fluctuation amplitude of the wheelchair when passing over the obstacle. A preset fluctuation amplitude threshold of 5 mm is assumed.

[0164] When the calculated fluctuation amplitude exceeds 5mm, the system generates 21 alternative steering angles at 2° intervals within the range of -20° to 20°.

[0165] For each alternative steering angle, dynamic simulation software was used to simulate the wheelchair's passage over an obstacle, calculating the new contact position between the support wheels and the obstacle and the corresponding fluctuation amplitude. The angle with the smallest fluctuation amplitude was selected as the optimal steering angle.

[0166] A servo motor control system is used to perform steering operations with an optimized steering angle at an angular velocity of 0.5° / s, ensuring a smooth steering process.

[0167] The folding angle of the support wheels is set to a maximum folding angle of 45°, and a high-precision stepper motor is used to control the folding process at a folding speed of 10° / s.

[0168] Based on the optimized fluctuation range, a neural network model is used to predict the maximum speed required for safe passage. For example, if the predicted maximum speed is 2 km / h, the wheelchair speed is limited to below 1.8 km / h, leaving a 10% safety margin.

[0169] Throughout the obstacle course, the stability of the wheelchair is continuously monitored using accelerometers and gyroscopes. If any abnormal fluctuations are detected, the system will immediately adjust the speed or steering angle.

[0170] In this way, the technical solution of this application can provide a more intelligent and safer obstacle-crossing 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, thereby smoothly and quickly passing through the obstacle. In contrast, traditional methods may require a complete stop for a large-angle turn or manual adjustment of the support wheels.

[0171] This application's technical solution effectively addresses the problem of ensuring a wheelchair's safe and stable passage through obstacles when the support wheels cannot completely avoid them, by introducing an intelligent analysis and selection mechanism with multiple alternative steering angles and a collaborative optimization strategy for folding angle and speed. Compared to simply setting the maximum foldable angle and adjusting the speed, this solution better adapts to complex obstacle environments, improving the success rate and stability of the wheelchair in overcoming obstacles. Simultaneously, by selecting and optimizing the steering angle and accurately calculating the maximum travel speed, it maximizes the wheelchair's travel efficiency while ensuring safety, significantly improving the user experience. This method not only solves the stability and efficiency problems that may arise from simple folding and deceleration in existing technologies but also provides a new technical approach for the intelligent control system of electric wheelchairs, demonstrating significant technological advancement.

[0172] In some embodiments described above, a method is proposed to control the number of support wheels in the folded state at any given time to not exceed a preset threshold in order to ensure the stability of the electric wheelchair. However, in this process, a fixed preset threshold may not be suitable for complex and varied obstacle environments, making it difficult to optimize the balance between wheelchair stability and passage 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 existing solutions have not fully considered this factor.

[0173] In response, this application further proposes to acquire surface feature information of obstacles, and when an obstacle is detected to have a plane that meets preset conditions, dynamically adjust a preset threshold for the number of support wheels that can be folded simultaneously based on the calculated contact area between the support wheels and the plane; control the contact between the folded support wheels and the plane of the obstacle, and monitor the stability parameters of the wheelchair in real time; and dynamically adjust the folding angle of each support wheel according to the real-time changes in the stability parameters, so as to maximize the passage efficiency while ensuring the stability of the wheelchair.

[0174] The technical solution of this application first obtains surface feature information of obstacles, especially detecting planes that meet preset conditions, providing a basis for dynamically adjusting the folding strategy of the support wheels. Based on the calculated contact area between the support wheels and the obstacle plane, the system can more accurately assess the stability of the wheelchair, thereby dynamically adjusting the preset threshold for the number of support wheels that can be folded simultaneously. This dynamic adjustment mechanism can flexibly change according to the actual situation, ensuring the stability of the wheelchair while improving the efficiency of passing through obstacles.

[0175] Secondly, by controlling the contact between the folded support wheels and the obstacle surface, the system can fully utilize the obstacle's surface features to increase the wheelchair's stability. Real-time monitoring of the wheelchair's stability parameters provides real-time and accurate data support for subsequent dynamic adjustments.

[0176] Finally, based on real-time monitoring of changes in stability parameters, the system can dynamically adjust the folding angle of each support wheel. This real-time, precise adjustment strategy not only ensures the stability of the wheelchair during obstacle crossing but also maximizes crossing efficiency within the allowable stability range.

[0177] The technical solution of this application effectively solves the problem that fixed preset thresholds are difficult to adapt to complex environments by introducing innovative features such as obstacle surface feature analysis, dynamic threshold adjustment, real-time stability monitoring, and refined folding angle control. It can intelligently balance wheelchair stability and passage efficiency in different obstacle environments, significantly improving the obstacle avoidance ability and adaptability of electric wheelchairs. This method not only improves the safety of wheelchair use but also enhances its mobility in complex environments, providing users with a more intelligent, safe, and efficient travel experience.

[0178] As a preferred embodiment, the electric wheelchair support wheel folding control method of this application can be specifically implemented as follows:

[0179] First, the electric wheelchair is equipped with a high-precision 3D laser scanner and depth camera to acquire three-dimensional information about the surrounding environment. When an obstacle is detected within 5 meters ahead, the system initiates a detailed surface feature analysis program.

[0180] For example, the system detects a rectangular obstacle that is 1.5 meters long, 0.8 meters wide, and 0.3 meters high. By analyzing the obtained point cloud data, the system identifies a plane with an area of ​​1.2 square meters and a tilt angle of no more than 5 degrees on the top of the obstacle. This plane meets the preset condition of "area greater than 1 square meter and tilt angle less than 10 degrees" and is identified by the system as a possible contact surface for the support wheel.

[0181] Next, based on the geometric parameters of the support wheel (assuming a diameter of 15 cm and a width of 5 cm) and the characteristics of the obstacle plane, the system calculates the theoretical maximum contact area to be approximately 75 square centimeters. Considering that the actual contact may not be a perfect fit, the system estimates the effective contact area as 80% of the theoretical maximum value, i.e., 60 square centimeters.

[0182] Based on this contact area, the system dynamically adjusts the preset threshold for the number of support wheels that can be folded simultaneously. Normally, this threshold is set to 1, but due to the detection of a large area of ​​stable contact surface, the system temporarily raises the threshold to 2.

[0183] As the wheelchair approaches an obstacle, the system controls two support wheels (let's say the left front, right front, and right rear) to begin folding. During the folding process, the system monitors the wheelchair's stability parameters in real time, including tilt angle, angular velocity, and pressure distribution at each support point.

[0184] For example, when the left front support wheel begins to contact the obstacle surface, the system detects that the wheelchair tilts 1.5 degrees to the left and the pressure on the right support wheel increases by 5%. Based on this data, the system immediately adjusts the folding angles of the right front and right rear support wheels, reducing the folding angle of the right front support wheel from the planned 45 degrees to 40 degrees, while increasing the folding angle of the right rear support wheel from 30 degrees to 35 degrees to balance the wheelchair's center of gravity.

[0185] As the wheelchair traverses obstacles, the system continuously makes similar real-time adjustments. When it detects that the wheelchair's center of gravity is beginning to shift backward, the system gradually increases the support force of the rear support wheels while decreasing the folding angle of the front support wheels to ensure the wheelchair remains stable throughout the process.

[0186] Ultimately, the wheelchair successfully navigated the obstacle course, maintaining stability parameters within a safe range throughout, with a maximum tilt angle not exceeding 2 degrees. The time taken to navigate the obstacle course was reduced by approximately 20% compared to using a fixed folding strategy.

[0187] Through this dynamic adjustment and precise control, the technical solution of this application not only ensures the stability of electric wheelchairs in complex obstacle environments, but also significantly improves the efficiency of passage, providing users with a safer and more comfortable riding experience.

[0188] In some embodiments described above, 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 step is proposed to optimize the obstacle avoidance performance of the electric wheelchair by calculating and analyzing multiple alternative steering angles and selecting the steering angle that minimizes the fluctuation amplitude at the new contact position between the support wheel and the obstacle. However, accurately selecting the optimal steering angle to minimize the fluctuation amplitude while considering the stability and safety of the electric wheelchair remains a challenge. Furthermore, how to calculate and select the optimal steering angle in real time in a dynamic environment, and how to balance computational complexity and real-time performance, are also problems that need to be solved.

[0189] To address this, this application further proposes obtaining n alternative steering angles θ1, θ2, ..., θ n The maximum foldable angle of the support wheel is α_max, and the obstacle space model is O(x, y, z, t). Within the time interval [t0, t1], for each candidate 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 optimal steering angle θ_opt.

[0190] The technical solution proposed in this application selects the optimal steering angle by introducing an integral function that comprehensively considers multiple factors. This solution first obtains key parameters, including candidate steering angles, the maximum foldable angle of the support wheels, the obstacle space model, the electric wheelchair's velocity function, and its mass. These parameters comprehensively describe the dynamic characteristics of the electric wheelchair and the obstacle. Then, an integral function f(θ, t) = ∫[t0, t1] [A(θ, t) * F(θ, t) + λ * S(θ, t)]dt is designed. This function cleverly combines the contact area A(θ, t), contact force F(θ, t), and stability index S(θ, t), and evaluates the performance of the entire obstacle avoidance process through time integration.

[0191] This application introduces the following constraints: A(θ, t) > 0 ensures contact exists; F(θ, t) ≤ F_max limits the maximum contact force; and S(θ, t) ≥ S_min guarantees minimum stability. These constraints 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 considers not only the instantaneous state but also the dynamic changes throughout the obstacle avoidance process.

[0192] The specific implementation of this scheme can be further refined. First, the obstacle spatial model O(x, y, z, t) can be acquired and updated in real time using sensors such as LiDAR and depth cameras. This model includes not only the geometry of the obstacles but also their trajectories, thus enabling the prediction of changes in the obstacle's position over a future period.

[0193] The contact area A(θ, t) can be calculated by spatially projecting 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 wheelchair's mass, velocity, and contact area. The stability index S(θ, t) can be evaluated by calculating the position of the wheelchair's center of gravity relative to the supporting polygon; considering dynamic factors, parameters such as angular momentum can also be introduced.

[0194] The integral function can be calculated using numerical integration methods, such as Simpson's method or Runge-Kutta's method. To improve computational efficiency, an adaptive step size can be used, employing smaller step sizes at critical time points (such as the start and end of contact) and larger step sizes at other time points.

[0195] The choice of the stability weighting factor λ has a significant 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 obstacle avoidance, while a larger λ value can be selected on rugged terrain to ensure wheelchair stability.

[0196] Machine learning algorithms can be introduced to train models using historical data, quickly predicting the range of optimal steering angles, thereby narrowing the search space and improving computational efficiency.

[0197] The technical solution of this application minimizes fluctuation amplitude while ensuring safety by comprehensively considering contact area, contact force, and stability. The introduction of time integration and a dynamic model enables the method to adapt to complex dynamic environments. By setting constraints, the practicality and safety of the solution are ensured.

[0198] The innovation of this scheme is reflected in the following aspects: First, dynamic modeling considers the dynamic characteristics of obstacles and electric wheelchairs, improving the accuracy of obstacle avoidance decisions. Second, multi-objective optimization simultaneously considers contact area, contact force, and stability through an integral function, achieving a balance among multiple objectives. Third, the time integration method considers not only the instantaneous state but also the entire obstacle avoidance process, improving the robustness of the decision. Finally, the stability weighting factor λ allows for adjustment of the importance of stability according to actual needs.

[0199] 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 intelligent obstacle avoidance of electric wheelchairs.

[0200] As a preferred embodiment, this application can be implemented through the following steps:

[0201] First, environmental information is acquired through a sensor system to construct an obstacle spatial model O(x, y, z, t). For example, a 360-degree LiDAR is used to scan the surrounding environment at a scanning frequency of 10Hz, a resolution of 0.1 degrees, and a ranging range of 0.1m to 100m. Combined with data from a depth camera (1920x1080 resolution, 30fps), a 3D environment map is constructed using the SLAM algorithm, and the position and velocity information of dynamic obstacles are updated in real time using a Kalman filter.

[0202] Secondly, based on the wheelchair's current speed and acceleration, combined with the user's input control commands, the velocity function v(t) for the next 5 seconds is predicted. For example, assuming the current speed is 1.2 m / s and the acceleration is 0.2 m / s², then v(t) = 1.2 + 0.2t (t∈[0, 5]) can be predicted.

[0203] Then, n alternative steering angles are generated, typically n can be chosen as 36, meaning one alternative angle for every 10 degrees. For each alternative steering angle θ, the integral function f(θ, t) is calculated over the time interval [0, 5]. The specific calculation process is as follows:

[0204] The time interval [0, 5] is divided into 50 equally spaced time points.

[0205] At each time point, the predicted position of the wheelchair is calculated based on O(x, y, z, t).

[0206] 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.

[0207] Calculate the contact force F(θ, t). Assuming the wheelchair mass is 100 kg, then F(θ, t) = 100 * 9.8 * v(t)² / (2 * 0.002) N.

[0208] Calculate the stability index S(θ, t). We can define S(θ, t) = d / r, where d is the shortest distance from the centroid to the edge of the supporting polygon, and r is the radius of the circumcircle of the supporting polygon.

[0209] Choose an appropriate value for λ, for example, λ = 0.5.

[0210] Numerical integration was performed using Simpson's method to calculate the value of f(θ, t). Under the constraints 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), the θ that minimizes f(θ, t) was selected as the optimal steering angle θ_opt.

[0211] Finally, the calculated θ_opt is transmitted to the wheelchair's control system to execute the corresponding steering operation. Simultaneously, the folding angle of the support wheels is adjusted based on θ_opt to minimize the amplitude of swaying.

[0212] This method enables precise obstacle avoidance control in complex dynamic environments, significantly improving the safety and comfort of electric wheelchairs. Compared to traditional methods, this solution better handles moving obstacles, reduces collision risks, and minimizes fluctuations during riding while maintaining stability, thus providing users with a safer and more comfortable riding experience.

[0213] Secondly, referring to Figure 2 This application further discloses an electric wheelchair support wheel folding control device, which includes:

[0214] The status acquisition module 210 is used to acquire the driving status information and surrounding environment information of the electric wheelchair;

[0215] The obstacle detection module 220 is used to determine whether there are obstacles based on driving status information and surrounding environment information;

[0216] The steering obstacle avoidance judgment module 230 is used to determine whether obstacle avoidance can be achieved by steering when an obstacle exists.

[0217] The folding obstacle avoidance triggering module 240 is used to trigger the support wheel folding obstacle avoidance program based on obstacle distance, wheelchair speed and environmental constraints when it is determined that obstacle avoidance cannot be achieved by steering.

[0218] The folding parameter determination module 250 is used to determine the folding direction and folding angle of the support wheels based on the obstacle position, the wheelchair travel direction and the obstacle height;

[0219] 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.

[0220] The recovery control module 270 is used to control the support wheel to automatically return to its original support state after passing through an obstacle.

[0221] By acquiring information about the electric wheelchair's driving status and surrounding environment, the system determines whether obstacles exist. When steering is not an option to avoid obstacles, it triggers a support wheel folding obstacle avoidance program. Based on the obstacle's location, the wheelchair's driving direction, and the obstacle's height, the system determines the folding direction and angle of the support wheels, thereby achieving intelligent obstacle avoidance. This has the advantages of improving the obstacle avoidance capabilities and safety of electric wheelchairs, and enhancing their adaptability in complex environments.

[0222] Furthermore, in some preferred embodiments, the electric wheelchair support wheel folding control device proposed in this application can perform any of the steps in the above method.

[0223] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for controlling the folding of support wheels on an electric wheelchair, characterized in that, The method includes: Obtain information on the driving status and surrounding environment of the electric wheelchair; Determine whether there are obstacles based on the driving status information and surrounding environment information; When an obstacle is present, 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 procedure is triggered based on the obstacle distance, wheelchair speed, and environmental constraints. The folding direction and angle of the support wheels are determined based on the location of the obstacle, the direction of wheelchair travel, and the height of the obstacle. The support wheels are controlled to fold in a predetermined folding direction and angle to achieve obstacle avoidance; After passing through an obstacle, the control support wheel automatically returns to its original support state; The step of triggering the support wheel folding obstacle avoidance procedure based on obstacle distance, wheelchair speed, and environmental constraints when it is determined that obstacle avoidance cannot be achieved by steering includes: The relative distances and predicted contact times between multiple support wheels and one or more obstacles are calculated based on obstacle distance, wheelchair speed, and environmental constraints. Based on the relative distances and predicted contact times, the obstacle avoidance priority of each support wheel and obstacle is determined. According to the obstacle avoidance priority, the folding obstacle avoidance program of each support wheel is triggered in chronological order, wherein, while ensuring the stability of the wheelchair, the number of support wheels in the folded state at any given time is controlled to not exceed a preset threshold. The step of calculating the relative distances and predicted contact times between multiple support wheels and one or more obstacles based on obstacle distance, wheelchair speed, and environmental constraints, and determining the obstacle avoidance priority of each support wheel based on the relative distances and predicted contact times, includes: 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 movement trajectory and speed of the obstacle. Based on the current position and motion state of each support wheel, as well as the movement 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; Based on the calculated relative distance sequence, determine the minimum relative distance between each support wheel and each obstacle and the corresponding predicted contact time; Based on wheelchair speed and environmental constraints, and using minimum relative distance and predicted contact time, an obstacle avoidance priority score is assigned to each support wheel; Based on the obstacle avoidance priority score, all support wheels are sorted to obtain the final obstacle avoidance priority sequence.

2. 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 whether obstacle avoidance can be achieved by turning when an obstacle exists includes: Obtain the current speed, safe steering angular velocity, and distance to obstacles of the electric wheelchair; Based on the preset safety threshold and the current speed and safe steering angular velocity of the electric wheelchair, the minimum safe distance required to complete the turn is calculated; When the actual distance between the electric wheelchair and the obstacle is less than the minimum safe distance, it is determined that obstacle avoidance cannot be achieved by steering.

3. The method for controlling the folding of the support wheels of an electric wheelchair according to claim 2, characterized in that, The steps for determining that obstacle avoidance cannot be achieved by steering include: The driving speed of the electric wheelchair and the position information of the obstacle are obtained, and based on the driving speed and the position information of the obstacle, the predicted contact point between the electric wheelchair and the obstacle after making a preset turning amplitude is calculated. Based on the predicted contact point, the predicted fluctuation amplitude when the electric wheelchair comes into contact with the obstacle is calculated. When the predicted fluctuation amplitude is greater than a preset threshold, it is determined that the obstacle cannot be avoided by turning.

4. The method for controlling the folding of the support wheels of an electric wheelchair according to claim 1, characterized in that, The steps of determining the folding direction and folding angle of the support wheels based on the obstacle's location, the wheelchair's direction of travel, and the obstacle's height include: The system acquires the three-dimensional contour information of the obstacle and the current posture parameters of the electric wheelchair, and constructs a spatial model of the obstacle based on the three-dimensional contour information, which includes the obstacle height. Based on the current posture parameters of the electric wheelchair and the constructed obstacle space model, the possible contact positions and contact angles between the support wheels and the obstacles are calculated. Combined with the mechanical structural constraints of the support wheels, the minimum folding angle required to avoid collision is calculated. When the minimum folding angle is greater than the maximum foldable angle of the support wheel, the folding angle is set to the maximum foldable angle, and the wheelchair speed 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 based on the wheelchair's direction of travel and its relative position to the obstacle in order to achieve obstacle avoidance control.

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 setting the folding angle to the maximum foldable angle and adjusting the wheelchair speed to ensure safe passage when the minimum folding angle is greater than the maximum foldable angle of the support wheel includes: When the fluctuation amplitude calculated based on the obstacle space model and the maximum foldable angle of the support wheel exceeds the preset threshold, the optimal steering angle is selected by calculating and analyzing multiple alternative steering angles to minimize the fluctuation amplitude at the new contact position between the support wheel and the obstacle. The wheelchair is controlled to perform a steering operation with the optimized steering angle, and the support wheel folding angle is set to the maximum foldable angle. The maximum travel speed required to safely pass is calculated based on the optimized fluctuation range. Adjust the wheelchair speed to not exceed the maximum speed mentioned above to ensure safe passage over obstacles.

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 controlling the number of support wheels in the folded state at any given time to not exceed a preset threshold includes: The surface feature information of the obstacle is acquired, and when the obstacle is detected to have a plane that meets the preset conditions, the preset threshold for the number of support wheels that can be folded at the same time is dynamically adjusted based on the calculated contact area between the support wheel and the plane. Control the folded support wheels to make contact with the plane of the obstacle, and monitor the stability parameters of the wheelchair in real time; Based on real-time changes in stability parameters, the folding angle of each support wheel is dynamically adjusted to maximize passage efficiency while ensuring wheelchair stability.

7. The method for controlling the folding of the support wheels of an electric wheelchair according to claim 5, characterized in that, The step of selecting the optimal steering angle as the one that minimizes the fluctuation amplitude at the new contact position between the support wheel and the obstacle when the fluctuation amplitude calculated based on the obstacle space model and the maximum foldable angle of the support wheel exceeds a preset threshold includes: Obtain n alternative steering angles θ1, θ2, ..., θ n The maximum foldable angle of the support wheel is α_max, and the obstacle space model is O(x, y, z, t). Within the time interval [t0, t1], for each candidate turning angle θ, calculate the integral function f(θ, t) = ∫[t0, t1] [A(θ, t) * F(θ, t) + λ * S(θ, t)] dt, 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 weighting 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 optimal steering angle θ_opt.

8. An electric wheelchair support wheel folding control device for performing the method of any one of claims 1 to 7, characterized in that, The device includes: The status acquisition module is used to acquire the driving status information and surrounding environment information of the electric wheelchair; An obstacle detection module is used to determine whether there are obstacles based on the driving status information and the surrounding environment information. The steering obstacle avoidance judgment module is used to determine whether obstacle avoidance can be achieved by steering when an obstacle is present. The folding obstacle avoidance trigger module is used to trigger the support wheel folding obstacle avoidance program based on obstacle distance, wheelchair speed and environmental constraints when it is determined that obstacle avoidance cannot be achieved by steering. The folding parameter determination module is used to determine the folding direction and folding angle of the support wheels based on the obstacle position, the wheelchair's driving direction, and the obstacle height. The folding control module is used to control the support wheels to fold in a determined folding direction and angle to achieve obstacle avoidance. The recovery control module is used to automatically restore the support wheels to their original support state after passing over an obstacle.

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