An adaptive telescopic control system applied to a drive wheel

Abstract

The present application provides an adaptive telescopic control system for a drive wheel, which relates to the technical field of self-telescopic control. The system includes: a drive wheel connection module for connecting the drive wheel; a three-dimensional reconstruction module for obtaining a ground road condition model; a position and attitude prediction module for predicting the first adapted telescopic position of the drive wheel and the corresponding first adapted attitude and / or the second adapted telescopic position of the drive wheel and the corresponding second adapted attitude; a deformation optimization module for setting an adaptive shape memory structure; a suspension optimization module for setting an adaptive magnetorheological fluid structure; and an adaptive telescopic control module for performing adaptive telescopic control. By means of the present application, the technical problem that the drive wheel cannot automatically adjust the telescopic position and attitude according to the real-time ground road condition, resulting in insufficient landing stability and adaptability, can be solved, and the technical effect of improving the landing stability and safety under complex terrain conditions by optimizing the attitude and balance of the flying vehicle can be achieved.

Description

Technical Field

[0001] This application relates to the technical field of telescopic control, and particularly to an adaptive telescopic control system applied to drive wheels. Background Art

[0002] With the rapid development of flying car technology and the continuous expansion of its application fields, as an important part of flying cars, the performance and adaptability of drive wheels have become one of the key factors determining the overall performance of flying cars. During the frequent switching between the flight mode and the landing mode of flying cars, the drive wheels need to quickly and accurately adjust their telescopic states to adapt to different ground conditions and flight requirements. However, existing drive wheel telescopic control systems often face many challenges. On the one hand, with the increase in the design complexity of flying cars and the expansion of production scale, higher requirements are put forward for the accuracy, response speed, and stability of drive wheel telescopic control; on the other hand, the diversity and uncertainty of ground road conditions, such as uneven ground, changing slopes, and possible obstacles, have increased the difficulty of adaptive adjustment of drive wheels. Traditional drive wheel telescopic control methods cannot dynamically adjust in real time and accurately according to the actual state of flying cars and the external environment, resulting in low control efficiency and affecting the stability and safety of flying cars. Summary of the Invention

[0003] The purpose of this application is to provide an adaptive telescopic control system applied to drive wheels to solve the technical problem that the drive wheels cannot automatically adjust their telescopic positions and postures according to real-time ground road conditions, resulting in insufficient landing stability and adaptability.

[0004] In view of the above problems, this application provides an adaptive telescopic control system applied to drive wheels.

[0005] The present application provides an adaptive telescopic control system applied to a drive wheel. The system includes: a drive wheel connection module for connecting the drive wheel, where the drive wheel includes a speed reducer, a synchronous belt, a motor, and a tensioning bracket; a three-dimensional reconstruction module for scanning the environment through a radar device integrated in the flying vehicle, analyzing the ground road conditions in real time, and three-dimensionally reconstructing to obtain a ground road condition model; a position and attitude prediction module for predicting, in the ground road condition model, a first adapted drive wheel telescopic position and a corresponding first adapted attitude and / or a second adapted drive wheel telescopic position and a corresponding second adapted attitude required in the flight mode and the landing mode based on the historical telescopic control instances corresponding to the tensioning bracket; a deformation optimization module for performing deformation optimization on each drive wheel based on the first adapted drive wheel telescopic position and the corresponding first adapted attitude and / or the second adapted drive wheel telescopic position and the corresponding second adapted attitude, and setting an adaptive shape memory structure; a suspension optimization module for performing suspension optimization on each drive wheel based on the first adapted drive wheel telescopic position and the corresponding first adapted attitude and / or the second adapted drive wheel telescopic position and the corresponding second adapted attitude, and setting an adaptive magnetorheological fluid structure; and an adaptive telescopic control module for using a sensor array to monitor the flying vehicle in real time, obtaining real-time monitoring data, and optimizing with the goal of maximizing the balance of the flying vehicle through the adaptive shape memory structure and the adaptive magnetorheological fluid structure, and performing adaptive telescopic control.

[0006] The technical solution provided in the present application has at least the following technical effects or advantages:

[0007] For the above-mentioned adaptive telescopic control system applied to a drive wheel, the system scans the environment through the integrated radar device, analyzes and constructs an accurate three-dimensional ground road condition model in real time. Then, based on this road condition model and the historical telescopic control data of the tensioning bracket, it predicts the optimal telescopic positions and corresponding attitudes that the drive wheels need to reach in the flight mode and the landing mode. This prediction ability enables the flying vehicle to make preparations in advance to ensure the best stability and performance in different modes. Subsequently, deformation optimization is performed on each drive wheel, and an adaptive shape memory structure is introduced. This structure can automatically adjust its shape according to the preset adapted positions and attitudes to ensure good contact and support between the drive wheel and the ground. At the same time, suspension optimization is performed on the drive wheel, and an adaptive magnetorheological fluid structure is adopted. This structure can dynamically adjust the stiffness and damping of the suspension according to the real-time monitored state of the flying vehicle and the ground road conditions, thereby maintaining the balance and stability of the flying vehicle. During the whole process, the sensor array will monitor various parameters of the flying vehicle in real time, and use these data, combined with the adaptive shape memory structure and the adaptive magnetorheological fluid structure, to perform real-time adaptive telescopic control with the goal of maximizing the balance of the flying vehicle. In this way, the flying vehicle can achieve smooth and efficient flight and landing under different ground conditions.

[0008] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application, it can be implemented according to the content of the specification. In order to make the above and other purposes, features and advantages of this application more obvious and understandable, the specific embodiments of this application are specifically given below. It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of this application, nor is it used to limit the scope of this application. Other features of this application will become easily understandable through the following description. Brief Description of the Drawings

[0009] In order to more clearly illustrate the technical solutions in this application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings described below are only exemplary. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to the provided drawings.

[0010] Figure 1 It is a schematic structural diagram of an adaptive telescopic control system applied to a drive wheel of this application;

[0011] Figure 2 It is a schematic flow diagram of real-time analysis of ground road conditions of an adaptive telescopic control system applied to a drive wheel of this application.

[0012] Description of the Reference Numerals in the Drawings:

[0013] Drive wheel connection module 1, three-dimensional reconstruction module 2, position and attitude prediction module 3, deformation optimization module 4, suspension optimization module 5, adaptive telescopic control module 6. Detailed Embodiments

[0014] By providing an adaptive telescopic control system applied to a drive wheel, this application solves the technical problem that the drive wheel cannot automatically adjust the telescopic position and attitude according to the real-time ground road conditions, resulting in insufficient landing stability and adaptability, and achieves the technical effect of improving the landing stability and safety under complex terrain conditions by optimizing the attitude and balance of the flying vehicle.

[0015] Next, the technical solutions in this application will be clearly and completely described with reference to the drawings. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments of this application. It should be understood that this application is not limited by the exemplary embodiments described here. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of this application. Additionally, it should be noted that for the sake of convenience of description, only the parts related to this application are shown in the drawings rather than all of them.

[0016] Embodiment, please refer to the appendix Figure 1 , this application provides an adaptive telescopic control system applied to a drive wheel. The system specifically includes the following modules:

[0017] Drive wheel connection module 1, used to connect the drive wheel, and the drive wheel includes a reducer, a synchronous belt, a motor, and a tensioning bracket.

[0018] Specifically, in drive wheel connection module 1, the system terminal is connected to the drive wheel. The drive wheel is an important component in a flying car, responsible for converting the power of the motor into actual driving or flying power, and is composed of a reducer, a synchronous belt, a motor, and a tensioning bracket. Among them, the reducer can convert the high-speed and low-torque power generated by the motor into low-speed and high-torque power to meet the driving force requirements of the flying car under different working conditions. The reducer ensures that the drive wheel can output sufficient torque to push the flying car through the speed reduction and torque increase effect of the internal gears. The synchronous belt is a transmission element that ensures the smooth and accurate transmission of the power generated by the motor to the drive wheel. The motor is the power source of the entire drive wheel, which can generate rotational power and drive the drive wheel to rotate through the synchronous belt and the reducer. The performance of the motor directly affects the acceleration, endurance, and driving stability of the flying car. The tensioning bracket is an important component for supporting and fixing the drive wheel, ensuring that the drive wheel maintains a stable attitude and position during driving or flying. At the same time, the tensioning bracket is also responsible for adjusting the tension of the synchronous belt to ensure the transmission efficiency and service life.

[0019] 3D reconstruction module 2, used to scan the environment through the radar device integrated in the flying car, analyze the ground road conditions in real time, and 3D reconstruct to obtain a ground road condition model.

[0020] Specifically, in the 3D reconstruction module 2, the system terminal uses the radar device integrated in the flying car to scan the surrounding environment omnidirectionally. After the system terminal activates the radar device, the radar device emits laser beams and receives the signals reflected from the ground. To obtain more comprehensive environmental information, the radar device adopts a multi-beam scanning technique, simultaneously emitting multiple laser beams to cover a wider area. During flight, the radar device continuously collects data to ensure real-time performance. After collecting various terrain data, the system terminal performs noise filtering on the collected raw data to remove interference signals caused by environmental factors such as atmospheric interference and equipment errors. Then, by calculating the time difference between the emission and reception of the laser beams, the point cloud data of the target is generated. These data points represent the three-dimensional coordinate information on the ground. Subsequently, the system terminal performs registration processing on the point cloud data obtained from multiple scans to eliminate the inconsistencies caused by the movement of the flying car, ensuring the continuity and integrity of the point cloud data. Then, these registered point cloud data are divided into a series of small cubes, i.e., voxels. Each voxel can contain one or more point cloud points or be empty. After that, for each non-empty voxel, the statistical features of the point cloud points inside it are calculated, including the average value, standard deviation, maximum / minimum value, etc., as well as the geometric features such as the normal vector and curvature. Then, based on the statistical and geometric features within the voxel, the state of each voxel is determined, such as ground, obstacle, unknown area, etc. For example, if the heights of the point cloud points within the voxel are relatively consistent, and the normal vector direction is roughly perpendicular to the horizontal plane, and the point cloud density within the voxel is high without obvious discrete points or mutations, then this voxel can be marked as the ground. This indicates that this voxel represents a flat or gently sloping ground area. If the heights of the point cloud points within the voxel vary greatly, the normal vector directions are inconsistent, or the height difference between this voxel and the adjacent ground voxels exceeds a preset height threshold, and the point cloud density within this voxel is also relatively high, then this voxel is marked as an obstacle. This indicates that this voxel may contain objects above the ground such as trees, buildings, vehicles, etc. If the point cloud data within the voxel is very sparse, or due to environmental factors such as occlusion and noise, it is impossible to accurately determine its state, then this voxel is marked as an unknown area. Then, the system terminal connects adjacent voxels with similar states or features to form a three-dimensional grid structure. This grid structure represents the three-dimensional model of the ground, i.e., the ground road condition model, which contains information such as the geometric shape of the ground and the positions of obstacles. Then, smoothing processing, boundary optimization, etc. are performed on the constructed grid model to improve the accuracy and visual effect of the ground road condition model. Through this ground road condition model, the flying car can pre-evaluate the safety and feasibility of the ground when flying or preparing to land, providing crucial decision-making support for autonomous driving or manual operation.

[0021] Furthermore, as Figure 2As shown, this application provides a method of scanning the environment through a radar device integrated in a flying car and analyzing the ground road conditions in real time, including:

[0022] Identifying the type of the current driving ground through the radar device integrated in the flying car; calling a corresponding shape memory deformation mode according to the type of the current driving ground, wherein the shape memory deformation mode is a programmable drive wheel structure designed based on carbon fiber composite materials and combined with shape memory alloys; activating the shape memory alloy based on the called shape memory deformation mode through the electric control signal corresponding to the adaptive shape memory structure, and instantaneously changing the wheel width and tread structure in combination with the wind resistance coefficient.

[0023] In a preferred embodiment, the system terminal uses the integrated radar device to judge whether the current voxel represents the ground by the same method as described above. If the judgment result is the ground, the system terminal calls the camera array of the flying car to identify the specific type of the ground, such as highway, mountain road, sand land, etc. Subsequently, according to the identified ground type, the flying car will intelligently select and call a preset shape memory deformation mode. These deformation modes correspond one-to-one with the ground types and are a programmable drive wheel structure designed based on carbon fiber composite material and shape memory alloy technology, enabling the wheel hub and tread to be adjusted as needed. When the corresponding shape memory deformation mode is activated, the system terminal stimulates the shape memory alloy through an electric control signal, and these alloys will deform according to the preset memory shape. At the same time, in combination with the influence of different ground types on the wind resistance coefficient, the flying car will also adjust the width of the wheel hub and the structure of the tread to achieve the best driving performance and stability. In this way, no matter what kind of ground conditions, the flying car can quickly adapt and optimize its tire configuration to ensure safe and efficient driving.

[0024] A position and attitude prediction module 3, configured to predict, in the ground road condition model, a first adapted drive wheel telescopic position and a corresponding first adapted attitude and / or a second adapted drive wheel telescopic position and a corresponding second adapted attitude required in a flight mode and a landing mode based on the historical telescopic control instances corresponding to the tension bracket.

[0025] Specifically, in the position and attitude prediction module 3, the system terminal collects historical data on the control of the telescopic position of the drive wheels by the tensioning bracket of a flying car under different ground road conditions in the past, that is, historical telescopic control instances. This historical telescopic control instance includes, but is not limited to, the switching moments between flight mode and landing mode, the corresponding ground types, the telescopic positions of the drive wheels, attitude adjustment parameters, etc. Then, the collected historical data is cleaned to remove noise and outliers, and classified and labeled to clarify the labeled telescopic positions and labeled attitudes of the first-adapted and / or second-adapted drive wheels under specific flight modes and landing modes. Subsequently, using the ground road condition model as the basic representation of the environment, information such as ground road conditions and obstacle positions in the historical telescopic control instance is mapped into a three-dimensional space. Based on the three-dimensional reconstruction, the system terminal constructs a deep learning model to predict the adaptation of the drive wheels. This model can receive the environmental representation of the ground road condition model as input and output the telescopic positions and attitudes of the drive wheels. The system terminal initializes the weights and biases of the deep learning model using random numbers, then corresponds the environmental representation of the ground road condition model with the labeled telescopic positions and labeled attitudes of the first-adapted and / or second-adapted drive wheels, and divides the corresponding results into a training set, a validation set, and a test set. After that, the training set is input into the deep learning model, and the predicted telescopic positions and predicted attitudes of the first-adapted and / or second-adapted drive wheels are calculated through the deep learning model. Then, the mean squared error is used to calculate the difference between the prediction result and the true label. Then, according to the gradient of the loss function, the parameters of the deep learning model are updated using stochastic gradient descent (SGD) to reduce the loss. The process of forward propagation, loss calculation, and backpropagation is repeated until the loss value no longer decreases significantly. During the training process, the system terminal regularly evaluates the performance of the deep learning model using the validation set. If the performance on the validation set does not improve significantly in several consecutive iterations, the training is stopped early to prevent overfitting. After the training is completed, the system terminal uses the test set to evaluate the final performance of the deep learning model and calculates evaluation metrics, including accuracy, recall, etc. If the evaluation metrics meet the expected expectations, this deep learning model is output and connected to the ground road condition model. Otherwise, the model structure, such as the number of hidden layers and the number of neurons, is adjusted and retrained. After obtaining the deep learning model, the system terminal represents the environment by inputting the environmental information scanned by the radar device into the ground road condition model, and then transmits the environmental representation result to the deep learning model for prediction of telescopic positions and attitudes. Through the knowledge learned, the deep learning model can predict the required telescopic position of the first-adapted drive wheel and the corresponding first-adapted attitude and / or the telescopic position of the second-adapted drive wheel and the corresponding second-adapted attitude under the current flight mode and landing mode. Through this prediction based on historical data and real-time road conditions, the flying car can dynamically adjust the configuration of its drive wheels to ensure the best driving performance and stability in various complex environments.

[0026] The deformation optimization module 4 is used to perform deformation optimization on each driving wheel based on the telescopic position of the first adaptive driving wheel and the corresponding first adaptive attitude and / or the telescopic position of the second adaptive driving wheel and the corresponding second adaptive attitude, and set an adaptive shape memory structure.

[0027] Specifically, in the deformation optimization module 4, after obtaining the telescopic position of the first adaptive driving wheel and the corresponding first adaptive attitude and / or the telescopic position of the second adaptive driving wheel and the corresponding second adaptive attitude, the system terminal sends control instructions to each driving wheel to make them deform according to the specified position and attitude. This deformation process is achieved through an adaptive shape memory structure, which can memorize and restore to a specific shape without the need for additional mechanical or hydraulic systems to drive. When the driving wheel receives the control instruction, the shape memory material inside it will be heated or cooled according to a preset program, thereby causing a change in shape, enabling the driving wheel to accurately reach the required position and attitude. Through such a deformation optimization process, the driving wheels of the flying car can better adapt to different terrains and flight conditions, improve the stability and safety of landing, and at the same time optimize the aerodynamic performance during flight, reducing energy consumption and noise. The application of this adaptive shape memory structure not only improves the intelligence level of the flying car but also provides more possibilities and flexibility for its application in various complex environments.

[0028] The suspension optimization module 5 is used to perform suspension optimization on each driving wheel based on the telescopic position of the first adaptive driving wheel and the corresponding first adaptive attitude and / or the telescopic position of the second adaptive driving wheel and the corresponding second adaptive attitude, and set an adaptive magnetorheological fluid structure;

[0029] Specifically, in the suspension optimization module 5, the system terminal evaluates and adjusts the performance requirements of the suspension structure according to the obtained telescopic position of the first adaptive driving wheel and the corresponding first adaptive attitude and / or the telescopic position of the second adaptive driving wheel and the corresponding second adaptive attitude. The main function of the suspension structure is to connect the vehicle body and the wheels and buffer the impacts and vibrations during driving to ensure the smoothness of vehicle driving. To achieve the optimization of the suspension structure, the system terminal sets an adaptive magnetorheological fluid structure. Magnetorheological fluid is an intelligent material whose rheological properties, such as viscosity and yield stress, will change significantly under the action of a magnetic field. In the suspension structure, the magnetorheological fluid is encapsulated in a specific device, and by controlling the magnetic field strength in the device, the rheological properties of the magnetorheological fluid can be adjusted in real time, thereby changing the damping and stiffness of the suspension structure. When the telescopic position and attitude of the driving wheel change, the suspension structure can quickly respond and adjust its performance to maintain the stability and comfort of the vehicle body, enabling the suspension structure to accurately adapt to the current driving conditions and better cope with various complex environments to ensure driving stability and safety.

[0030] The adaptive telescopic control module 6 is used to monitor the flying vehicle in real time by using the sensor array, obtain real-time monitoring data, and optimize the flying vehicle with the goal of maximizing the balance degree through the adaptive shape memory structure and the adaptive magnetorheological fluid structure, and perform adaptive telescopic control.

[0031] Specifically, in the adaptive telescopic control module 6, the system terminal uses the sensor array to monitor the flying vehicle comprehensively and in real time, collects various data including position, attitude, speed, acceleration, etc. These data are important bases for the flying vehicle to perform dynamic adjustment and optimization. Subsequently, based on these real-time monitoring data, the system terminal obtains the factors and aerodynamic drag coefficients that affect the balance degree of the flying vehicle, and adjusts the adaptive shape memory structure and the adaptive magnetorheological fluid structure according to these factors and aerodynamic drag coefficients to cope with different road conditions and flight states. This monitoring and adjustment process will continue to ensure that the balance degree and stability of the flying vehicle are maximized.

[0032] Furthermore, this application provides for using the sensor array to monitor the flying vehicle in real time and obtain real-time monitoring data, including:

[0033] Based on the real-time monitoring data, collect the real-time load distribution data set; through the real-time load distribution data set, analyze the influence of the load distribution change on the balance degree of the flying vehicle to obtain the load distribution balance influence factor.

[0034] In an alternative embodiment, after obtaining the real-time monitoring data of the flying car through the sensor array, the system terminal extracts a real-time load distribution data set from the real-time monitoring data. This real-time load distribution data set records the load conditions borne by each part of the flying car at different flight stages or under different operating conditions. The load distribution refers to the distribution of forces such as gravity, lift, and drag borne by each point or component on the flying car. Subsequently, the system terminal visualizes the real-time load distribution data set using a heat map to intuitively understand the change trend and distribution of the load. Then, analyze the change of the load distribution over time, identify the laws and characteristics of the load distribution change. For example, through the heat map, it can be observed that the color of the contact area between the driving wheel and the ground gradually deepens, indicating that as the power increases, the driving force borne by the driving wheel also increases. When the stable speed is reached, the color change tends to be stable, indicating that the load distribution reaches a relatively stable state. Analyze the load distribution differences between different parts to determine which parts bear larger loads and which parts bear smaller loads. For example, if the heat map shows that the color of the driving wheel is relatively uniform, it indicates that the driving force is evenly shared. However, if there is wheel slip or uneven wear of the driving wheel, the color of the heat map will be darker, indicating that it bears a greater load. Analyze the load transfer phenomenon during flight, such as the load changes in key stages such as takeoff, turning, and landing. For example, during turning, due to the action of centrifugal force, the flying car tends to deviate outward. At this time, if the color of the outer driving wheel in the heat map is darker than that of the inner driving wheel, it means that the outer driving wheel needs to bear greater lateral force and centripetal force loads to maintain the stable turning of the flying car. Then, according to the characteristics of the flying car, define balance indicators, including attitude angle, angular velocity, acceleration, etc., to quantitatively evaluate the balance state of the flying car. Then, through statistical methods, understand the relationship between the load distribution change and the balance indicators, determine the key load distribution factors affecting the balance of the flying car, and define these factors as load distribution balance impact factors. These factors can be used to evaluate the impact degree of the current load distribution state on the balance of the flying car.

[0035] Based on the load distribution balance impact factors, configure the optimal suspension hardness and damping parameters; based on the optimal suspension hardness and damping parameters, activate the magnetorheological fluid material through the electric control signal corresponding to the adaptive magnetorheological fluid structure, and combine with the wind resistance coefficient to instantaneously adjust the suspension damping and hardness of the magnetorheological fluid.

[0036] In an alternative embodiment, optimizing the suspension performance of a flying car based on the load distribution balance influence factor is a comprehensive dynamic adjustment process. The system terminal first uses the obtained load distribution balance influence factor, combines professional knowledge and historical experience, and configures the optimal suspension hardness and damping parameters. The suspension hardness determines the ability of the suspension structure to resist the up and down movement of the vehicle, while the damping is used to control the attenuation speed of this movement. By adjusting these two parameters, the stability, comfort, and controllability of the flying car can be significantly improved. After determining the optimal suspension hardness and damping parameters, the system terminal sends corresponding electronic control signals to the adaptive magnetorheological fluid structure to activate and control the magnetic field therein, thereby instantly changing the suspension damping and hardness of the magnetorheological fluid. During the adjustment process, the system terminal also considers the influence of the wind resistance coefficient. The wind resistance coefficient is a quantitative index of the air resistance suffered by the flying car during driving, which affects the driving stability and energy consumption of the vehicle. Therefore, when adjusting the suspension parameters, the system terminal comprehensively considers the wind resistance coefficient to ensure that the vehicle can maintain the best suspension performance under different wind speeds and wind directions. For example, under strong wind conditions, the flying car will be subject to a large wind resistance. At this time, the system terminal increases the damping of the suspension according to the current wind resistance coefficient to reduce the shaking of the vehicle caused by the wind and improve the stability. Under relatively stable wind conditions, the system terminal will reduce the damping to improve the riding comfort.

[0037] Furthermore, the present application provides optimization aiming at maximizing the balance degree of the flying car and performs adaptive telescopic control, including:

[0038] After sending the electronic control signal corresponding to the adaptive shape memory structure and / or the electronic control signal corresponding to the adaptive magnetorheological fluid structure, update the center of gravity coordinates of the flying car; continuously monitor the current vehicle attitude, and calculate the balance degree in combination with the updated center of gravity coordinates.

[0039] In an alternative embodiment, after the system terminal sends an electronic control signal for the adaptive shape memory structure and / or the adaptive magnetorheological fluid structure, the adaptive shape memory structure and the adaptive magnetorheological fluid structure will undergo corresponding physical changes according to the signal to adjust the suspension state, body shape, or other balance-related parameters of the flying car. After this process is completed, the system terminal will immediately update the center of gravity coordinates of the flying car to reflect the center of gravity position of the vehicle in the current state. Subsequently, use a sensor array to collect the attitude angle data of the vehicle in real time, including key attitude information such as tilt angle, pitch angle, yaw angle, etc. Then. Use the obtained attitude angle data and the updated center of gravity position to construct a dynamic equation, and solve this equation to obtain the balance degree of the flying car. The larger the balance degree, the more stable the flying car; on the contrary, it means that the flying car is more likely to become unstable.

[0040] Further, the present application provides landing prediction, including:

[0041] When the flying vehicle is in the flight mode, predict the landing point and predict the ground conditions at the landing point; based on the ground conditions at the landing point, perform seamless switching optimization from the flight mode to the landing mode, and send the corresponding electric control signals of the adaptive shape memory structure and / or the corresponding electric control signals of the adaptive magnetorheological fluid structure.

[0042] In an optional implementation manner, when the flying vehicle is in the flight mode and ready to land, the system terminal will call the constructed ground road condition model, combine the navigation information to predict the possible landing points. In this process, the ground road condition model is used to judge whether the terrain ahead is a relatively flat ground and whether there are obstacles, and combine the navigation information to predict the points where landing can be carried out ahead. Subsequently, use radar equipment to collect the ground conditions at the landing point and predict key factors such as the hardness, friction coefficient, and slope of the ground. Then, based on the ground conditions at the landing point, the system terminal adjusts the flight attitude, including the pitch angle, roll angle, and yaw angle, to ensure that the flight trajectory can be stably controlled when approaching the landing point, and gradually reduces the flight speed to reduce the impact force during landing. Then, adjust the motor output power of the flying vehicle so that the flying vehicle can gradually decelerate and hover stably when approaching the landing point. At the same time, adjust the lift system of the flying vehicle, such as the thrust of the rotor or jet engine, to maintain a stable hovering state. Then, according to the predicted ground conditions, calculate the optimal wheel width, tread structure, and / or suspension parameters by the same method as described above, and send the corresponding electric control signals to the adaptive shape memory structure and / or the adaptive magnetorheological fluid structure to adjust their physical properties to adapt to the ground conditions at the landing point.

[0043] Further, the present application provides feedback optimization adjustment from the flight mode to the landing mode, including:

[0044] After landing, collect landing impact data; through the radar equipment integrated in the flying vehicle, collect ground vibration feedback data; based on the landing impact data and the ground vibration feedback data, perform feedback optimization adjustment from the flight mode to the landing mode.

[0045] In an alternative embodiment, after landing, the flying car immediately starts collecting landing impact data. These data mainly reflect the interaction between the flying car and the ground at the moment of landing, including key parameters such as the magnitude, direction, and duration of the impact force. These data are crucial for evaluating the smoothness of the landing process, the bearing capacity of the flying car's structure, and the comfort of passengers. At the same time, the flying car's integrated radar device is also used to collect ground vibration feedback data. The radar device can accurately detect the minute vibrations generated on the ground due to the landing impact and convert these vibration signals into analyzable data to generate ground vibration feedback data. These data provide information about the ground properties, landing effects, and potential hazards. Subsequently, the system terminal analyzes the collected landing impact data to determine the distribution of the landing impact force, checks for the presence of abnormal or excessive impact forces, and evaluates the impact of the impact force on the flying car's structure, including whether it causes structural damage or affects passenger comfort. Then, based on the collected ground vibration feedback data, it analyzes the characteristics of the ground vibrations, such as vibration frequency and amplitude, determines the impact of the ground conditions on the landing process, evaluates the impact of the ground vibrations on the stability of the flying car, and checks for potential safety hazards. After that, the system terminal comprehensively evaluates the landing impact data and the ground vibration feedback data to determine whether the switching process from the flight mode to the landing mode is smooth and safe. It identifies the existing problems and deficiencies to provide a basis for subsequent optimization and adjustment. Then, based on the data analysis structure, the system terminal combines the landing impact data and the ground vibration feedback data to adjust the suspension parameters of the flying car to improve the shock absorption effect during landing and optimize the response speed and stability of the suspension structure to ensure good landing performance under different ground conditions. It also adjusts the power output of the flying car to control the landing speed and reduce the impact force, and optimizes the response speed and adjustment range of the power system to ensure precise control of the flying car's motion state during landing. Through these adjustments, the flying car can continuously improve its landing performance to ensure a smoother, safer, and more efficient landing process in the future.

[0046] Furthermore, the present application provides takeoff prediction, including:

[0047] When the flying car is in the landing mode, predict the takeoff point and predict the flight conditions at the takeoff point; based on the flight conditions at the takeoff point, perform seamless switching optimization from the landing mode to the flight mode, and send out the electric control signals corresponding to the adaptive shape memory structure and / or the electric control signals corresponding to the adaptive magnetorheological fluid structure.

[0048] In an optional embodiment, when the flying car is in landing mode and needs to take off, the system terminal will immediately enter the take-off preparation stage. The core task of this stage is to predict the take-off point and predict in detail the flight conditions that the take-off point may face. The system terminal predicts the flight conditions of the take-off point by combining the ground road condition model, radar equipment and navigation information in the same way as the aforementioned landing point prediction. Then, the same seamless switching optimization method as mentioned above is used to optimize the flying car's driving attitude, output power, and lift system. In addition, the system terminal also performs obstacle avoidance path planning to ensure a smooth take-off. Finally, based on the seamless switching optimization results, the system terminal sends an electrical control signal corresponding to the adaptive shape memory structure and / or an electrical control signal corresponding to the adaptive magnetorheological fluid structure.

[0049] Furthermore, the present application provides a method for optimizing the seamless switching from the landing mode to the flight mode based on the flight conditions at the take-off point, including:

[0050] When the flying car is in the landing mode, the radar device integrated in the flying car is used to identify obstacles on the planned path and issue an emergency obstacle avoidance warning; the emergency obstacle avoidance warning is immediately responded to and an obstacle avoidance path is planned; after the obstacle avoidance action is completed, the driving state defined by the planned path is restored.

[0051] In an optional embodiment, when the flying car is in landing mode and driving along the planned path, the system terminal collects terrain information on the planned path ahead through the integrated radar device, and combines it with the ground road condition model to determine whether there is an obstacle ahead. When the ground road condition model feedbacks that there is an obstacle ahead, the system terminal will immediately issue an emergency obstacle avoidance warning to remind the driver or the automatic driving system to pay attention to obstacle avoidance. In the face of the emergency obstacle avoidance warning, the system terminal will quickly activate the emergency response mechanism. The core of this mechanism is to plan an obstacle avoidance path based on the navigation system, that is, to plan an alternative path for the flying car that can avoid obstacles while ensuring safety, so as to ensure that the flying car can deviate from the original planned path as little as possible while avoiding obstacles and maintain driving efficiency. After the obstacle avoidance action is completed, that is, after the flying car successfully bypasses the obstacle and regains a safe driving space, the system terminal will automatically or according to the driver's instructions to restore the driving state limited by the planned path. This means that the flying car will continue to drive along the original planned path again until it reaches the predetermined destination.

[0052] In summary, the adaptive telescopic control system applied to the driving wheel provided by the present application has the following technical effects:

[0053] In this application, the driving wheel is closely connected to the flying car through the driving wheel connection module 1 to ensure smooth power transmission. At the same time, the radar equipment integrated in the flying car is used to scan and analyze the surrounding environment in real time, constructing an accurate three-dimensional ground road condition model, which provides strong support for subsequent path planning and driving wheel adjustment. During driving, according to historical telescopic control instances and the current ground road condition model, the most suitable telescopic position and attitude required by the driving wheel in flight and landing modes are predicted. Through the deformation optimization module 4, the adaptive shape memory structure is adjusted immediately, enabling the driving wheel to deform flexibly to best meet different road conditions and driving requirements. In addition, the suspension optimization module 5 adjusts the suspension structure of the driving wheel according to the predicted attitude and road conditions, adopting an adaptive magnetorheological fluid structure to improve the driving stability and comfort with optimal suspension hardness and damping parameters. To ensure the maximization of the balance of the flying car, a sensor array is used for real-time monitoring to collect and analyze various parameter data. Based on these data, the adaptive shape memory structure and the adaptive magnetorheological fluid structure are precisely adjusted to achieve the adaptive telescopic control of the driving wheel. This intelligent adjustment not only improves the driving performance of the flying car but also enhances its safety and stability. These technical effects jointly solve the technical problem that the driving wheel cannot automatically adjust the telescopic position and attitude according to real-time ground road conditions, resulting in insufficient landing stability and adaptability, and achieve the technical effect of improving the landing stability and safety under complex terrain conditions by optimizing the attitude and balance of the flying car.

[0054] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0055] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is also intended to include these changes and modifications.

Claims

1. An adaptive telescopic control system applied to a driving wheel, characterized in that: include: A driving wheel connection module, used to connect the driving wheel, wherein the driving wheel includes a reducer, a synchronous belt, a motor, and a tensioning bracket; The 3D reconstruction module is used to scan the environment through the radar equipment integrated in the flying car, analyze the ground road conditions in real time and reconstruct the ground road condition model in 3D; A position and attitude prediction module, for predicting the first adaptive drive wheel telescopic position and the corresponding first adaptive attitude and / or the second adaptive drive wheel telescopic position and the corresponding second adaptive attitude required in the flight mode and the landing mode in the ground road condition model based on the historical telescopic control instance corresponding to the tensioning bracket; A deformation optimization module, used to optimize the deformation of each driving wheel and set an adaptive shape memory structure based on the first adaptive driving wheel telescopic position and the corresponding first adaptive posture and / or the second adaptive driving wheel telescopic position and the corresponding second adaptive posture; A suspension optimization module, for optimizing the suspension of each drive wheel and setting an adaptive magnetorheological fluid structure based on the first adapted drive wheel telescopic position and the corresponding first adapted posture and / or the second adapted drive wheel telescopic position and the corresponding second adapted posture; An adaptive telescopic control module is used to monitor the flying car in real time using a sensor array, obtain real-time monitoring data, and optimize the flying car with the goal of maximizing its balance through the adaptive shape memory structure and the adaptive magnetorheological fluid structure, and perform adaptive telescopic control; The method of scanning the environment and analyzing the ground road conditions in real time by using the radar equipment integrated in the flying car also includes: Identify the current driving surface type through the radar equipment integrated into the flying car; According to the current driving ground type, the corresponding shape memory deformation mode is called, wherein the shape memory deformation mode is based on carbon fiber composite materials and combined with shape memory alloys to design a programmable driving wheel structure; Based on the called shape memory deformation mode, the shape memory alloy is activated by the electric control signal corresponding to the adaptive shape memory structure, and the wheel hub width and tread structure are instantly changed in combination with the drag coefficient; The method of using a sensor array to monitor the flying car in real time and obtain real-time monitoring data includes: Based on the real-time monitoring data, collecting a real-time load distribution data set; By using the real-time load distribution data set, the influence of the load distribution change on the balance of the flying car is analyzed to obtain the load distribution balance influencing factor; Based on the load distribution balance influencing factors, configuring the optimal suspension hardness and damping parameters; Based on the optimal suspension hardness and damping parameters, the magnetorheological fluid material is activated by the electric control signal corresponding to the adaptive magnetorheological fluid structure, and the suspension damping and hardness of the magnetorheological fluid are adjusted in real time in combination with the wind resistance coefficient.

2. The adaptive telescopic control system for a driving wheel according to claim 1, characterized in that: The flying car is optimized with the goal of maximizing its balance and performing adaptive telescopic control, including: After sending an electrical control signal corresponding to the adaptive shape memory structure and / or an electrical control signal corresponding to the adaptive magnetorheological fluid structure, updating the coordinates of the center of gravity of the flying car; Continuously monitor the current vehicle posture and calculate the balance based on the updated center of gravity coordinates.

3. The adaptive telescopic control system for driving wheels according to claim 2, characterized in that: When the flying car is in flight mode, predicting the landing point and predicting the ground conditions at the landing point; Based on the ground conditions at the landing point, seamless switching optimization from the flight mode to the landing mode is performed, and an electrical control signal corresponding to the adaptive shape memory structure and / or an electrical control signal corresponding to the adaptive magnetorheological fluid structure is issued.

4. The adaptive telescopic control system for driving wheels according to claim 3, characterized in that: Also includes: After landing, landing impact data is collected; Collect ground vibration feedback data through the radar equipment integrated into the flying car; Based on the landing impact data and the ground vibration feedback data, feedback optimization adjustment from the flight mode to the landing mode is performed.

5. The adaptive telescopic control system for driving wheels according to claim 2, characterized in that: When the flying car is in a landing mode, pre-determine the take-off point and predict the flight conditions of the take-off point; Based on the flight conditions at the take-off point, seamless switching optimization from the landing mode to the flight mode is performed, and an electrical control signal corresponding to the adaptive shape memory structure and / or an electrical control signal corresponding to the adaptive magnetorheological fluid structure is issued.

6. The adaptive telescopic control system for driving wheels according to claim 5, characterized in that: Optimize seamless transition from landing mode to flight mode based on flight conditions at the takeoff point, including: When the flying car is in the landing mode, the flying car uses a radar device integrated in the flying car to identify obstacles on the planned path and issue an emergency obstacle avoidance warning; Respond immediately to the emergency obstacle avoidance warning and perform obstacle avoidance path planning; After the obstacle avoidance action is completed, the driving state defined by the planned path is restored.

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