Foot end ground contact information detection method, device and equipment based on wheel-legged robot and medium

Abstract

The invention provides a foot end ground contact information detection method, device and equipment based on a wheel-legged robot and a medium, and relates to the technical field of wheel-legged robots. The method comprises the steps that tire strain data of the wheel-legged robot are acquired through a signal acquisition system arranged on the wheel-legged robot; acquiring acceleration data of a ground contact point when tires of the wheel-foot robot are in a ground contact state; according to the established mapping relation between the input vector and the output vector, the contact force data of the wheel-legged robot and the spatial position of a tire contact point are obtained through the tire strain data and the acceleration data; and based on the contact force data and the spatial position, ground contact information of the position where the wheel-foot robot is located is analyzed. According to the invention, the ground contact information of the wheel-legged robot can be accurately detected.

Description

Technical Field

[0001] The present application relates to the technical field of wheel-legged robots, and particularly to a method, device, equipment and medium for detecting foot-end touchdown information based on a wheel-legged robot. Background Art

[0002] A wheel-legged robot is a robot that combines the movement characteristics of wheels and legs and has high terrain adaptability. It is usually composed of two movement modes, wheels and legs, and can move freely in complex terrains and different environments. Therefore, wheel-legged robots can cope with various complex environments when performing tasks and are widely used in exploration, rescue, military, and special operation fields.

[0003] The detection of the foot-end touchdown information of a wheel-legged robot is crucial for its stability and adaptability. Since a wheel-legged robot needs to perform tasks on complex and variable terrains, the accurate perception of the foot-end touchdown state can timely feedback the ground characteristics and help the robot judge factors such as the hardness, inclination, and obstacles of the current terrain. This information plays an important role in the robot's switching of movement modes, adjustment of movement strategies, and optimization of control systems. Especially in complex terrains or extreme environments, the detection of foot-end touchdown information can effectively prevent the robot from becoming unstable or unable to pass obstacles, and improve its adaptability and task execution ability in dynamic environments. Therefore, accurate touchdown information detection is not only the basis for the efficient and safe operation of a wheel-legged robot but also an indispensable part of its intelligent and automated control system. Summary of the Invention

[0004] The present application provides a method, device, equipment and medium for detecting foot-end touchdown information based on a wheel-legged robot, which can accurately detect the touchdown information of the wheel-legged robot.

[0005] In a first aspect of the present application, a method for detecting foot-end touchdown information based on a wheel-legged robot is provided. The method is applied to a server and includes:

[0006] Obtaining tire strain data of the wheel-foot robot through a signal acquisition system arranged on the wheel-foot robot;

[0007] Obtaining acceleration data of the touchdown point when the tire of the wheel-foot robot is in a touchdown state;

[0008] According to the established mapping relationship between the input vector and the output vector, obtaining contact force data of the wheel-legged robot and the spatial position of the tire touchdown point through the tire strain data and the acceleration data;

[0009] Analyzing the touchdown information of the position where the wheel-foot robot is located based on the contact force data and the spatial position.

[0010] Based on the above technical solutions, preferably, analyzing the ground contact information of the wheel-legged robot based on the contact force data and the spatial position specifically includes:

[0011] Based on the contact force data, calculate the contact area of the contact area of the wheel-legged robot, where the contact area is the area where the tire of the wheel-legged robot contacts the ground;

[0012] According to the contact force data and the contact area, calculate the change rate of the contact force in the contact area;

[0013] Based on the change of the spatial position, calculate the ground deformation;

[0014] According to the change rate and the ground deformation data, calculate the ground hardness;

[0015] Judge the magnitude relationship between the ground hardness and different preset thresholds to determine the ground hardness category of the contact area.

[0016] Based on the above technical solutions, preferably, calculating the ground hardness according to the change rate and the ground deformation specifically calculates through the following formula:

[0017]

[0018] Among them, H is the ground hardness, F z is the normal reaction force included in the contact force data, Δz is the ground deformation, A is the contact area, and ΔF z is the change amount of the contact force.

[0019] Based on the above technical solutions, preferably, before obtaining the contact force data of the wheel-leg robot and the spatial position of the tire contact point through the mapping relationship between the established input vector and output vector and using the tire strain data and the acceleration data, the method further includes:

[0020] Construct the input vector, where the input vector is composed of the acceleration data and multiple groups of the tire strain data;

[0021] Construct an output vector, where the output vector includes the contact force data and the spatial position;

[0022] Train the Gaussian process regression model with training data, where the training data includes training acceleration data and multiple groups of training tire strain data as inputs, and training contact force data and training spatial position as outputs;

[0023] Train the Gaussian process regression model with the training data, and use the Gaussian process regression model to model the mapping relationship between the input vector and the output vector, where the mapping relationship is expressed as follows:

[0024] Y j = k * (K - σ n I) -1 y j

[0025] where Y j is the output vector, Y j = [F x , F y , F z T , y j is the input vector, σ n is the standard deviation, the input vector is expressed as x = [s 1 , s 2 T , k * is a variable independent of the input quantity, and K is the covariance matrix.

[0026] Based on the above technical solutions, preferably, according to the established mapping relationship between the input vector and the output vector, the contact force data of the wheel-legged robot and the spatial position of the tire contact point are obtained through the tire strain data and the acceleration data, specifically including:

[0027] Input the tire strain data and the acceleration data into the trained Gaussian process regression model, and obtain the contact force data and the spatial position of the tire contact point output by the trained Gaussian process regression model, where the contact force data includes the shear force in the x-axis direction, the shear force in the y-axis direction, and the normal reaction force, and the spatial position includes the spatial coordinates of the contact point.

[0028] Based on the above technical solutions, preferably, the signal acquisition system includes multiple sensor units, where:

[0029] Multiple sensor units are embedded in the solid tire of the wheel-legged robot, and are annularly and equally angularly distributed inside the tire with the tire rotation axis as the center. Each sensor unit includes a polytetrafluoroethylene hose and a resistive strain gauge, and the resistive strain gauge includes a resin cover sheet, a metal foil, and a resin substrate;

[0030] The resistive strain gauge is arranged in the central area of the polytetrafluoroethylene hose, and a two-component acrylate adhesive is injected into the polytetrafluoroethylene hose to cure and encapsulate the resistive strain gauge; ​​

[0031] The metal foil is disposed directly above the resin substrate, and the resin covering sheet covers directly above the metal foil.

[0032] Based on the above technical solution, preferably, the sensor unit further includes a strain gauge wire, and the server and the sensor unit are communicatively connected based on the strain gauge wire;

[0033] The tire strain data of the wheeled-legged robot is obtained through a signal acquisition system provided on the wheeled-legged robot, specifically including:

[0034] Obtain a plurality of resistance data transmitted by the sensor unit. Among them, when an external pressure acts on the solid tire, it is transmitted to the PTFE hose through the solid tire and then to the resistive strain gauge. The resistance value of the resistive strain gauge changes linearly with the external pressure;

[0035] Calculate the resistance change value according to the plurality of resistance data;

[0036] Calculate the strain value according to the resistance change value to obtain the tire strain data, which is specifically calculated by the following formula:

[0037]

[0038] Among them, ΔR is the resistance change value, R is the initial resistance value, K s is the strain sensitivity, and ε is the strain value.

[0039] In the second aspect of the present application, a foot-end touchdown information detection device based on a wheeled-legged robot is provided. The device is a server, including an acquisition module, a processing module, and an output module, where:

[0040] The acquisition module is configured to obtain the tire strain data of the wheeled-legged robot through a signal acquisition system provided on the wheeled-legged robot;

[0041] The acquisition module is configured to obtain the acceleration data at the touchdown point when the tire of the wheeled-legged robot is in the touchdown state;

[0042] The processing module is configured to obtain the contact force data of the wheeled-legged robot and the spatial position of the tire touchdown point through the tire strain data and the acceleration data according to the established mapping relationship between the input vector and the output vector;

[0043] The output module is configured to analyze the touchdown information of the position where the wheeled-legged robot is located based on the contact force data and the spatial position.

[0044] Based on the above technical solutions, preferably, the processing module is configured to calculate the contact area of the contact region of the wheel-legged robot based on the contact force data, where the contact region is the region where the tire of the wheel-legged robot contacts the ground;

[0045] The processing module is configured to calculate the change rate of the contact force in the contact region according to the contact force data and the contact area;

[0046] The processing module is configured to calculate the ground deformation amount based on the change of the spatial position;

[0047] The processing module is configured to calculate the ground hardness according to the change rate and the ground deformation data;

[0048] The processing module is configured to determine the ground hardness category of the contact region by judging the magnitude relationship between the ground hardness and different preset thresholds.

[0049] Based on the above technical solutions, preferably, the processing module is configured to calculate the ground hardness according to the change rate and the ground deformation amount, and specifically calculate it through the following formula:

[0050]

[0051] where H is the ground hardness, F z is the normal reaction force included in the contact force data, Δz is the ground deformation amount, A is the contact area, and ΔF z is the change amount of the contact force.

[0052] Based on the above technical solutions, preferably, the processing module is configured to construct the input vector, where the input vector is composed of the acceleration data and multiple sets of the tire strain data;

[0053] The processing module is configured to construct an output vector, where the output vector includes the contact force data and the spatial position;

[0054] The processing module is configured to train the Gaussian process regression model with training data, where the training data includes training acceleration data and multiple sets of training tire strain data as inputs, and training contact force data and training spatial position as outputs;

[0055] The output module is configured to train the Gaussian process regression model with the training data, and use the Gaussian process regression model to model the mapping relationship between the input vector and the output vector, where the mapping relationship is expressed as follows:

[0056] Y j= k * (K - σ n Ι) -1 y j

[0057] Among them, Y j is the output vector, Y j = [F x , F y , F z T , y j is the input vector, σ n is the standard deviation, and the input vector is expressed as x = [s 1 , s 2 T , k * is a variable independent of the input quantity, and K is the covariance matrix.

[0058] Based on the above technical solutions, preferably, the output module is used to input the tire strain data and the acceleration data into the trained Gaussian process regression model, and obtain the contact force data and the spatial position of the tire contact point output by the trained Gaussian process regression model. Among them, the contact force data includes the shear force in the x-axis direction, the shear force in the y-axis direction, and the normal reaction force, and the spatial position includes the spatial coordinates of the contact point.

[0059] Based on the above technical solutions, preferably, the acquisition module is used to acquire a plurality of resistance data transmitted by the sensor unit. When an external pressure acts on the solid tire, it is transmitted to the PTFE hose through the solid tire and then to the resistive strain gauge, and the resistance value of the resistive strain gauge changes linearly with the external pressure;

[0060] The processing module is used to calculate the resistance change value according to the plurality of resistance data;

[0061] The processing module is used to calculate the strain value according to the resistance change value to obtain the tire strain data, and specifically calculate it through the following formula:

[0062]

[0063] Among them, ΔR is the resistance change value, R is the initial resistance value, K s is the strain sensitivity, and ε is the strain value.

[0064] ​​In a third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface, and a network interface. The memory is used to store instructions. Both the user interface and the network interface are used to communicate with other devices. The processor is used to execute the instructions stored in the memory, so that the electronic device executes the method described in any one of the above.

[0065] In a fourth aspect of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores instructions, and when the instructions are executed, the method described in any one of the above is executed.

[0066] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0067] 1. The present application can accurately detect the ground contact information of the wheel-legged robot. By combining the strain data of the tire and the acceleration data of the ground contact point, a mapping relationship between the input vector and the output vector is established using the Gaussian process regression model, and the contact force data and the spatial position of the tire ground contact point are accurately deduced. This method of multi-source data fusion can not only capture the force condition when the tire contacts the ground, but also combine the spatial coordinates of the ground contact point to comprehensively analyze the contact state of the wheel-foot robot, and then accurately reflect the hardness, shape, and contact force distribution of the ground, thereby improving the detection accuracy and reliability of the ground contact information.

[0068] 2. By comprehensively considering the contact force data, contact area, contact force change rate, and ground deformation data, the ground hardness is accurately calculated and classified through a set threshold. Thus, it can help the robot to perceive the ground characteristics in real time, such as hard road surface or soft road surface.

[0069] 3. A mapping relationship between the tire strain data, acceleration data, contact force data, and the spatial position of the tire ground contact point is accurately established through the Gaussian process regression model, and the contact force and ground contact position of the wheel-legged robot can be efficiently deduced. This method ensures high-precision detection in various complex terrains and different load conditions by training the model and combining multiple sets of sensor data.

[0070] 4. By encapsulating the resistive strain gauge and embedding it into the solid tire by drilling, it has the advantages of simple installation and high measurement accuracy. This solid tire detection method shows reliable dynamic measurement ability in the tire impact loading scenario. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 is a schematic flowchart of a method for detecting the ground contact information of the foot end of a wheel-legged robot disclosed in an embodiment of the present application;

[0072] Figure 2It is a schematic diagram of an application scenario of a method for detecting foot-end touchdown information based on a wheel-legged robot disclosed in an embodiment of the present application;

[0073] Figure 3 It is a schematic diagram of a simulation result of a finite element analysis of the touchdown situation of a solid tire disclosed in an embodiment of the present application;

[0074] Figure 4 It is a schematic diagram of the layout of a sensor unit disclosed in an embodiment of the present application;

[0075] Figure 5 It is a schematic diagram of the structure of a sensor unit disclosed in an embodiment of the present application;

[0076] Figure 6 It is a schematic diagram of the change of strain signals on different road surfaces disclosed in an embodiment of the present application;

[0077] Figure 7 It is a schematic diagram of the modules of a device for detecting foot-end touchdown information based on a wheel-legged robot disclosed in an embodiment of the present application;

[0078] Figure 8 It is a schematic diagram of the structure of an electronic device disclosed in an embodiment of the present application.

[0079] Explanation of reference numerals: 201, sensor unit; 202, acquisition module; 203, lower computer; 204, server; 501, polytetrafluoroethylene hose; 502, resistive strain gauge; 5021, metal foil; 5022, resin substrate; 5023, resin cover sheet; 503, strain gauge wire; 701, acquisition module; 702, processing module; 703, output module; 801, processor; 802, communication bus; 803, user interface; 804, network interface; 805, memory. Detailed implementation manners

[0080] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments.

[0081] In the description of the embodiments of the present application, words such as "for example" or "for illustration" are used to indicate examples, illustrations or explanations. Any embodiment or design solution described as "for example" or "for illustration" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, the use of words such as "for example" or "for illustration" is intended to present relevant concepts in a specific manner.

[0082] In the description of the embodiments of the present application, the term "plurality" means two or more. For example, a plurality of systems means two or more systems, and a plurality of screen terminals means two or more screen terminals. In addition, the terms "first" and "second" are used only for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the technical features indicated. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features. The terms "comprising", "including", "having" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0083] The wheel-leg robot combines the characteristics of wheeled and legged locomotion, has strong terrain adaptability, and can move flexibly in complex environments. Detection of foot-end contact information is crucial for its stability and adaptability, and can accurately sense characteristics such as the hardness, inclination, and obstacles of the ground, providing real-time feedback for the robot to adjust its motion mode and optimize the control system. In complex terrains or extreme environments, accurate contact information can effectively improve the adaptability and task execution ability of the robot in dynamic environments, ensuring its efficient and safe operation.

[0084] This embodiment discloses a method for detecting foot-end contact information of a wheel-leg robot, referring to Figure 1 , and includes the following steps S110 - S140:

[0085] S110, obtain the tire strain data of the wheel-leg robot through a signal acquisition system disposed on the wheel-leg robot.

[0086] A method for detecting foot-end contact information of a wheel-leg robot disclosed in the embodiments of the present application is applied to the server 204. The server 204 includes, but is not limited to, electronic devices such as mobile phones, tablet computers, wearable devices, and PCs (Personal Computers), and may also be a background server 204 running a method for detecting foot-end contact information of a wheel-leg robot. The server 204 can be implemented by an independent server 204 or a server cluster composed of multiple servers 204.

[0087] The wheel-leg robot is equipped with two sets of independent control systems and wheel-leg structures. Therefore, studying the foot-end contact information plays an important role in improving its motion stability and decision-making performance. In the design of the foot contact unit of the wheel-leg robot, the previously used pneumatic tires are prone to failure due to being punctured by sharp objects in rough terrains and require frequent maintenance to maintain normal tire pressure, and are not suitable for long-term special operations in extreme environments. In contrast, solid tires made of polyurethane have better puncture resistance and smaller deformations under heavy loads, and are suitable for the wheel-leg robot to perform long-term load operations in dangerous environments.

[0088] In order to effectively obtain contact information from a solid tire during the dynamic process of a wheel-legged robot, a sensor packaging structure that is lighter, softer, and more impact-resistant is required. Previous studies mainly focused on discussing the ground contact state detection method of a wheel-legged robot with an inflated tire as the foot end. Usually, a surface sensor is attached to the bottom or the side of the tire, and the ground contact state of the wheel-legged robot is judged by measuring the voltage signal output by the sensor. However, the above method is not applicable to the ground contact state detection of a solid tire, and there is currently a lack of an effective detection method for the ground contact state of a solid tire wheel-legged robot.

[0089] In a possible implementation manner, the signal acquisition system includes a plurality of sensor units 201. Among them, the plurality of sensor units 201 are embedded in the solid tire of the wheel-legged robot, and are annularly and equally angularly distributed inside the tire with the tire rotation axis as the center. Each sensor unit 201 includes a polytetrafluoroethylene hose 501 and a resistive strain gauge 502. The resistive strain gauge 502 includes a resin covering sheet 5023, a metal foil 5021, and a resin base 5022; the resistive strain gauge 502 is arranged in the central area of the polytetrafluoroethylene hose 501, and a two-component acrylate adhesive is injected into the polytetrafluoroethylene hose 501 to cure and package the resistive strain gauge 502; the metal foil 5021 is arranged directly above the resin base 5022, and the resin covering sheet 5023 covers directly above the metal foil 5021.

[0090] Specifically, referring to Figure 2 , the lower board of the signal acquisition system, the acquisition module 202, and the plurality of sensor units 201. The lower board and the acquisition module 202 read the sensor signals output by the sensor units 201 through the interface at a sampling frequency of 200 Hz and amplify and convert them into 10-bit analog values. To achieve this function, the present application further performs a finite element analysis on the ground contact situation of the solid tire in a simulation environment. Referring to Figure 3 , the figure shows a schematic diagram of the simulation result, and the strain-sensitive area is determined to be the area marked in the figure.

[0091] Based on the above conclusions, this application proposes to embed strain gauges into the solid tire through drilling to complete the construction of the design scheme of the sensor unit 201. Multiple sensor units 201 need to be embedded into the solid tire of the wheel-legged robot. The sensor units 201 are evenly distributed in a ring centered on the rotation axis of the tire, and the distribution angle of each sensor unit 201 is equal, so as to ensure comprehensive monitoring of the entire tire contact area. This method effectively utilizes the closed structure of the solid tire and provides a good protection environment for the sensors. However, in the actual application of the foot-end sensors of the wheel-legged robot, due to the relatively high material rigidity of the solid tire, the strain generated after being stressed is small, making it difficult for the distal strain gauges to effectively capture the ground contact signal and reducing the sensitivity of the sensors to the foot-end contact state. In addition, the wheel-legged robot faces complex terrain conditions during actual operation and needs to frequently switch the wheel-leg working mode. When the robot switches to the legged motion mode, the foot-end motion is limited by the rotational motion characteristics, which further increases the difficulty of a single set of sensors to collect effective signals, resulting in a certain impact on the accuracy and stability of the ground contact signal. To address these challenges, the design of the sensor unit 201 must be optimized for the ability to obtain foot-end contact signals to ensure the stability and reliability of the system in complex environments.

[0092] Based on the above requirements, this application proposes an improved sensor layout scheme. Referring to Figure 4 , the sensor units 201 are distributed in a circumferential array along the wheel center of the solid tire, and a sensor unit 201 is inserted every 30°, thus forming a high-density annular array. This design can not only increase the number of sensor arrangements but also significantly improve the spatial coverage and sensitivity of the contact signal. Through the collaborative work of the sensor array, even under complex terrain conditions or frequent switching of the wheel-leg working mode, the signal acquisition system can still obtain a stable and accurate ground contact signal response.

[0093] The principle and benefits of using the setting parameter of inserting a sensor unit 201 every 30° are mainly reflected in the following aspects:

[0094] First of all, the setting angle of 30° can ensure the uniform distribution of the sensor units 201 and ensure that the tire can be monitored more evenly throughout the entire contact area. Since the contact surface of the tire is a continuous area, by evenly distributing the sensors along the rotation axis direction of the tire, comprehensive monitoring of the tire's ground contact state can be achieved, avoiding the situation where some areas cannot be collected due to overly sparse sensor distribution.

[0095] Secondly, a distribution angle of 30° can effectively balance the relationship between the number of sensors, cost, and system complexity. A smaller angle (e.g., 15°) will lead to a significant increase in the number of sensors, which will not only increase the manufacturing and maintenance costs of the system but may also introduce signal redundancy and increase the complexity of data processing. While a larger angle (e.g., 45° or greater) may result in too sparse a distribution of sensors, reducing the spatial coverage and sensitivity of the contact signals, thereby affecting the stability and accuracy of the system in complex terrains. Therefore, the 30° angle is a compromise solution that can ensure high sensitivity without causing an excessive number of sensors, maintaining the rationality and economy of the system.

[0096] Furthermore, the 30° angle allows the sensors to work more efficiently in different terrain conditions. Through this spacing, the sensor array can better cover the contact area of the tire, ensuring that even in complex terrains or when frequently switching motion modes, the sensors can still stably and accurately collect the ground contact signals. The signals of different sensors can complement each other. When the position of a sensor changes slightly due to tire deformation, other sensors can still maintain a strong signal response, ensuring the stable operation of the system.

[0097] In summary, adopting a 30° angle as the setting parameter for sensor arrangement is to effectively control the number of sensors, cost, and data processing complexity on the premise of ensuring high coverage and sensitivity of the contact signals, ultimately achieving the goal of optimizing sensor arrangement and improving the reliability and accuracy of the system.

[0098] Furthermore, this application makes full use of the advantages of the array - type sensor distribution, enabling the wheel - legged robot to achieve multi - directional and multi - modal ground contact sensing capabilities. This improved solution not only enhances the robot's adaptability to complex terrains but also provides important support for its intelligent control and motion planning in unknown environments.

[0099] Furthermore, referring to Figure 5 , each sensor unit 201 is composed of a polytetrafluoroethylene hose 501 and a resistive strain gauge 502. Among them, due to the excellent bending elasticity of the polytetrafluoroethylene hose 501 material, it can not only sensitively respond to external pressure changes but also effectively transmit stress information to the internal strain gauge, ensuring the detection sensitivity of the sensor. The resistive strain gauge 502 includes a resin substrate 5022, a metal foil 5021, and a resin cover sheet 5023. The resistive strain gauge 502 is placed in the central area of the polytetrafluoroethylene hose 501. In this way, when the tire is stressed, the force is transmitted through the hose to the resistive strain gauge 502, causing it to deform and thus changing the resistance value.

[0100] To ensure the stability and reliability of the resistive strain gauge 502, the resistive strain gauge 502 is fixed and encapsulated in a PTFE hose 501 through a two-component acrylate adhesive. The two-component acrylate adhesive has high bonding strength and good curing performance, which can firmly fix the strain gauge after rapid curing and ensure its stability in actual applications. After the adhesive is injected into the hose, it will cure and encapsulate the resistive strain gauge 502, enabling it to work effectively for a long time without being affected by the external environment.

[0101] The specific structure of the resistive strain gauge 502 is that the metal foil 5021 is arranged directly above the resin substrate 5022, and the resin cover sheet 5023 covers directly above the metal foil 5021. A strain-sensitive area is formed between the metal foil 5021 and the resin substrate 5022. When an external force acts on the tire, this area will undergo a slight deformation, resulting in a change in resistance. Through this design, the resistive strain gauge 502 can accurately sense the deformation of the tire and convert the deformation into a measurable electrical signal. The resistive strain gauge 502 is connected to the acquisition module 202 through the strain gauge wire 503, thereby transmitting the electrical signal to the acquisition module 202. The acquisition module 202 then transmits it to the lower computer 203, and finally the lower computer 203 transmits the signal to the server 204.

[0102] This embedded sensor unit 201 design can provide real-time monitoring of the tire's force and deformation inside the tire, helping the wheel-legged robot accurately sense the touchdown state, thereby providing necessary ground adaptation information for the robot and supporting its efficient movement in complex environments.

[0103] Furthermore, multiple sensor units 201 are installed inside the solid tire of the wheel-legged robot. Each sensor unit 201 includes a resistive strain gauge 502 and a PTFE hose 501, where the resistive strain gauge 502 senses the impact of external pressure on the tire through resistance changes. When external pressure acts on the tire, the pressure is transmitted to the PTFE hose 501 through the solid tire and further acts on the resistive strain gauge 502. Under the working principle of the resistive strain gauge 502, the strain caused by external pressure causes a change in resistance, and the resistance change is linearly related to the applied external pressure.

[0104] The resistance change value of each sensor unit 201 will be transmitted to the server 204 through the strain gauge wire 503. The multiple resistance data received by the server 204 represents the responses of the strain gauges at different sensor positions, and then the strain value of the tire is calculated through calculation. Next, based on the obtained resistance change value, the strain value can be calculated through the strain sensitivity coefficient and the resistance change rate formula. The specific calculation formula is:

[0105]

[0106] where ΔR is the change in resistance, R is the initial resistance value, K s is the strain sensitivity, and ε is the strain value. The strain value directly reflects the degree of deformation of the tire caused by external pressure, and this data is crucial for the wheel-legged robot to judge the contact state between the tire and the ground and evaluate the ground information.

[0107] S120, obtain the acceleration data of the touchdown point when the tire of the wheel-foot robot is in the touchdown state.

[0108] Arrange six-axis acceleration sensors near the touchdown point of the tire. The function of these acceleration sensors is to monitor the acceleration information of the touchdown point in real time, including the acceleration along the x-axis and y-axis and the normal acceleration. The six-axis acceleration sensor can sense the acceleration changes in different directions, so as to accurately record the interaction force between the tire and the ground when touching the ground.

[0109] The arrangement of the sensors should consider the motion characteristics of the tire and the possible force application points to ensure that the sensors can cover the entire touchdown area, especially in the edge area where the tire contacts the ground, so as to capture the acceleration changes in all directions. According to specific application requirements, the sensors can be evenly distributed or concentrated in key areas to ensure sufficient information is collected.

[0110] S130, according to the established mapping relationship between the input vector and the output vector, obtain the contact force data of the wheel-legged robot and the spatial position of the tire touchdown point through the tire strain data and the acceleration data.

[0111] In a possible implementation manner, before obtaining the contact force data of the wheel-legged robot and the spatial position of the tire touchdown point through the tire strain data and the acceleration data according to the established mapping relationship between the input vector and the output vector, the method further includes: constructing an input vector, where the input vector is composed of the acceleration data and multiple sets of tire strain data; constructing an output vector, where the output vector includes the contact force data and the spatial position; training the Gaussian process regression model with training data, where the training data includes the training acceleration data and multiple sets of training tire strain data as the input, and the training contact force data and the training spatial position as the output; training the Gaussian process regression model with the training data, and using the Gaussian process regression model to model the mapping relationship between the input vector and the output vector.

[0112] Specifically, since the relationship between the readings and the applied force presents a non-linear complex relationship and cannot be directly analyzed and modeled, a Gaussian process regression model is used for modeling and analysis. First, the input vector needs to include acceleration data and multiple sets of tire strain data. The acceleration data provides the acceleration information when the tire touches the ground, thereby obtaining the shear forces and the normal reaction force along the x-axis and y-axis, while the tire strain data reflects the deformation of the tire due to external pressure. Each set of input data represents a specific tire state. By combining these data into an input vector, multi-dimensional information can be provided for the Gaussian process regression model, thereby helping to accurately estimate the output value.

[0113] The output vector includes contact force data, namely the shear forces and the normal force along the x, y, and z axes, and the spatial position of the tire contact point. The contact force data reflects the interaction force between the tire and the ground, while the spatial position represents the actual position of the tire contact point with the ground. By using these output data as the target, the model can train a mapping relationship to predict the corresponding output from the input data.

[0114] The training dataset is the basis for model training. The training data includes training acceleration data and training tire strain data as inputs, which represent the tire contact conditions under different ground conditions. At the same time, the output dataset includes training contact force data and training spatial positions, representing the force between the tire and the ground and the spatial coordinates of the contact point corresponding to the input data respectively. These training data should be widely representative and be able to cover different terrains, different loads, and different motion modes.

[0115] The Gaussian process regression model is trained using the training data. Gaussian process regression is a non-parametric Bayesian model that can learn the complex relationship between the input vector and the output vector through the built-in Gaussian kernel function. During the training process, the model learns the mapping relationship between the input data and the output data and optimizes the model parameters by minimizing the error. After training, the model can predict the output vector contact force and spatial position based on the input vector acceleration data and strain data.

[0116] Using the trained Gaussian process regression model, the mapping relationship between the input vector and the output vector is modeled using the training data. This mapping relationship is expressed as:

[0117] Y j =k * (K - σ n Ι) -1 y j

[0118] Where Y j is the output vector, Y j =[F x,F y ,F z T ,y j is the input vector, σ n is the standard deviation, and the input vector is expressed as x = [s 1 , s 2 T , k * is a variable independent of the input quantity, and K is the covariance matrix.

[0119] In the above formula, the expression of k * is as follows:

[0120] k * = [k(x * , x 1 ) k(x * , x 2 )... k(x * , x n )

[0121] where k(x * , x i ) is the covariance value between the input vector x * and the training data point x i , representing the correlation between the input data.

[0122] The expression of the covariance matrix K calculated from the covariances between all training data points is as follows:

[0123]

[0124] where the squared exponential covariance function is used as the kernel k during the calculation, and its expression is as follows:

[0125]

[0126] where σ f represents the signal variance, l represents the length scale, and the hyperparameters σ n , σ f and l are manually adjusted based on the evaluation regression results of the validation dataset.

[0127] ​​Integrate the strain data and acceleration data of the tire into an input vector and input it into the trained Gaussian process regression model. The trained model can, through the learned mapping relationship, predict the contact force data when the tire contacts the ground based on the input vector, including the shear forces in the x-axis and y-axis and the normal reaction force. In addition, it can also predict the spatial position of the tire contact point, that is, the three-dimensional coordinates of the contact point. Specifically, the input data passes through the inference process of the Gaussian process regression model by calculating the covariance matrix and the kernel function to obtain the output of the contact force and spatial position most relevant to the input data. Through this process, the wheel-legged robot can obtain the mechanical characteristics of the contact between its tire and the ground and the position of the contact point in real time, so as to achieve terrain adaptation and stable motion control.

[0128] Referring to Figure 6 , there are significant differences in the strain signal behavior between hard roads and soft roads. Referring to Figure 6 Part a in Figure 6 , on a hard road surface, the tire strain signal shows more drastic changes and higher peaks, indicating concentrated contact forces and larger instantaneous deformations. This behavior reflects the rapid strain changes associated with high-rigidity contact. Referring to

[0129] S140. Analyze the ground contact information of the position where the wheel-foot robot is located based on the contact force data and the spatial position.

[0130] In a possible implementation, analyze the ground contact information of the position where the wheel-foot robot is located based on the contact force data and the spatial position, specifically including: based on the contact force data, calculate the contact area of the contact area of the wheel-foot robot, and the contact area is the area where the tire of the wheel-foot robot contacts the ground; according to the contact force data and the contact area, calculate the change rate of the contact force in the contact area; based on the change of the spatial position, calculate the ground deformation data; according to the change rate and the ground deformation data, calculate the ground hardness; judge the size relationship between the ground hardness and different preset thresholds to determine the ground hardness category of the contact area.

[0131] Specifically, first, according to the normal reaction force and the contact pressure when the tire contacts the ground, the contact area can be calculated. The calculation formula for the contact area is:

[0132]

[0133] where F z is the normal reaction force included in the contact force data, representing the pressure in the vertical direction of the tire; σ contactis the contact pressure, which can usually be deduced from the data of the strain sensor.

[0134] By observing the change of the contact force data, the change rate of the contact force in the contact area can be calculated. Specifically, based on the change of the normal reaction force, its change rate can be calculated:

[0135]

[0136] The change rate here reflects the fluctuation of the contact force between the tire and the ground. When the contact force changes greatly, it usually indicates that the pressure is concentrated in the contact area and the ground may be harder; when the change is small, it means that the contact force is relatively uniform and the ground is softer.

[0137] According to the change of the spatial position of the tire when it contacts the ground, especially the deformation in the vertical direction, the deformation of the ground can be calculated. This step needs to be estimated based on the height change of the tire at the contact point:

[0138] Δz = z(new) - z(initial)

[0139] where Δz is the ground deformation, that is, the vertical displacement of the tire contact area, representing the deformation of the ground due to the pressure exerted by the tire. A larger ground deformation indicates a softer ground, and a smaller ground deformation indicates a harder ground.

[0140] By integrating the contact force data, the change rate of the contact area, and the ground deformation data, the ground hardness can be calculated, specifically through the following formula:

[0141]

[0142] where H is the ground hardness, F z is the normal reaction force included in the contact force data, Δz is the ground deformation, A is the contact area, and ΔF z is the change amount of the contact force.

[0143] This formula calculates the ground hardness by integrating the contact force data, the change rate of the contact area, and the ground deformation data. First, the basic part of the formula calculates the ground hardness through the normal reaction force, the contact area A, and the ground deformation, reflecting the magnitude of the force exerted under the unit contact area and unit vertical deformation. Next, by adding the correction term considering the change amount of the contact force, which reflects the change rate of the contact force in the contact area, further corrects the hardness calculation, making the formula able to more accurately represent the rigidity and force distribution of the ground. When the contact force changes greatly, the pressure is concentrated in the contact area and the hardness will increase accordingly; while a small change in the contact force indicates a softer ground.

[0144] After calculating the ground hardness, it is next necessary to compare the calculated hardness value with a preset threshold. Based on the magnitude of the hardness value, it is possible to determine which category the ground belongs to. If the ground hardness is greater than the set hardness threshold, the ground is considered a hard road surface; if the ground hardness is less than or equal to the hardness threshold, the ground is considered a soft road surface.

[0145] This embodiment also discloses a foot-end ground contact information detection device based on a wheel-legged robot. The device is the server 204. Refer to Figure 7 , and it includes an acquisition module 701, a processing module 702, and an output module 703, where:

[0146] The acquisition module 701 is configured to obtain the tire strain data of the wheel-legged robot through a signal acquisition system arranged on the wheel-foot robot.

[0147] The acquisition module 701 is configured to obtain the acceleration data of the touchdown point when the tire of the wheel-legged robot is in the ground contact state.

[0148] The processing module 702 is configured to obtain the contact force data of the wheel-legged robot and the spatial position of the tire touchdown point through the tire strain data and the acceleration data according to the established mapping relationship between the input vector and the output vector.

[0149] The output module 703 is configured to analyze the ground contact information of the position where the wheel-foot robot is located based on the contact force data and the spatial position.

[0150] In a possible implementation manner, the processing module 702 is configured to calculate the contact area of the contact area of the wheel-legged robot based on the contact force data, and the contact area is the area where the tire of the wheel-legged robot contacts the ground.

[0151] The processing module 702 is configured to calculate the change rate of the contact force in the contact area according to the contact force data and the contact area.

[0152] The processing module 702 is configured to calculate the ground deformation amount based on the change situation of the spatial position.

[0153] The processing module 702 is configured to calculate the ground hardness according to the change rate and the ground deformation data.

[0154] The processing module 702 is configured to judge the magnitude relationship between the ground hardness and different preset thresholds, and determine the ground hardness category of the contact area.

[0155] In a possible implementation manner, the processing module 702 is configured to calculate the ground hardness according to the change rate and the ground deformation amount, and specifically calculate it through the following formula:

[0156]

[0157] Among them, H is the ground hardness, F z is the normal reaction force included in the contact force data, Δz is the ground deformation, A is the contact area, and ΔF z is the change in the contact force.

[0158] In a possible implementation, the processing module 702 is configured to construct an input vector, where the input vector consists of acceleration data and multiple sets of tire strain data.

[0159] The processing module 702 is configured to construct an output vector, where the output vector includes contact force data and spatial position.

[0160] The processing module 702 is configured to train the Gaussian process regression model with training data, where the training data includes training acceleration data and multiple sets of training tire strain data as inputs, and training contact force data and training spatial position as outputs.

[0161] The output module 703 is configured to train the Gaussian process regression model with training data, and use the Gaussian process regression model to model the mapping relationship between the input vector and the output vector, where the mapping relationship is expressed as follows:

[0162] Y j = k * (K - σ n Ι) -1 y j

[0163] Among them, Y j is the output vector, Y j = [F x , F y , F z T , y j is the input vector, σ n is the standard deviation, the input vector is expressed as x = [s 1 , s 2 T , k * is a variable independent of the input quantity, and K is the covariance matrix.

[0164] In a possible implementation, the output module 703 is configured to input tire strain data and acceleration data into the trained Gaussian process regression model, and obtain the contact force data and the spatial position of the tire contact point output by the trained Gaussian process regression model, where the contact force data includes the shear force in the x-axis direction, the shear force in the y-axis direction, and the normal reaction force, and the spatial position includes the spatial coordinates of the contact point.

[0165] ​​In a possible implementation, an acquisition module 701 is configured to acquire a plurality of resistance data transmitted by a sensor unit 201. When an external pressure acts on a solid tire, it is transmitted to a polytetrafluoroethylene hose 501 through the solid tire and then to a resistive strain gauge 502. The resistance value of the resistive strain gauge 502 changes linearly in proportion to the external pressure.

[0166] A processing module 702 is configured to calculate a resistance change value based on the plurality of resistance data.

[0167] The processing module 702 is configured to calculate a strain value based on the resistance change value to obtain tire strain data, which is specifically calculated through the following formula:

[0168]

[0169] where ΔR is the resistance change value, R is the initial resistance value, K s is the strain sensitivity, and ε is the strain value.

[0170] It should be noted that: when the device provided in the above embodiment realizes its functions, only the division of the above functional modules is used for illustration. In practical applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process can be seen in the method embodiment, which will not be elaborated here.

[0171] This embodiment also discloses an electronic device. Referring to Figure 8 , the electronic device may include: at least one processor 801, at least one communication bus 802, a user interface 803, a network interface 804, and at least one memory 805.

[0172] Among them, the communication bus 802 is used to realize the connection and communication between these components.

[0173] Among them, the user interface 803 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 803 may further include a standard wired interface and a wireless interface.

[0174] Among them, the network interface 804 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0175] Among them, the processor 801 may include one or more processing cores. The processor 801 connects various parts within the entire server through various interfaces and lines, and executes various functions of the server and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 805, and by calling the data stored in the memory 805. Optionally, the processor 801 may be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). The processor 801 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), a modem, etc. Among them, the CPU mainly processes the operating system, user interface, application programs, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communication. It can be understood that the above-mentioned modem may not be integrated into the processor 801 and may be implemented separately by a single chip.

[0176] Among them, the memory 805 may include random access memory (RAM) and may also include read-only memory. Optionally, the memory includes a non-transitory computer-readable storage medium. The memory 805 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 805 may include a program storage area and a data storage area. Among them, the program storage area may store instructions for implementing the operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store the data involved in the above-mentioned various method embodiments. Optionally, the memory 805 may also be at least one storage device located far from the aforementioned processor 801. The memory 805, as a computer storage medium, may include an operating system, a network communication module, a user interface 803 module, and an application program for a method for detecting foot-end touchdown information of a wheel-legged robot.

[0177] In Figure 8In the electronic device shown, the user interface 803 is mainly used to provide an interface for the user to input data and obtain the data input by the user. The processor 801 can be used to call an application program stored in the memory 805, which is a method for detecting foot-end touchdown information of a wheel-legged robot. When executed by one or more processors 801, the electronic device performs the method of one or more of the above embodiments.

[0178] It should be noted that, for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0179] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0180] In several embodiments provided by this application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection can be through some service interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical or other form.

[0181] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0182] In addition, in each embodiment of this application, the functional units can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0183] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory 805 and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in various embodiments of the present application. And the aforementioned memory 805 includes: various media such as USB flash drives, mobile hard disks, magnetic disks, or optical discs that can store program codes.

[0184] The present application also discloses a computer-readable storage medium that stores instructions. When executed by one or more processors 801, it causes the electronic device to execute the method as described in one or more of the above embodiments.

[0185] The above are only exemplary embodiments of the present disclosure, and the scope of the present disclosure cannot be limited thereby. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure still fall within the scope covered by the present disclosure. After considering the specification and the disclosure of the practical truth, those skilled in the art will easily think of other implementation manners of the present disclosure. The present application aims to cover any variations, uses, or adaptive changes of the present disclosure, and these variations, uses, or adaptive changes follow the general principles of the present disclosure and include the common general knowledge or conventional technical means in the technical field not recorded in the present disclosure. The specification and the embodiments are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for detecting foot-end ground contact information based on a wheel-legged robot, characterized in that: The method is applied to a server (204), and the method comprises: Acquiring tire strain data of the wheel-foot robot through a signal acquisition system arranged on the wheel-foot robot; Acquiring acceleration data of a contact point when a tire of the wheeled robot is in a contact state; According to the established mapping relationship between the input vector and the output vector, the contact force data of the wheel-legged robot and the spatial position of the tire contact point are obtained through the tire strain data and the acceleration data; Based on the contact force data and the spatial position, the ground contact information of the wheeled robot is analyzed.

2. A method for detecting foot-end ground contact information based on a wheel-legged robot according to claim 1, characterized in that: The analyzing the ground contact information of the position of the wheeled robot based on the contact force data and the spatial position specifically includes: Based on the contact force data, calculating the contact area of ​​the wheeled robot contact region, the contact region being the region where the tire of the wheeled robot contacts the ground; Calculating a change rate of the contact force in the contact area according to the contact force data and the contact area; Calculating the ground deformation amount based on the change of the spatial position; Calculating ground hardness according to the change rate and the ground deformation data; The relationship between the ground hardness and different preset thresholds is judged to determine the ground hardness category of the contact area.

3. The method for detecting foot contact information of a wheel-legged robot according to claim 2, characterized in that: The ground hardness is calculated according to the change rate and the ground deformation, specifically by the following formula: Wherein, H is the ground hardness, F z is the normal reaction force included in the contact force data, Δz is the ground deformation, A is the contact area, ΔF z is the change in contact force.

4. The method for detecting foot contact information of a wheel-legged robot according to claim 1, characterized in that: Before obtaining the contact force data of the wheel-legged robot and the spatial position of the tire contact point through the tire strain data and the acceleration data according to the established mapping relationship between the input vector and the output vector, the method further includes: Constructing the input vector, wherein the input vector is composed of the acceleration data and a plurality of sets of tire strain data; constructing an output vector, wherein the output vector includes the contact force data and the spatial position; Training the Gaussian process regression model using training data, wherein the training data includes training acceleration data and multiple sets of training tire strain data as input, and training contact force data and training spatial position as output; The Gaussian process regression model is trained by the training data, and the mapping relationship between the input vector and the output vector is modeled by using the Gaussian process regression model, wherein the mapping relationship is expressed as follows: Y j =k * (K-s n I) -1 y j Among them, Y j is the output vector, Y j =[F x ,F y ,F z ] T ,y j is the input vector, σ n is the standard deviation, the input vector is represented by x=[s1,s2] T , k * is a variable independent of the input quantity, and K is the covariance matrix.

5. The method for detecting foot contact information of a wheel-legged robot according to claim 1, characterized in that: The step of obtaining the contact force data of the wheel-legged robot and the spatial position of the tire contact point through the tire strain data and the acceleration data according to the established mapping relationship between the input vector and the output vector specifically includes: The tire strain data and the acceleration data are input into the trained Gaussian process regression model to obtain the contact force data output by the trained Gaussian process regression model and the spatial position of the tire contact point, wherein the contact force data includes shear force in the x-axis direction, shear force in the y-axis direction and normal reaction force, and the spatial position includes the spatial coordinates of the contact point.

6. The method for detecting foot contact information of a wheel-legged robot according to claim 1, characterized in that: The signal acquisition system comprises a plurality of sensor units (201), wherein: A plurality of sensor units (201) are embedded in the solid tire of the wheel-legged robot and are distributed in a circular manner with equal angles inside the tire with the tire rotation axis as the center. Each sensor unit (201) comprises a polytetrafluoroethylene hose (501) and a resistance strain gauge (502). The resistance strain gauge (502) comprises a resin covering sheet (5023), a metal foil (5021) and a resin substrate (5022). The resistance strain gauge (502) is arranged in the central area of ​​the polytetrafluoroethylene hose (501), and a two-component acrylic adhesive is injected into the polytetrafluoroethylene hose (501) to cure and package the resistance strain gauge (502); The metal foil (5021) is disposed directly above the resin substrate (5022), and the resin covering sheet (5023) covers directly above the metal foil (5021).

7. The method for detecting foot contact information of a wheel-legged robot according to claim 6, characterized in that: The sensor unit (201) further comprises a strain gauge wire (503), and the server (204) and the sensor unit (201) are communicatively connected based on the strain gauge wire (503); The step of obtaining tire strain data of the wheeled robot by means of a signal acquisition system provided on the wheeled robot specifically includes: Acquiring a plurality of resistance data transmitted by the sensor unit (201), wherein when external pressure acts on the solid tire, the resistance data is transmitted to the polytetrafluoroethylene hose (501) through the solid tire and then to the resistance strain gauge (502), and the resistance value of the resistance strain gauge (502) changes linearly with the external pressure; Calculating a resistance change value according to the plurality of resistance data; The strain value is calculated according to the resistance change value to obtain the tire strain data, which is specifically calculated by the following formula: Wherein, ΔR is the resistance change value, R is the initial resistance value, K s is the strain sensitivity, and ε is the strain value.

8. A device for detecting foot-end ground contact information based on a wheel-legged robot, characterized in that: The device is a server (204), comprising an acquisition module (701), a processing module (702) and an output module (703), wherein: The acquisition module (701) is used to acquire tire strain data of the wheeled-legged robot through a signal acquisition system arranged on the wheeled-legged robot; The acquisition module (701) is used to acquire acceleration data of the contact point when the tire of the wheeled robot is in a contact state; The processing module (702) is used to obtain the contact force data of the wheel-legged robot and the spatial position of the tire contact point according to the established mapping relationship between the input vector and the output vector, through the tire strain data and the acceleration data; The output module (703) is used to analyze the ground contact information of the position of the wheeled robot based on the contact force data and the spatial position.

9. An electronic device, characterized in that: The electronic device comprises a processor (801), a communication bus (802), a user interface (803), a network interface (804) and a memory (805), wherein the memory (805) is used to store instructions, the user interface (803) and the network interface (804) are both used to communicate with other devices, the communication bus (802) is used to realize connection and communication between components in the electronic device, and the processor (801) is used to execute the instructions stored in the memory (805) so that the electronic device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is executed.

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