Terrain adaptive hexapod robot based on wheel-foot composite structure and K230 visual identification

Abstract

The invention belongs to the field of mobile robots, and provides a terrain self-adaptive hexapod robot based on a wheel-foot composite structure and K230 visual identification, which comprises a wheel-foot composite mechanical body, a multi-mode sensing system and a self-adaptive control system, the wheel-foot composite mechanical body comprises a leg wheel-foot integrated unit, a leg joint structure and a main body frame structure which are connected in sequence; the multi-mode sensing system comprises a hardware layer, a sensing layer and a fusion layer which are connected in sequence, a wheel type motion control unit, a foot type motion control unit and a mode switching control unit are embedded in the self-adaptive control system; by means of the multi-mode sensing system, the pavement flatness can be judged in real time, and motor output is dynamically optimized; through precise recognition of the sensing layer on the obstacle type and the ground material, the gait and torque can be intelligently adjusted, and the passing success rate of the complex terrain is improved.

Description

Technical Field

[0001] This invention relates to the field of mobile robots, and in particular to a terrain-adaptive hexapod robot based on a wheel-leg composite structure and K230 visual recognition. Background Technology

[0002] Existing mobile robots are mainly divided into wheeled, legged, and hybrid wheel-legged types. Wheeled robots, such as traditional AGVs, are fast and energy-efficient on flat surfaces, but they have poor obstacle-crossing ability and are prone to sinking on soft surfaces such as sand and mud, resulting in severely insufficient terrain adaptability. Legged robots, especially hexapods, can effectively traverse complex terrains based on their discrete footholds. However, existing hexapods require multiple joint servo motors to simulate gait when moving on flat surfaces, leading to slow movement speed, high energy consumption, and rapid wear and tear on the mechanical structure.

[0003] To address the aforementioned issues, hybrid wheel-legged robots have emerged. For example, CN105923067A discloses a small hybrid wheel-legged hexapod robot with switchable wheel structures at the ends of its legs. However, this existing technology and similar hybrid wheel-legged solutions generally suffer from the following drawbacks: weak environmental perception capabilities, relying heavily on low-precision sensors such as infrared and ultrasonic sensors, making it impossible to accurately identify terrain types and dynamic obstacles; due to insufficient perception capabilities, the switching of wheel-legged modes largely depends on preset programs or simple distance triggers, failing to make intelligent decisions based on real-time and complex environmental information, resulting in delayed or misjudged mode switching, and limited overall efficiency improvement in mixed terrain. Summary of the Invention

[0004] In order to overcome the shortcomings of the prior art, the purpose of this invention is to provide a terrain-adaptive hexapod robot based on a wheel-leg composite structure and K230 visual recognition, which solves the problems of low mobility and poor terrain adaptability of existing devices.

[0005] To achieve the above objectives, the present invention provides the following solution:

[0006] A terrain-adaptive hexapod robot based on a wheel-foot composite structure and K230 visual recognition includes: a wheel-foot composite mechanical body, a K230-based multimodal perception system, and an adaptive control system; the wheel-foot composite mechanical body includes: a leg wheel-foot integrated unit, a leg joint structure, and a main frame structure connected in sequence; the multimodal perception system includes: a hardware layer, a perception layer based on the YOLO-V5 architecture, and a fusion layer connected in sequence; the adaptive control system embeds a wheel motion control unit, a leg motion control unit, and a mode switching control unit; the leg wheel-foot integrated unit includes: 6 sets of wheel-foot composite structures;

[0007] The multimodal sensing system is mounted on the wheel-foot composite mechanical body, and the adaptive control system is connected to both the wheel-foot composite mechanical body and the multimodal sensing system.

[0008] The fusion layer is used to synchronously align and filter the recognition results output by the perception layer and the attitude data and ranging data collected by the sensors to obtain environmental perception results. The environmental perception results include: terrain type, precise location of obstacles, movement speed, and ground tilt information. The mode switching control unit is used to control the working state of the wheeled motion control unit and the footed motion control unit according to the environmental perception results using preset switching trigger conditions.

[0009] Preferably, the wheel-foot composite structure is made of an external rotor type brushless DC hub motor and a lower leg structure through an integrated injection molding process; the stator of the brushless DC hub motor is fixed inside the outer shell of the lower leg structure; the rotor of the brushless DC hub motor is covered with a rubber tire; the rotation axis of the brushless DC hub motor and the swing axis of the lower leg structure are spatially perpendicular.

[0010] Preferably, the leg joint structure includes: a large arm structure and a servo motor;

[0011] The boom structure is connected to the lower leg structure via one of the servo motors; the boom structure is connected to the side plate of the main frame structure via two of the servo motors.

[0012] Preferably, the main frame structure is integrally formed using PLA material through 3D printing technology; the supporting structure of the main frame structure is honeycomb-shaped; and the K230 vision terminal of the multimodal perception system is fixed in the mounting compartment at the top front end of the main frame structure.

[0013] Preferably, the hardware layer includes: a microcontroller, the K230 vision terminal, a six-axis inertial measurement unit, and an ultrasonic sensor;

[0014] The six-axis inertial measurement unit is fixed at the center of gravity of the main frame structure; the ultrasonic sensors are respectively set at the front, left and right sides of the main frame structure; the ultrasonic sensor at the front of the main frame structure is 150mm above the ground; the ultrasonic sensors on the left and right sides of the main frame structure are distributed at a 120° angle.

[0015] Preferably, the operation of the wheeled motion control unit includes:

[0016] The images acquired by the K230 vision terminal are processed using a preset path planning algorithm to obtain the optimal path;

[0017] Extract the error between the optimal path and the current direction to obtain the actual physical distance deviation;

[0018] The brushless DC hub motor is controlled using a PID controller based on the actual physical distance deviation.

[0019] Preferably, the operation of the foot motion control unit includes:

[0020] The target ground is divided into passable and impassable areas based on the terrain category and the preset range of passable categories;

[0021] The leg wheel-foot integrated unit is controlled using a triangular gait algorithm within the passable area.

[0022] The present invention discloses the following technical effects:

[0023] This invention provides a terrain-adaptive hexapod robot based on a wheel-leg composite structure and K230 visual recognition. Through a multimodal perception system, it solves the problem of low mobility of existing devices and realizes dynamic optimization of motor output. Through the perception layer's accurate identification of obstacle types and ground materials, it solves the problem of poor terrain adaptability of existing devices and realizes intelligent control of gait and torque. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 3D modeling images of body parts provided in embodiments of the present invention;

[0026] Figure 2 3D modeling images of the leg connection joints provided in embodiments of the present invention;

[0027] Figure 3 A 3D modeling image of the foot provided in an embodiment of the present invention;

[0028] Figure 4 The 3D modeling image of the leg provided in the embodiment of the present invention;

[0029] Figure 5 This is an overall structural diagram provided for an embodiment of the present invention. Detailed Implementation

[0030] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] The purpose of this invention is to provide a terrain-adaptive hexapod robot based on a wheel-leg composite structure and K230 visual recognition, which solves the problems of low mobility and poor terrain adaptability of existing devices.

[0032] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0033] Figure 1 These are 3D modeling images of body parts provided in embodiments of the present invention. Figure 2 This is a 3D modeling image of the leg connection joint provided in an embodiment of the present invention. Figure 3 This is a 3D modeling image of the foot provided in an embodiment of the present invention. Figure 4 The 3D modeling image of the leg provided in the embodiment of the present invention, such as Figures 1 to 4 As shown, this invention provides a terrain-adaptive hexapod robot based on a wheel-foot composite structure and K230 visual recognition, comprising: a wheel-foot composite mechanical body, a K230-based multimodal perception system, and an adaptive control system; the wheel-foot composite mechanical body includes: a leg wheel-foot integrated unit, a leg joint structure, and a main frame structure connected in sequence; the multimodal perception system includes: a hardware layer, a perception layer based on the YOLO-V5 architecture, and a fusion layer connected in sequence; the adaptive control system embeds a wheel motion control unit, a leg motion control unit, and a mode switching control unit; the leg wheel-foot integrated unit includes: 6 sets of wheel-foot composite structures;

[0034] The multimodal sensing system is mounted on the wheel-foot composite mechanical body, and the adaptive control system is connected to both the wheel-foot composite mechanical body and the multimodal sensing system.

[0035] The fusion layer is used to synchronously align and filter the recognition results output by the perception layer and the attitude data and ranging data collected by the sensors to obtain environmental perception results. The environmental perception results include: terrain type, precise location of obstacles, movement speed, and ground tilt information. The mode switching control unit is used to control the working state of the wheeled motion control unit and the footed motion control unit according to the environmental perception results using preset switching trigger conditions.

[0036] Specifically, the wheel-foot composite structure is made of an external rotor type brushless DC hub motor and a lower leg structure through an integrated injection molding process; the stator of the brushless DC hub motor is fixed inside the outer shell of the lower leg structure; the rotor of the brushless DC hub motor is covered with a rubber tire; the rotation axis of the brushless DC hub motor and the swing axis of the lower leg structure are spatially perpendicular.

[0037] Furthermore, the leg joint structure includes: a large arm structure and a servo motor;

[0038] The boom structure is connected to the lower leg structure via one of the servo motors; the boom structure is connected to the side plate of the main frame structure via two of the servo motors.

[0039] Specifically, the main frame structure is integrally formed using PLA material through 3D printing technology; the supporting structure of the main frame structure is honeycomb-shaped; and the K230 vision terminal of the multimodal perception system is fixed in the mounting compartment at the top front end of the main frame structure.

[0040] Furthermore, the hardware layer includes: a microcontroller, the K230 vision terminal, a six-axis inertial measurement unit, and an ultrasonic sensor;

[0041] The six-axis inertial measurement unit is fixed at the center of gravity of the main frame structure; the ultrasonic sensors are respectively set at the front, left and right sides of the main frame structure; the ultrasonic sensor at the front of the main frame structure is 150mm above the ground; the ultrasonic sensors on the left and right sides of the main frame structure are distributed at a 120° angle.

[0042] Specifically, the operation of the wheeled motion control unit includes:

[0043] The images acquired by the K230 vision terminal are processed using a preset path planning algorithm to obtain the optimal path;

[0044] Extract the error between the optimal path and the current direction to obtain the actual physical distance deviation;

[0045] The brushless DC hub motor is controlled using a PID controller based on the actual physical distance deviation.

[0046] Furthermore, the working process of the foot motion control unit includes:

[0047] The target ground is divided into passable and impassable areas based on the terrain category and the preset range of passable categories;

[0048] The leg wheel-foot integrated unit is controlled using a triangular gait algorithm within the passable area.

[0049] Specifically, the wheel-foot hybrid mechanical body includes: 1) Leg wheel-foot integrated unit: A drive wheel is integrated at the end of each leg, forming an integrated wheel-foot functional unit. This unit is manufactured by an external rotor brushless DC hub motor and the lower leg structure through an integrated injection molding process. The stator of the hub motor is fixed inside the outer shell of the lower leg end, while the rotor directly drives the outer rubber tire, with a tire diameter of 80mm. This integrated unit is reliably connected to the output shaft of the lower leg servo motor through threaded fasteners. The rotation axis of the wheel-foot and the swing axis of the lower leg are set to be spatially perpendicular. This design ensures that the wheels will not make unintended contact or interfere with the ground when the robot is performing legged walking.

[0050] 2) Leg joint structure: The lower leg and upper arm are connected via a high-torque servo motor, model RDS3115, with an output torque of 17 kg·cm. This joint is responsible for enabling the foot's pitch movement in the sagittal plane. The root of the upper arm is fixed to the robot's main frame side plate via two RDS3115 servos (arranged vertically). Through this connection method, a single leg forms a spatial open-chain motion mechanism with three rotational degrees of freedom.

[0051] 3) Main Frame Structure: The main frame is made of PLA material and integrally formed using 3D printing technology, with a nominal wall thickness of 3mm. Inside the frame, this embodiment designs and implements a lightweight honeycomb support structure. After mechanical verification, this structure successfully keeps the robot's total weight below 4.8 kg while ensuring the overall frame's bending modulus is greater than 2.5 GPa. At the top front of the frame, there is a dedicated K230 vision terminal mounting compartment. This terminal is double-fixed with clips and four M3 screws to ensure its stability under vibration. Furthermore, all leg servo motor mounting positions have M3 threaded sleeves pre-embedded during the printing process, with the clearance between the sleeve and the frame body precisely controlled to 0.05mm. (Overall structure reference) Figure 5 .

[0052] Furthermore, the multimodal perception system consists of a hardware layer, a perception layer, and a fusion layer: 1) Hardware layer: The STM32F407VET6 microcontroller manufactured by STMicroelectronics serves as the core of the entire system's computation and control. Visual perception tasks are handled by the K230 terminal, which integrates a 1080P resolution camera and a dedicated AI acceleration chip. The camera's X-axis is equipped with a servo motor, allowing for free adjustment of the camera's angle with the horizontal plane, thus avoiding obstruction of the robot's field of vision by its own legs during walking. The posture perception module uses an MPU6050 six-axis inertial measurement unit, which is fixed to the projection point of the robot's center of gravity onto the frame. In addition, three ultrasonic sensors are arranged at the front and left / right sides of the frame. The front sensor is 150mm above the ground, and the left and right sensors are distributed at a 120° angle, collectively forming a forward perception sector with a detection range of 0.2 meters to 2 meters.

[0053] 2) Perception Layer: On the K230 terminal, this embodiment deploys a specially improved visual recognition model. This model is based on the classic YOLO-V5 object detection architecture.

[0054] 3) Fusion Layer: To address the potential unreliability of data from a single sensor, this embodiment constructs a data fusion function based on the STM32F407VET6 main control chip. This function receives and integrates the recognition results from the K230 vision terminal, the attitude data measured by the MPU6050, and the ranging data from the ultrasonic sensor. Specifically, after the K230 vision terminal completes image recognition and processing through its built-in NPU, it sends the structured recognition results (including target category, pixel coordinates, confidence level, etc.) to the STM32 main control chip via the USART serial port in GBK encoding format. The main control chip's receive interrupt service routine captures the data packets in real time, performs verification and parsing, and extracts valid information. MPU6050 Attitude Data: The MPU6050 inertial measurement unit connects to the main control chip via the I2C bus, continuously outputting raw digital quantities (register values) of three-axis acceleration and three-axis angular velocity. The main control chip reads this raw data via I2C polling or interrupt methods. Ultrasonic ranging data: The main controller sends a trigger pulse via GPIO, and the sensor subsequently echoes a high-level signal with a pulse width proportional to the distance. The main controller measures this echo pulse width Techo through the input capture function (or a high-precision timer). Based on the measured echo pulse width Techo, using the speed of sound in air, the distance d is calculated using the formula: d = (Techo × 10⁻⁶) / (10⁻⁶) −6The actual distance to the obstacle ahead is calculated by multiplying (×340) / 5. Finally, the STM32F407VET6 outputs a comprehensive environmental perception result, which is presented in array form. This result includes the target category identified by the K230, pixel coordinates, confidence level, precise location of the obstacle (including X, Y, and Z axis coordinates and distance from the six-legged robot), current movement speed, and ground tilt information.

[0055] Specifically, the control methods of adaptive control systems include wheel motion control, foot motion control, and smooth switching control between the two modes: 1) Wheel motion control method:

[0056] Path tracking control: The K230 vision terminal acquires real-time images of the surrounding environment at a rate of 23 frames per second and identifies the centerline of the passable path based on an edge detection algorithm. It then calculates the lateral pixel offset E of the robot relative to this centerline. This pixel offset is converted into an actual physical distance deviation e using a pre-calibrated conversion coefficient (0.02 pixels). Subsequently, a proportional-integral-derivative (PID) controller is used for closed-loop regulation of this lateral deviation. The output U of the PID controller is calculated by the following formula: The proportional gain Kp is set to 0.8, the integral gain Ki to 0.2, and the derivative gain Kd to 0.1. These controller parameters were determined through the Ziegler-Nichols engineering tuning method, combined with multiple tests on a physical platform.

[0057] Motor actuation and differential steering: The control quantity U calculated by the PID controller is converted into the difference in pulse width modulation duty cycle of the left and right hub motors through a pre-established mapping relationship. By controlling the speed difference between the two motors, precise steering control is achieved during the robot's movement.

[0058] Obstacle avoidance and mode switching: The system continuously integrates the distance information of obstacles ahead measured by ultrasonic sensors with the detection and speed estimation results of moving obstacles by the K230 vision terminal. When a static obstacle is detected within 20cm ahead, or a sudden change in ground type is detected, the control system will immediately interrupt the current wheeled movement and trigger the switching process to foot-based movement mode.

[0059] 2) Foot movement control methods:

[0060] Improved triangular gait: A leg movement coordination method based on triangular gait is adopted. The robot's six legs are divided into two groups, namely group A (legs 1, 3, and 5) and group B (legs 2, 4, and 6). During movement, the two groups of legs alternate between the support phase and the swing phase.

[0061] Visually guided landing point planning: The core improvement of the gait algorithm lies in the introduction of visual feedback. Passable areas of the ground identified by the K230 (such as hard surfaces) are assigned lower cost values, while impassable areas (such as soft sand or puddles) are assigned higher cost values. During the swing phase, the algorithm plans a safe landing point for the foot of the swinging leg within the low-cost area.

[0062] Terrain-adaptive gait adjustment: When the perception system detects that the robot is on a slope and the measured slope angle θ is greater than 15 degrees, the gait algorithm dynamically adjusts its parameters. The support phase time Ts will be determined according to the formula... The system adaptively extends its stride length by 25% via a servo motor current feedback loop to effectively prevent slippage on slopes. When the vision system detects a narrow passage (less than 30cm wide) ahead, it automatically switches from the standard gait to a "narrow passage gait" with a 40% reduction in stride length to ensure smooth passage.

[0063] 3) Mode switching control method:

[0064] Switching trigger conditions: The switching between wheeled and footed modes is dynamically triggered by the output of the multimodal sensing system, rather than depending on fixed time or distance thresholds.

[0065] The conditions for switching from wheeled mode to footed mode are: the K230 vision terminal, combined with ultrasonic sensors, detects an obstacle that cannot be directly crossed within 20cm in front, such as a step with a height greater than 5cm, or detects a sudden change in the ground type.

[0066] The conditions for switching from foot mode back to wheel mode are: the K230 vision terminal confirms that there are no obstacles within 50cm in front, and the ground is judged to be flat (tilt angle θ is less than 5 degrees). This state must be maintained stably for 2 seconds.

[0067] Switching process calibration: During mode switching, the system continuously monitors whether the leg posture is adjusted correctly. If the actual distance between the foot and the ground deviates from the expected value by more than 2cm, the main control board will notify the user via I / O. 2 The C-communication interface immediately sends an angle correction command to the corresponding servo. After switching to foot mode and taking the first step, the system will visually track the actual landing point. If the landing point deviates from the planned position by more than 5cm, the system will fine-tune the subsequent gait cycle duration (the adjustment amount is approximately ±0.1 seconds) to ensure the stability and continuity of the gait.

[0068] The beneficial effects of this invention are as follows:

[0069] (1) Significantly improve mobility: Wheel mode is used preferentially on flat ground, resulting in faster speed and lower energy consumption. The K230 vision system judges the road surface flatness in real time and can dynamically optimize the motor output. Actual measurements show that the energy consumption of wheel mode is at least 15% lower than that of traditional solutions.

[0070] (2) Strong terrain adaptability: When encountering complex terrain, the legged mode can easily cross obstacles. Combined with YOLO-V5's accurate recognition of obstacle types and ground materials, the robot can intelligently adjust its gait and torque, increasing the success rate of crossing complex terrain by 30%.

[0071] (3) Compact and reliable structure: The self-designed wheel and foot integrated structure reduces the number of parts, lowers the failure rate and maintenance cost. The integrated design of the K230 terminal is 40% smaller in size than the traditional split camera + processing board solution, and is more adaptable to harsh environments such as vibration and dust.

[0072] (4) Highly intelligent control: The control system based on STM32F407VET6 achieves fully autonomous decision-making. Combined with real-time environmental data fed back by K230, parameters such as stride and joint torque are dynamically fine-tuned through multi-sensor fusion algorithms, which greatly improves the robot's automation level and environmental adaptability.

[0073] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0074] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A terrain-adaptive hexapod robot based on a wheel-leg composite structure and K230 visual recognition, characterized in that, include: Wheel-foot composite mechanical body, K230-based multimodal sensing system, adaptive control system; The wheel-foot composite mechanical body includes: a leg wheel-foot integrated unit, a leg joint structure, and a main frame structure connected in sequence; the multimodal perception system includes: a hardware layer, a perception layer based on the YOLO-V5 architecture, and a fusion layer connected in sequence; the adaptive control system embeds a wheel motion control unit, a foot motion control unit, and a mode switching control unit; the leg wheel-foot integrated unit includes: 6 sets of wheel-foot composite structures; The multimodal sensing system is mounted on the wheel-foot composite mechanical body, and the adaptive control system is connected to both the wheel-foot composite mechanical body and the multimodal sensing system. The fusion layer is used to synchronously align and filter the recognition results output by the perception layer and the attitude data and ranging data collected by the sensors to obtain environmental perception results. The environmental perception results include: terrain type, precise location of obstacles, movement speed, and ground tilt information. The mode switching control unit is used to control the working state of the wheeled motion control unit and the footed motion control unit according to the environmental perception results using preset switching trigger conditions.

2. The terrain-adaptive hexapod robot based on a wheel-leg composite structure and K230 visual recognition as described in claim 1, characterized in that, The wheel-foot composite structure is made of an external rotor type brushless DC hub motor and a lower leg structure through an integrated injection molding process; the stator of the brushless DC hub motor is fixed inside the outer shell of the lower leg structure; the rotor of the brushless DC hub motor is covered with a rubber tire; the rotation axis of the brushless DC hub motor and the swing axis of the lower leg structure are spatially perpendicular.

3. The terrain-adaptive hexapod robot based on a wheel-leg composite structure and K230 visual recognition as described in claim 2, characterized in that, The leg joint structure includes: a large arm structure and a servo motor; The boom structure is connected to the lower leg structure via one of the servo motors; the boom structure is connected to the side plate of the main frame structure via two of the servo motors.

4. A terrain-adaptive hexapod robot based on a wheel-leg composite structure and K230 visual recognition as described in claim 3, characterized in that, The main frame structure is integrally formed using PLA material through 3D printing technology; the supporting structure of the main frame structure is honeycomb-shaped; the K230 vision terminal of the multimodal perception system is fixed in the mounting compartment at the top front of the main frame structure.

5. A terrain-adaptive hexapod robot based on a wheel-leg composite structure and K230 visual recognition as described in claim 4, characterized in that, The hardware layer includes: a microcontroller, the K230 vision terminal, a six-axis inertial measurement unit, and an ultrasonic sensor; The six-axis inertial measurement unit is fixed at the center of gravity of the main frame structure; the ultrasonic sensors are respectively set at the front, left and right sides of the main frame structure; the ultrasonic sensor at the front of the main frame structure is 150mm above the ground; the ultrasonic sensors on the left and right sides of the main frame structure are distributed at a 120° angle.

6. A terrain-adaptive hexapod robot based on a wheel-leg composite structure and K230 visual recognition as described in claim 5, characterized in that, The operation of the wheeled motion control unit includes: The images acquired by the K230 vision terminal are processed using a preset path planning algorithm to obtain the optimal path; Extract the error between the optimal path and the current direction to obtain the actual physical distance deviation; The brushless DC hub motor is controlled using a PID controller based on the actual physical distance deviation.

7. A terrain-adaptive hexapod robot based on a wheel-leg composite structure and K230 visual recognition as described in claim 5, characterized in that, The working process of the foot motion control unit includes: The target ground is divided into passable and impassable areas based on the terrain category and the preset range of passable categories; The leg wheel-foot integrated unit is controlled using a triangular gait algorithm within the passable area.

Images