Motion control method, device and equipment of quadruped and leg-wheel robot and storage medium

Abstract

The application discloses a motion control method, device and equipment of a quadruped and leg-wheel robot and a storage medium, relates to the technical field of robot motion control, and comprises the following steps: acquiring a navigation instruction, environment image information and camera internal parameter information; constructing a corresponding local depth map based on the environment image information and the camera internal parameter information; analyzing corresponding obstacle positions and landing positions based on the local depth map, and determining obstacle position analysis results and landing position analysis results; updating the navigation instruction based on the obstacle position analysis results and the landing position analysis results, and generating a robot motion instruction; and controlling the robot to move, avoid obstacles or adjust gaits based on the robot motion instruction. The application synchronously analyzes obstacles and landable areas by constructing a local depth map, thereby generating a motion instruction that combines obstacle avoidance and gait adjustment, so that the robot can autonomously select an efficient and stable movement strategy in various terrains.

Description

Technical Field

[0001] This application relates to the field of robot motion control technology, and in particular to motion control methods, devices, equipment and storage media for quadrupedal and wheeled robots. Background Technology

[0002] Quadrupedal and wheeled robots have important applications in complex environments such as wilderness exploration, emergency rescue, and terrain inspection. Their requirements for autonomous motion control capabilities are increasing. In edge computing scenarios, robots need to achieve integrated control functions such as real-time environmental perception, autonomous obstacle avoidance, foothold selection, and navigation tracking without relying on high-performance GPUs or cloud computing power, in order to ensure stable and efficient operation on resource-constrained platforms.

[0003] Currently, existing robot motion control systems mostly rely on high-performance GPU platforms or cloud computing resources. They use vision or fusion sensors for environmental perception and 3D reconstruction, and employ algorithms such as SLAM and path planning for navigation and obstacle avoidance. However, existing methods rely on high-computing-power perception and planning algorithms that are difficult to run in real time on low-power platforms such as ARM, resulting in large response delays and low control frequencies. Furthermore, functions such as navigation, obstacle avoidance, and foothold selection are often handled separately, lacking a deep fusion mechanism. In dynamic and unstructured environments, decision conflicts and motion instability are prone to occur, making it difficult to meet real-time requirements. Therefore, how to achieve more efficient and stable motion control for quadrupedal and wheeled robots has become an urgent problem to be solved.

[0004] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main objective of this application is to provide a motion control method, device, equipment, and storage medium for quadrupedal and legged wheeled robots, aiming to solve the technical problem of how to perform motion control of quadrupedal and legged wheeled robots more efficiently and stably.

[0006] To achieve the above objectives, this application proposes a motion control method for a quadrupedal and wheeled robot, the method comprising: Acquire navigation instructions, environmental image information, and camera intrinsic parameter information; A corresponding local depth map is constructed based on the environmental image information and the camera intrinsic parameter information; Based on the local depth map, the corresponding obstacle positions and landing positions are analyzed to determine the obstacle position analysis results and landing position analysis results; The navigation instructions are updated based on the obstacle position analysis results and the foot position analysis results to generate robot movement instructions; The robot's movement, obstacle avoidance, or gait adjustment are controlled based on the aforementioned robot motion commands.

[0007] In one embodiment, the step of constructing a corresponding local depth map based on the environmental image information and the camera intrinsic parameter information includes: The environmental image information is downsampled to determine the corresponding sparse key point feature set; The disparity of the sparse keypoint feature set is calculated using local block matching to determine the disparity information; A corresponding local depth map is constructed based on the camera intrinsic information and the parallax information.

[0008] In one embodiment, the step of analyzing the corresponding obstacle positions and landing positions based on the local depth map to determine the obstacle position analysis results and landing position analysis results includes: Obtain the gradient change rate and target depth threshold; The obstacle position is analyzed based on the local depth map, the gradient change rate, and the target depth threshold to determine the obstacle position analysis result; The landing position is analyzed based on the target landing area corresponding to the local depth map, and the landing position analysis result is determined.

[0009] In one embodiment, the step of analyzing the obstacle position based on the local depth map, the gradient change rate, and the target depth threshold to determine the obstacle position analysis result includes: Based on the gradient change rate and the target depth threshold, a first-order difference operation is performed on the local depth map to determine the local height change of the obstacle. The corresponding obstacle confidence level is determined based on the local height change of the obstacle. Based on the obstacle confidence level, the corresponding obstacle positions are analyzed to determine the obstacle position analysis results.

[0010] In one embodiment, the step of analyzing the landing position based on the target landing area corresponding to the local depth map and determining the landing position analysis result includes: The target landing area is detected using a sliding window in the local depth map to determine the target landing area information; Calculate the height variance and ground normal information for each region based on the target landing area information; Calculate the corresponding landing confidence level based on the height variance information and the ground normal information; The landing positions are analyzed based on the landing confidence level corresponding to the available landing points, and the landing position analysis results are obtained.

[0011] In one embodiment, the step of updating the navigation instructions based on the obstacle position analysis results and the foot position analysis results to generate robot motion instructions includes: Based on the obstacle position analysis results and the landing position analysis results, corresponding obstacle avoidance correction and landing stability correction are generated. The navigation commands are updated based on the obstacle avoidance correction and the foot stability correction to obtain robot motion commands.

[0012] In one embodiment, the step of updating the navigation commands based on the obstacle avoidance correction and the foot stability correction to obtain robot motion commands includes: Obtain weight parameter information; The robot fusion command is determined by fusing the weight parameter information, the obstacle avoidance correction amount, the foot stability correction amount, and the navigation command. The corresponding robot motion mode is updated according to the robot fusion instruction to obtain the robot motion instruction.

[0013] Furthermore, to achieve the above objectives, this application also proposes a motion control device for a quadrupedal and wheeled robot, the motion control device for the quadrupedal and wheeled robot comprising: The acquisition module is used to acquire navigation instructions, environmental image information, and camera intrinsic parameter information; The processing module is used to construct a corresponding local depth map based on the environmental image information and the camera intrinsic parameter information; The processing module is also used to analyze the corresponding obstacle positions and landing positions based on the local depth map, and determine the obstacle position analysis results and landing position analysis results; An execution module is used to update the navigation instructions based on the obstacle position analysis results and the foot position analysis results, and generate robot motion instructions; The execution module is also used to control the robot's movement, obstacle avoidance, or gait adjustment based on the robot's motion commands.

[0014] In addition, to achieve the above objectives, this application also proposes a motion control device for a quadrupedal and wheeled robot, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the motion control method for the quadrupedal and wheeled robot as described above.

[0015] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the motion control method for the quadrupedal and wheeled robot described above.

[0016] One or more technical solutions proposed in this application have at least the following technical effects: This embodiment proposes a motion control method for a quadrupedal and wheeled robot. The method involves acquiring navigation commands, environmental image information, and camera intrinsic parameters. A corresponding local depth map is constructed based on the environmental image information and the camera intrinsic parameters. The positions of obstacles and landing positions are analyzed based on the local depth map to determine the obstacle position analysis results and landing position analysis results. The navigation commands are updated based on the obstacle position analysis results and landing position analysis results to generate robot motion commands. The robot's movement, obstacle avoidance, or gait adjustment is controlled based on these motion commands. This application constructs a local depth map to simultaneously analyze obstacles and landing areas, and directly integrates the corresponding analysis results into the navigation commands. This generates robot motion commands that integrate obstacle avoidance and gait adjustment in real time, meeting the real-time response requirements in dynamic environments. It achieves coordination between motion intention and terrain perception, enabling the robot to autonomously select efficient and stable movement strategies in both flat and complex terrains, significantly improving the motion continuity and overall stability in unstructured terrain. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A flowchart illustrating an embodiment of the motion control method for a quadrupedal and wheeled robot of this application; Figure 2 A flowchart illustrating a second embodiment of the motion control method for a quadrupedal and wheeled robot of this application; Figure 3 This is a schematic diagram of the module structure of the motion control device for a quadrupedal and wheeled robot according to an embodiment of this application; Figure 4This is a schematic diagram of the equipment structure of the hardware operating environment involved in the motion control method of the quadrupedal and wheeled robot in the embodiments of this application.

[0020] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0022] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0023] The main solution of this application embodiment is as follows: acquiring navigation instructions, environmental image information, and camera intrinsic parameter information; constructing a corresponding local depth map based on the environmental image information and the camera intrinsic parameter information; analyzing the corresponding obstacle positions and landing positions based on the local depth map to determine the obstacle position analysis results and the landing position analysis results; updating the navigation instructions based on the obstacle position analysis results and the landing position analysis results to generate robot motion instructions; and controlling the robot to move, avoid obstacles, or adjust its gait based on the robot motion instructions.

[0024] In this embodiment, for ease of description, the motion control device for recognizing quadrupedal and wheeled robots will be used as the execution subject in the following description.

[0025] Because existing technologies rely on high-performance perception and planning algorithms that are difficult to run in real time on low-power platforms such as ARM, they result in large response delays, low control frequencies, and often separate processing of functions such as navigation, obstacle avoidance, and landing point selection, lacking a deep integration mechanism. In dynamic and unstructured environments, they are prone to decision conflicts and motion instability, making it difficult to meet real-time requirements.

[0026] This application provides a solution for acquiring navigation commands, environmental image information, and camera intrinsic parameter information; constructing a corresponding local depth map based on the environmental image information and the camera intrinsic parameter information; analyzing the corresponding obstacle positions and landing positions based on the local depth map to determine the obstacle position analysis results and landing position analysis results; updating the navigation commands based on the obstacle position analysis results and landing position analysis results to generate robot motion commands; and controlling the robot to move, avoid obstacles, or adjust its gait based on the robot motion commands.

[0027] As can be seen from the above embodiments, this application constructs a local depth map to synchronously analyze obstacles and footholds, and directly integrates the corresponding analysis results into the navigation commands to generate robot motion commands that integrate obstacle avoidance and gait adjustment in real time. This meets the real-time response requirements in dynamic environments, realizes the coordination of motion intention and terrain perception, and enables the robot to autonomously select efficient and stable movement strategies in both flat and complex terrains, significantly improving the motion continuity and overall stability of movement in unstructured terrain.

[0028] Based on this, embodiments of this application provide a motion control method for a quadrupedal and wheeled robot, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the motion control method for the quadrupedal and wheeled robot of this application.

[0029] In this embodiment, the motion control method for the quadrupedal and wheeled robot includes steps S10 to S50: Step S10: Obtain navigation instructions, environmental image information, and camera intrinsic parameter information; It should be noted that the navigation command is a command issued by the robot's upper-level path planning module or external control system to indicate the robot's expected motion state. It is a motion target that guides the robot to move along a global or local path. The environmental image information is an image containing visual data of the scene in front of the robot, which is collected in real time by the binocular camera on the robot. The camera intrinsic parameter information is the internal parameter of the camera's own imaging geometric properties obtained through camera calibration.

[0030] In a specific embodiment, navigation commands are issued in real time by the robot's upper-level path planning module or an external control system. These commands may include the robot's desired speed, turning angle, and target position on the travel path. Simultaneously, the robot's binocular cameras acquire environmental images of the scene in front of it at a fixed frequency, resulting in left and right eye images. To adapt to the limited computing power of the ARM platform, the images are downsampled to reduce resolution, decrease data volume, and meet the requirements of lightweight computing. The camera intrinsic parameters are obtained through pre-calibration and may include focal length, optical center coordinates, and lens distortion parameters. These parameters are stored in the robot's local configuration file and loaded when the system starts for subsequent depth map construction and 3D coordinate transformation.

[0031] Step S20: Construct a corresponding local depth map based on the environmental image information and the camera intrinsic parameter information; It should be noted that the local depth map is a two-dimensional matrix data reconstructed in real time, representing the three-dimensional spatial structure of the terrain surface relative to the camera coordinate system within a limited distance in front of the robot. The limited distance can be 2-3 meters, and each pixel value in the two-dimensional matrix data corresponds to the distance and depth of that point in the scene relative to the camera.

[0032] In a specific embodiment, the environmental image information is downsampled to determine the corresponding sparse keypoint feature set. Local block matching is used to calculate the disparity of the sparse keypoint feature set to determine the disparity information. Based on the camera intrinsic information and the disparity information, a corresponding local depth map is constructed. This allows for a lightweight depth calculation method using local block matching and sparse feature aggregation, and simplifies 3D coordinate reconstruction using camera intrinsics. First, the left eye image of the environmental image information is processed. And right eye image Perform downsampling to This allows for the extraction of sparse keypoint feature sets. Features such as ORB or FAST are used to obtain disparity through block matching. , is represented as:

[0033] Then, a simplified depth transformation is used to calculate the local depth map: Where f is the focal length, B is the binocular baseline, and ε is a differential value to prevent division by zero.

[0034] At this point, a sparse point cloud can be generated and locally filtered and averaged within a sliding window to obtain the distance and depth of that point in the scene relative to the camera, represented as:

[0035] The sparse local point cloud constructed in this way retains only the key region (the central region within 2–3 m in front), reducing the computational cost to 10%–15% of the original algorithm, and can run in real time at 10–15 Hz on platforms such as ARM Cortex-A78.

[0036] In one feasible implementation, step S20 may include steps A11 to A13: Step A11: Downsample the environmental image information to determine the corresponding sparse key point feature set; It should be noted that the sparse key point feature set is a set of significant feature points distributed throughout the entire image but whose number is significantly less than the total number of pixels, extracted from the environmental image acquired by the binocular camera and after downsampling processing using lightweight feature detection algorithms such as FAST and ORB. For example, corner points and edge intersections. It has the characteristics of data sparsity and computational efficiency, which greatly reduces the amount of computation for subsequent matching while preserving key structural information of the scene.

[0037] Step A12: Use local block matching to calculate the disparity of the sparse keypoint feature set and determine the disparity information; It should be noted that the parallax information is a set of horizontal position differences of the same spatial point on the left and right image planes, calculated by performing local block matching on the sparse key point feature set in the environmental images acquired from the left and right perspectives by a binocular camera. It represents the imaging offset of each feature point in the scene caused by the camera baseline and is inversely proportional to the object depth.

[0038] Step A13: Construct a corresponding local depth map based on the camera intrinsic information and the parallax information.

[0039] It is understood that the local depth map can extract sparse key point feature sets by downsampling the image through a lightweight algorithm, solve the parallax using local block matching, and then combine the camera intrinsic parameters to generate depth in real time. It focuses on a local area in front of the robot, such as the central area within 2-3 meters, to retain key environmental information in a sparse form, reduce computational overhead, and thus efficiently support obstacle position analysis and foothold selection on the ARM platform with limited computing power.

[0040] Step S30: Analyze the corresponding obstacle positions and landing positions based on the local depth map to determine the obstacle position analysis results and landing position analysis results; Understandably, obstacle location is the set of spatial coordinates and boundary descriptions of all terrain protrusions, depressions, or foreign objects in the identified environment that pose a collision risk to the robot's movement or exceed its passage capability. It represents the distance, orientation, and contour information relative to the robot body. Foot placement is a set of ground points that can be stably and safely contacted by the robot's feet after detection by a sliding window and evaluation by combining height variance and ground normal. The selection criteria include area flatness, tilt angle, and support confidence, thereby obtaining candidate foot placement coordinates with feasibility and priority, which are used for gait generation and foot trajectory planning.

[0041] In a specific embodiment, the gradient change rate and the target depth threshold are obtained; based on the local depth map, the gradient change rate, and the target depth threshold, the obstacle position is analyzed to determine the obstacle position analysis result. That is, after obtaining the local depth map, this invention adopts a lightweight obstacle avoidance criterion based on the depth threshold and the gradient change rate. (The text then abruptly shifts to a different topic: "For the depth map...") Perform a first-order difference operation to obtain the local height change:

[0042] Define barrier confidence :

[0043] when At that time, it was considered that the area was flat and passable; when When this happens, the obstacle avoidance strategy is triggered.

[0044] It avoids complex 3D reconstruction and voxelization processes, requiring only a small amount of difference and exponential operations, making it suitable for real-time execution on edge platforms.

[0045] Based on the target landing area corresponding to the local depth map, the landing position is analyzed to determine the landing position analysis results. That is, in the same depth map, a sliding window is used to detect candidate landing areas, and combined with ground normal estimation and height variance constraints, a set of possible landing points is quickly selected. The local height variance is determined as follows:

[0046] like Then the area is flat; Next, determine the ground normal constraint, which is obtained from the local gradient and expressed as:

[0047] like If so, then it is considered acceptable to settle down; Calculate the overall confidence level:

[0048] Select the one that satisfies The point is used as a landing point.

[0049] It can be implemented entirely on a 2D depth map without the need for large-scale 3D fitting, and its computational complexity is significantly lower than that of traditional point cloud normal estimation.

[0050] Step S40: Update the navigation instructions based on the obstacle position analysis results and the foot position analysis results to generate robot motion instructions; It should be noted that the robot motion command is based on the upper-level navigation command, which integrates the obstacle avoidance correction and landing stability correction generated by the obstacle position analysis results and the landing position analysis results obtained from the local depth map analysis. After being weighted and fused by weight parameters, the output is a real-time control command used to directly drive the robot's underlying actuator.

[0051] In a specific embodiment, corresponding obstacle avoidance corrections and landing stability corrections are generated based on the obstacle position analysis results and the landing position analysis results; weight parameter information is obtained; the weight parameter information, the obstacle avoidance corrections, the landing stability corrections, and the navigation commands are fused to determine the robot fusion command; the corresponding robot motion mode is updated according to the robot fusion command to obtain the robot motion command, which means that the upper-level navigation commands can be integrated at the motion control level. By fusing with visual perception results, local path adjustment can be achieved under low computing power conditions. Specifically, if... Output instructions to the navigation module. The correction amount provided for the obstacle avoidance module. For local stability correction of the landing point planning, the fusion control command can be expressed as:

[0052]

[0053] in, These are the weight parameters.

[0054] This fusion strategy enables robots to make integrated decisions on navigation, obstacle avoidance, and landing control without relying on complex path optimization algorithms.

[0055] To adapt to the computing power characteristics of the ARM architecture, multi-level targeted optimizations were carried out at the algorithm implementation level. In terms of computational accuracy, all matrix and convolution operations are processed using fixed-point quantization (INT8) instead of traditional floating-point calculations. This significantly reduces computational load, memory usage, and power consumption while ensuring perception accuracy. At the image processing level, a row-block partitioning parallel strategy is adopted for the binocular vision process. The image is divided into independent processing blocks, and the NEON SIMD instruction set of the ARM platform is called to accelerate intensive operations such as pixel matching and feature extraction at the hardware level, which greatly improves matching efficiency. At the system scheduling level, the control module adopts an event-driven mechanism instead of polling and waiting. The decision and planning process is triggered only when the perception result is updated or the instruction arrives, which effectively reduces CPU idle usage and response latency. This ensures that the algorithm can still achieve high-frequency real-time processing and low-latency motion control on low-power platforms such as the ARM Cortex-A series.

[0056] In one feasible implementation, step S40 may include steps B11-B12: Step B11: Generate corresponding obstacle avoidance correction and landing stability correction based on the obstacle position analysis results and the landing position analysis results; It should be noted that the obstacle avoidance correction amount is an instantaneous motion adjustment amount used to adjust the robot's original navigation path, such as lateral offset distance, steering angular velocity correction, or global velocity attenuation coefficient. It is used to generate a local avoidance trajectory in real time when an obstacle threat is detected, ensuring that the robot can safely detour without leaving the navigation target.

[0057] It is understood that the foot stability correction is used to optimize the local adjustment parameters of the foot landing position and the fuselage attitude, such as the longitudinal / lateral fine adjustment of the foot trajectory, the landing timing offset or the body height compensation value, to select stable support points in unstructured terrain and coordinate multi-leg movement sequences in order to improve gait continuity and overall movement stability.

[0058] Step B12: Update the navigation command based on the obstacle avoidance correction and the foot stability correction to obtain the robot motion command.

[0059] It is understood that the robot motion commands may include parameters such as travel speed, turning angle, gait pattern and joint trajectory, which are used to realize integrated control of navigation tracking, dynamic obstacle avoidance and gait adaptive adjustment, and ensure the continuity, stability and real-time response capability of the robot's movement in complex terrain.

[0060] In one feasible implementation, step B12 may include steps C11-C13: Step C11: Obtain weight parameter information; It should be noted that the weight parameter information is a set of predefined configurable coefficients used to dynamically adjust the contribution of each control source. It can be determined through experimental calibration or online learning based on the robot's motion characteristics, terrain conditions and task requirements. It is used to balance the priorities between navigation tracking, obstacle avoidance response and gait stability, thereby achieving smooth, cooperative and adaptive motion control in complex environments.

[0061] Step C12: Based on the weight parameter information, the obstacle avoidance correction amount, the foot stability correction amount, and the navigation command, the robot fusion command is determined. It should be noted that the robot fusion command is a composite control command used to control the robot's motion posture and trajectory. It integrates navigation intent, obstacle avoidance requirements and gait stability requirements in the speed and steering dimensions. It is a decision result of the collaborative output of navigation tracking, dynamic obstacle avoidance and terrain adaptive adjustment. Through the efficient integration and real-time coordination of multiple control sources on a limited computing power platform, it ensures that the robot can achieve smooth, stable and timely integrated motion control in complex environments.

[0062] Step C13: Update the corresponding robot motion mode according to the robot fusion instruction to obtain the robot motion instruction.

[0063] It is understandable that the robot's movement mode is the specific movement strategy and execution mode adopted by quadruped and wheeled robots to adapt to different terrain conditions and environmental constraints, based on fused commands. This can include the selection of gait mode or movement mode. The gait mode can be walking, trotting, and crawling, and the movement mode can be wheel rolling and legged obstacle crossing. This covers a series of low-level control parameters such as the movement trajectory of each joint, timing coordination, foot contact force planning, and body posture adjustment within the gait cycle. By parsing high-level motion commands into specific and executable joint motor control signals, comprehensive motion goals such as navigation, obstacle avoidance, and stable footing are achieved.

[0064] Step S50: Control the robot to move, avoid obstacles, or adjust its gait based on the robot motion commands.

[0065] Understandably, the system can parse robot motion commands and execute specific actions such as robot movement, obstacle avoidance, or gait adjustment. Movement involves driving the robot to make basic displacements according to the speed and direction specified in the command. Obstacle avoidance involves real-time avoidance and bypassing of obstacles based on the path correction amount included in the command during movement. Gait adjustment involves dynamically optimizing and switching the gait mode, support phase timing, and joint movements of quadrupedal or wheeled robots based on the foot trajectory and posture parameters in the command, thereby achieving unified control of motion stability, efficiency, and adaptability in complex terrain.

[0066] In a specific embodiment, the robot's motion commands, including speed, direction, and gait parameters, can be analyzed to drive the robot's actuators in real time. In unobstructed, flat areas, the navigation component in the commands dominates, enabling precise path tracking and movement. When an obstacle is detected, obstacle avoidance correction is triggered, and the commands dynamically adjust the trajectory or speed to bypass or stop the obstacle. In unstructured terrain, foot stability correction further adjusts the foot landing point and gait timing to ensure stable support and smooth gait. This achieves closed-loop collaborative control of navigation, obstacle avoidance, and gait adjustment, enabling the robot to dynamically and adaptively perform safe, continuous, and efficient movements based on the environment.

[0067] This embodiment proposes a motion control method for a quadrupedal and wheeled robot. The method involves acquiring navigation commands, environmental image information, and camera intrinsic parameters. A corresponding local depth map is constructed based on the environmental image information and the camera intrinsic parameters. The positions of obstacles and landing positions are analyzed based on the local depth map to determine the obstacle position analysis results and the landing position analysis results. The navigation commands are updated based on the obstacle position analysis results and the landing position analysis results to generate robot motion commands. The robot's movement, obstacle avoidance, or gait adjustment is controlled based on these motion commands. This method solves the technical problem of how to perform more efficient and stable motion control of quadrupedal and wheeled robots. Compared with existing technologies, this application constructs a local depth map to simultaneously analyze obstacles and landing areas, and directly integrates the corresponding analysis results into the navigation commands. This generates robot motion commands that integrate obstacle avoidance and gait adjustment in real time, meeting the real-time response requirements in dynamic environments. It achieves coordination between motion intention and terrain perception, enabling the robot to autonomously select efficient and stable movement strategies in both flat and complex terrains, significantly improving the motion continuity and overall stability in unstructured terrain.

[0068] Based on the first embodiment of this application, in the second embodiment of this application, the same or similar content as the first embodiment can be referred to the above description, and will not be repeated hereafter.

[0069] In this embodiment, refer to Figure 2 , Figure 2 The flowchart provided for Embodiment 2 of the motion control method for the quadrupedal and wheeled robot of this application shows that step S30 specifically includes steps S31 to S33: Step S31: Obtain the gradient change rate and the target depth threshold; It should be noted that the gradient change rate can characterize the rate or magnitude of change of depth value on a two-dimensional plane, and is used to quantify the steepness of the terrain surface. The target depth threshold can distinguish between passable and avoidable depth ranges. When used in conjunction with the gradient change rate, it can quickly identify terrain abrupt changes or obstacle areas that exceed the robot's ability to pass through in lightweight computing.

[0070] In a specific embodiment, the gradient change rate can be set by pre-configured empirical parameters or by a coefficient that is adaptively adjusted based on the robot's movement speed. The target depth threshold is a depth threshold that is pre-set based on the robot's mechanical dimensions, obstacle-crossing ability, and safety distance requirements, and is set based on the maximum height or depth change that can be passed in front of the robot as the origin.

[0071] Step S32: Analyze the obstacle position based on the local depth map, the gradient change rate, and the target depth threshold to determine the obstacle position analysis result; It is understood that the obstacle location analysis results are the spatial locations of areas in the environment that are impassable or have a risk of collision, such as boundary coordinates and distances from the robot, identified by performing first-order difference operations on the local depth map using the gradient change rate and the target depth threshold and calculating the obstacle confidence.

[0072] In a specific embodiment, a first-order difference operation is performed on the local depth map based on the gradient change rate and the target depth threshold to determine the local height change of the obstacle. That is, after obtaining the local depth map, this invention employs a lightweight obstacle avoidance criterion based on the depth threshold and the gradient change rate. (Regarding the depth map...) Perform a first-order difference operation to obtain the local height change:

[0073] The obstacle confidence level is determined based on the local height change of the obstacle; the obstacle position is then analyzed based on the obstacle confidence level to determine the obstacle position analysis result, i.e., the obstacle confidence level is defined. :

[0074] when At that time, it was considered that the area was flat and passable; when When this happens, the obstacle avoidance strategy is triggered.

[0075] It avoids complex 3D reconstruction and voxelization processes, requiring only a small amount of difference and exponential operations, making it suitable for real-time execution on edge platforms.

[0076] In one feasible implementation, step S32 may include steps D11 to D13: Step D11: Perform a first-order difference operation on the local depth map based on the gradient change rate and the target depth threshold to determine the local height change of the obstacle; It should be noted that the local height change of the obstacle is a value obtained by performing a first-order difference operation on adjacent pixels or regions in the local depth map, such as calculating the depth difference in the horizontal and vertical directions, to quantify the degree of height change of the terrain surface at the microscale.

[0077] It is understood that the local height change of the obstacle can characterize the depth difference between a point in the depth map and its neighboring points, and is used to determine whether there are obstacle features such as steep slopes, protrusions or depressions.

[0078] Step D12: Determine the corresponding obstacle confidence level based on the local height change of the obstacle; It should be noted that the obstacle confidence score is a probability value or scoring index used to quantify the possibility that a certain area is an obstacle. Its value ranges from 0 to 1. The higher the value, the greater the confidence that there is an obstacle in the area. This confidence score is directly compared with a preset threshold to determine the location of the obstacle.

[0079] Step D13: Analyze the corresponding obstacle positions based on the obstacle confidence level to determine the obstacle position analysis results.

[0080] Understandably, the obstacle confidence level can be compared with a preset obstacle determination threshold, the local depth map can be binarized and segmented to generate an initial obstacle region, and noise can be eliminated and the region contour smoothed. The boundaries and centroids of each independent obstacle region can be extracted. Then, by combining the transformation relationship between the camera coordinate system and the robot base coordinate system, the precise position of each obstacle region relative to the robot can be calculated, thereby obtaining the obstacle position analysis results.

[0081] Step S33: Analyze the landing position based on the target landing area corresponding to the local depth map, and determine the landing position analysis result.

[0082] It is understood that the foot placement analysis result is a set of points and their coordinates that can provide safe and stable support for the robot's feet, which are selected by using the same local depth map, detecting in the target foot placement area through a sliding window, and evaluating the foot placement confidence by calculating the height variance and ground normal information.

[0083] In a specific embodiment, a sliding window is used to detect the target landing area in the local depth map to determine the target landing area information; based on the target landing area information, the height variance information and ground normal information corresponding to each area are calculated. That is, in the same depth map, a sliding window is used to detect candidate landing areas, and by combining ground normal estimation and height variance constraints, a set of possible landing points is quickly selected. The local height variance is determined as follows:

[0084] like Then the area is flat; Next, determine the ground normal constraint, which is obtained from the local gradient and expressed as:

[0085] like If so, then it is considered acceptable to settle down; Based on the height variance information and the ground normal information, the corresponding landing confidence level is calculated. Then, based on the possible landing points corresponding to the landing confidence level, the landing positions are analyzed to obtain the landing position analysis results, i.e., the overall confidence level is calculated.

[0086] Select the one that satisfies The point is used as a landing point.

[0087] It can be implemented entirely on a 2D depth map without the need for large-scale 3D fitting, and its computational complexity is significantly lower than that of traditional point cloud normal estimation.

[0088] In one feasible implementation, step S33 may include steps E11 to E14: Step E11: Use a sliding window to detect the target landing area of ​​the local depth map and determine the target landing area information; It should be noted that the target landing area information is a set of candidate areas that are initially screened as having potential landing possibilities after scanning and detecting on the local depth map by a sliding window.

[0089] It is understood that the target landing area information may include the center coordinates, spatial range, height variance, and ground normal vector of each candidate area, which are used to determine whether the terrain is suitable for robot foot contact, thereby selecting safe and stable landing points for gait planning.

[0090] Step E12: Calculate the height variance information and ground normal information corresponding to each region based on the target landing area information; It should be noted that the height variance information is a statistical measure used to quantify the flatness of the terrain surface by calculating the average of the squared deviations between the depth values ​​of each point in the candidate landing area of ​​the local depth map and its average depth. The smaller the value, the flatter the terrain in the area, and the more suitable it is as a stable landing support point.

[0091] It is understood that the ground normal information is a unit vector used to describe the direction and degree of the surface tilt of the region by calculating the gradient of the depth value of the same candidate area in the horizontal and vertical directions, combining them into a vector in three-dimensional space and then normalizing it. The angle between the normal vector and the gravity direction is evaluated to determine whether the ground is in a safe and footholdable horizontal or gentle slope range.

[0092] Step E13: Calculate the corresponding landing confidence level based on the height variance information and the ground normal information; It should be noted that the foot confidence level is used to quantify the reliability of the area as a stable support point for the robot's foot. Its value ranges from 0 to 1. The higher the value, the flatter the ground and the closer the inclination is to horizontal, and therefore the greater the possibility of it being a safe footing point. By setting a confidence level threshold, a set of footing points that can be used for actual gait planning can be selected.

[0093] Step E14: Analyze the landing positions based on the landing confidence level corresponding to the available landing points to obtain the landing position analysis results.

[0094] Understandably, by setting a confidence threshold, we can filter out the landing points that are above the threshold from all candidate points, analyze these points, remove isolated outliers and merge neighboring areas to form a number of stable candidate landing sets. At this point, by combining robot gait parameters, such as stride length and foot workspace, we can evaluate the center position of the landing point and generate the corresponding landing position analysis results.

[0095] This embodiment proposes a motion control method for a quadrupedal and wheeled robot, which involves obtaining the gradient change rate and the target depth threshold; analyzing the obstacle position based on the local depth map, the gradient change rate, and the target depth threshold to determine the obstacle position analysis result; and analyzing the landing position based on the target landing area corresponding to the local depth map to determine the landing position analysis result. This invention addresses the technical challenge of achieving more efficient and stable motion control for quadrupedal and wheeled robots. Compared to existing technologies, this application utilizes the gradient rate of change and a target depth threshold to perform parallel analysis on the same local depth map. For obstacle analysis, a first-order difference operation is performed on the depth map based on both parameters to extract local height changes, thereby calculating obstacle confidence and determining the precise location and contour of obstacles. For landing point analysis, within a pre-defined target landing area, sliding window detection, height variance calculation, and ground normal estimation are combined to assess the flatness and tilt of the area, thereby calculating landing confidence and determining the set of possible landing points. By sharing the same depth map data source and employing lightweight difference and statistical algorithms, computational redundancy and memory consumption are significantly reduced, meeting the high-frequency real-time processing requirements of edge platforms. Furthermore, the parameterized design of the gradient rate of change and depth threshold allows the system to flexibly adapt to different terrain and obstacle features, enabling obstacle avoidance decisions and gait planning to be coordinated at the source, thereby enhancing the continuity, stability, and overall safety of robot movement in complex terrain.

[0096] This application also provides a motion control device for a quadrupedal and wheeled robot. Please refer to [reference needed]. Figure 3 The motion control device for the quadrupedal and wheeled robot includes: The acquisition module 10 is used to acquire navigation instructions, environmental image information, and camera intrinsic parameter information; Processing module 20 is used to construct a corresponding local depth map based on the environmental image information and the camera intrinsic parameter information; The processing module 20 is also used to analyze the corresponding obstacle position and landing position based on the local depth map, and determine the obstacle position analysis result and the landing position analysis result; Execution module 30 is used to update the navigation instructions based on the obstacle position analysis results and the foot position analysis results, and generate robot motion instructions; The execution module 30 is also used to control the robot's movement, obstacle avoidance, or gait adjustment based on the robot's motion commands.

[0097] The processing module 20 is further configured to downsample the environmental image information to determine the corresponding sparse key point feature set; The disparity of the sparse keypoint feature set is calculated using local block matching to determine the disparity information; A corresponding local depth map is constructed based on the camera intrinsic information and the parallax information.

[0098] The processing module 20 is also used to obtain the gradient change rate and the target depth threshold; The obstacle position is analyzed based on the local depth map, the gradient change rate, and the target depth threshold to determine the obstacle position analysis result; The landing position is analyzed based on the target landing area corresponding to the local depth map, and the landing position analysis result is determined.

[0099] The processing module 20 is further configured to perform a first-order difference operation on the local depth map based on the gradient change rate and the target depth threshold to determine the local height change of the obstacle; The corresponding obstacle confidence level is determined based on the local height change of the obstacle. Based on the obstacle confidence level, the corresponding obstacle positions are analyzed to determine the obstacle position analysis results.

[0100] The processing module 20 is also used to detect the target landing area of ​​the local depth map using a sliding window and determine the target landing area information; Calculate the height variance and ground normal information for each region based on the target landing area information; Calculate the corresponding landing confidence level based on the height variance information and the ground normal information; The landing positions are analyzed based on the landing confidence level corresponding to the available landing points, and the landing position analysis results are obtained.

[0101] The execution module 30 is also used to generate corresponding obstacle avoidance correction and landing stability correction based on the obstacle position analysis results and the landing position analysis results; The navigation commands are updated based on the obstacle avoidance correction and the foot stability correction to obtain robot motion commands.

[0102] The execution module 30 is also used to obtain weight parameter information; The robot fusion command is determined by fusing the weight parameter information, the obstacle avoidance correction amount, the foot stability correction amount, and the navigation command. The corresponding robot motion mode is updated according to the robot fusion instruction to obtain the robot motion instruction.

[0103] The motion control device for quadrupedal and wheeled robots provided in this application employs the motion control method for quadrupedal and wheeled robots described in the above embodiments, and can solve the technical problem of how to perform motion control of quadrupedal and wheeled robots more efficiently and stably. Compared with the prior art, the beneficial effects of the motion control device for quadrupedal and wheeled robots provided in this application are the same as those of the motion control method for quadrupedal and wheeled robots provided in the above embodiments, and other technical features in the motion control device for quadrupedal and wheeled robots are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0104] This application provides a motion control device for a quadrupedal and wheeled robot. The motion control device for the quadrupedal and wheeled robot includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the motion control method for the quadrupedal and wheeled robot in the first embodiment described above.

[0105] The following is for reference. Figure 4 This document illustrates a schematic diagram of a motion control device suitable for implementing the embodiments of the quadrupedal and wheeled robot of this application. The motion control device for the quadrupedal and wheeled robot in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 4 The motion control device for the quadrupedal and wheeled robot shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0106] like Figure 4As shown, the motion control device of a quadrupedal and wheeled robot may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to programs stored in ROM (Read Only Memory) 1002 or programs loaded from storage device 1003 into RAM (Random Access Memory) 1004. RAM 1004 also stores various programs and data required for the operation of the motion control device of the quadrupedal and wheeled robot. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via bus 1005. Input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the motion control equipment of quadrupedal and wheeled robots to communicate wirelessly or wiredly with other devices to exchange data. Although the figures show motion control equipment for quadrupedal and wheeled robots with various systems, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented alternatively.

[0107] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0108] The motion control device for quadrupedal and wheeled robots provided in this application, employing the motion control method for quadrupedal and wheeled robots described in the above embodiments, can solve the technical problem of how to perform motion control of quadrupedal and wheeled robots more efficiently and stably. Compared with the prior art, the beneficial effects of the motion control device for quadrupedal and wheeled robots provided in this application are the same as those of the motion control method for quadrupedal and wheeled robots provided in the above embodiments, and other technical features in the motion control device for quadrupedal and wheeled robots are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0109] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0110] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0111] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the motion control method of the quadrupedal and wheeled robot in the above embodiments.

[0112] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0113] The aforementioned computer-readable storage medium may be included in the motion control device of a quadrupedal and wheeled robot; or it may exist independently and not be assembled into the motion control device of a quadrupedal and wheeled robot.

[0114] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by the motion control device of the quadrupedal and wheeled robot, the motion control device of the quadrupedal and wheeled robot causes the following: it acquires navigation commands, environmental image information, and camera intrinsic parameter information; it constructs a corresponding local depth map based on the environmental image information and the camera intrinsic parameter information; it analyzes the corresponding obstacle positions and foot placement positions based on the local depth map, and determines the obstacle position analysis results and foot placement position analysis results; it updates the navigation commands based on the obstacle position analysis results and the foot placement position analysis results, generating robot motion commands; and it controls the robot to move, avoid obstacles, or adjust its gait based on the robot motion commands.

[0115] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0116] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0117] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0118] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., computer programs) for executing the motion control method of the quadrupedal and wheeled robot described above. This solves the technical problem of how to perform motion control of quadrupedal and wheeled robots more efficiently and stably. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the motion control method for quadrupedal and wheeled robots provided in the above embodiments, and will not be repeated here.

[0119] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A motion control method for a quadrupedal and wheeled robot, characterized in that, The method includes: Acquire navigation instructions, environmental image information, and camera intrinsic parameter information; A corresponding local depth map is constructed based on the environmental image information and the camera intrinsic parameter information; Based on the local depth map, the corresponding obstacle positions and landing positions are analyzed to determine the obstacle position analysis results and landing position analysis results; The navigation instructions are updated based on the obstacle position analysis results and the foot position analysis results to generate robot movement instructions; The robot's movement, obstacle avoidance, or gait adjustment are controlled based on the aforementioned robot motion commands.

2. The method as described in claim 1, characterized in that, The step of constructing a corresponding local depth map based on the environmental image information and the camera intrinsic parameter information includes: The environmental image information is downsampled to determine the corresponding sparse key point feature set; The disparity of the sparse keypoint feature set is calculated using local block matching to determine the disparity information; A corresponding local depth map is constructed based on the camera intrinsic information and the parallax information.

3. The method as described in claim 1, characterized in that, The step of analyzing the corresponding obstacle positions and landing positions based on the local depth map to determine the obstacle position analysis results and landing position analysis results includes: Obtain the gradient change rate and target depth threshold; The obstacle position is analyzed based on the local depth map, the gradient change rate, and the target depth threshold to determine the obstacle position analysis result; The landing position is analyzed based on the target landing area corresponding to the local depth map, and the landing position analysis result is determined.

4. The method as described in claim 3, characterized in that, The step of analyzing the obstacle position based on the local depth map, the gradient change rate, and the target depth threshold to determine the obstacle position analysis result includes: Based on the gradient change rate and the target depth threshold, a first-order difference operation is performed on the local depth map to determine the local height change of the obstacle. The corresponding obstacle confidence level is determined based on the local height change of the obstacle. Based on the obstacle confidence level, the corresponding obstacle positions are analyzed to determine the obstacle position analysis results.

5. The method as described in claim 3, characterized in that, The step of analyzing the landing position based on the target landing area corresponding to the local depth map and determining the landing position analysis result includes: The target landing area is detected using a sliding window in the local depth map to determine the target landing area information; Calculate the height variance and ground normal information for each region based on the target landing area information; Calculate the corresponding landing confidence level based on the height variance information and the ground normal information; The landing positions are analyzed based on the landing confidence level corresponding to the available landing points, and the landing position analysis results are obtained.

6. The method as described in claim 1, characterized in that, The step of updating the navigation commands and generating robot motion commands based on the obstacle position analysis results and the foot position analysis results includes: Based on the obstacle position analysis results and the landing position analysis results, corresponding obstacle avoidance correction and landing stability correction are generated. The navigation commands are updated based on the obstacle avoidance correction and the foot stability correction to obtain robot motion commands.

7. The method as described in claim 6, characterized in that, The step of updating the navigation commands based on the obstacle avoidance correction and the foot stability correction to obtain robot motion commands includes: Obtain weight parameter information; The robot fusion command is determined by fusing the weight parameter information, the obstacle avoidance correction amount, the foot stability correction amount, and the navigation command. The corresponding robot motion mode is updated according to the robot fusion instruction to obtain the robot motion instruction.

8. A motion control device for a quadrupedal and wheeled robot, characterized in that, The device includes: The acquisition module is used to acquire navigation instructions, environmental image information, and camera intrinsic parameter information; The processing module is used to construct a corresponding local depth map based on the environmental image information and the camera intrinsic parameter information; The processing module is also used to analyze the corresponding obstacle positions and landing positions based on the local depth map, and determine the obstacle position analysis results and landing position analysis results; An execution module is used to update the navigation instructions based on the obstacle position analysis results and the foot position analysis results, and generate robot motion instructions; The execution module is also used to control the robot's movement, obstacle avoidance, or gait adjustment based on the robot's motion commands.

9. A motion control device for a quadrupedal and wheeled robot, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the motion control method for a quadrupedal and wheeled robot as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the motion control method for the quadrupedal and wheeled robot as described in any one of claims 1 to 7.

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