A robot walking stability control method, device, equipment and storage medium

Abstract

This invention discloses a method, apparatus, device, and storage medium for robot walking stability control. The method includes: acquiring environmental point cloud data, target walking direction, and motion posture data corresponding to the target robot; determining the target landing point and target terrain feature parameters based on a preset walking stride length, a preset maximum stride length, environmental point cloud data, target walking direction, and motion posture data; determining the target landing gait parameters of the target robot based on the target landing point and target terrain feature parameters; and determining target joint control commands based on the robot's dynamics model and the target landing gait parameters. This invention enables stable control of robot walking, improves adaptability to complex terrain, enhances the robot's intelligence in landing point selection and gait generation, and ensures the robot's walking stability, safety, and adaptability in complex environments, thereby improving robot mobility efficiency and reducing energy consumption and task failure risk.

Description

Technical Field

[0001] This invention relates to the field of robot walking control technology, and in particular to a robot walking stability control method, device, equipment and storage medium. Background Technology

[0002] Robotic walking is a key capability for mobile robots to perform complex tasks such as field exploration and disaster relief. Its walking stability directly determines the robot's operating range, mobility efficiency, and safety.

[0003] Currently, traditional robot walking stability control methods mainly rely on foot force sensors, joint torque sensors, or inertial measurement units (IMUs) to provide feedback and correction after the robot has made contact or deviated from its posture. However, traditional robot walking stability control methods have poor adaptability to complex terrain and lack precision and intelligence in foot placement, terrain-adaptive gait generation, and overall dynamic coordination control. This fails to guarantee the robot's walking stability, safety, and adaptability in complex environments, resulting in low robot mobility, excessive energy consumption, and a higher risk of mission failure. Summary of the Invention

[0004] This invention provides a robot walking stability control method, device, equipment, and storage medium to achieve stable control of robot walking, improve adaptability to complex terrain, enhance the robot's intelligence in foothold selection and gait generation, ensure the robot's walking stability, safety, and adaptability in complex environments, thereby improving robot mobility efficiency and reducing energy consumption and mission failure risk.

[0005] According to one aspect of the present invention, a robot walking stability control method is provided, the method comprising: Acquire environmental point cloud data, target walking direction, and motion posture data corresponding to the target robot; Based on the preset walking step length, preset maximum step length, environmental point cloud data, target walking direction and motion posture data of the target robot, the target landing point of the target robot in the next step and the target terrain feature parameters corresponding to the target landing point are determined. Based on the target landing point and the target terrain feature parameters corresponding to the target landing point, the target landing gait parameters corresponding to the target robot are determined; Based on the robot dynamics model corresponding to the target robot and the target landing gait parameters, the target joint control command corresponding to the target robot is determined, so as to perform walking stability control on the target robot based on the target joint control command.

[0006] According to another aspect of the present invention, a robot walking stability control device is provided, the device comprising: The data acquisition module is used to acquire environmental point cloud data, target walking direction and motion posture data corresponding to the target robot; The terrain parameter determination module is used to determine the target landing point of the target robot's next step and the target terrain feature parameters corresponding to the target landing point based on the preset walking step length, preset maximum step length, environmental point cloud data, target walking direction and motion posture data corresponding to the target robot; The gait parameter determination module is used to determine the target landing gait parameters corresponding to the target robot based on the target landing point and the target terrain feature parameters corresponding to the target landing point. The walking stability control module is used to determine the target joint control command corresponding to the target robot based on the robot dynamics model corresponding to the target robot and the target landing gait parameters, so as to perform walking stability control on the target robot based on the target joint control command.

[0007] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the robot walking stability control method according to any embodiment of the present invention.

[0008] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the robot walking stability control method according to any embodiment of the present invention.

[0009] The technical solution of this invention, by acquiring environmental point cloud data, target walking direction, and motion posture data corresponding to the target robot, can provide comprehensive robot environmental data and walking state data, providing a reliable data foundation for subsequent steps. Based on the preset walking step length, preset maximum step length, environmental point cloud data, target walking direction, and motion posture data corresponding to the target robot, the target landing point of the target robot's next movement and the target terrain feature parameters corresponding to the target landing point are determined, thereby achieving advance assessment of the terrain, actively avoiding unstable areas, and improving the robot's walking stability. Based on the target landing point and the target terrain feature parameters corresponding to the target landing point, the target landing foot posture parameters corresponding to the target robot are determined, which can significantly improve the robot's landing stability, reduce disturbances, and improve the robot's walking stability. Based on the robot dynamics model corresponding to the target robot and the target landing foot posture parameters, the target joint control commands corresponding to the target robot are determined, and the walking stability control of the target robot is performed based on the target joint control commands, greatly improving the robot's walking stability in different terrain environments. This invention acquires environmental point cloud data, target walking direction, and motion posture data corresponding to the target robot, and combines them with preset walking stride length and preset maximum stride length to determine the target landing gait parameters of the robot, generate target joint control commands, and achieve precise control of the robot's stable walking. This greatly improves the accuracy and intelligence in landing point selection, terrain-adaptive gait generation, and walking control, ensuring the robot's walking stability, safety, and adaptability in complex environments, thereby improving the robot's mobility efficiency and reducing energy consumption and the risk of mission failure.

[0010] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0012] Figure 1 This is a flowchart of a robot walking stability control method according to Embodiment 1 of the present invention; Figure 2 This is a flowchart of a robot walking stability control method according to Embodiment 2 of the present invention; Figure 3This is a schematic diagram of a robot walking stability control device according to Embodiment 3 of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device that implements the robot walking stability control method of the present invention. Detailed Implementation

[0013] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0014] It should be noted that the terms "first," "second," "target," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0015] Example 1 Figure 1 This is a flowchart illustrating a robot walking stability control method according to Embodiment 1 of the present invention. This embodiment is applicable to controlling robot walking stability under various terrain conditions. The method can be executed by a robot walking stability control device, which can be implemented in hardware and / or software. This robot walking stability control device can be configured in an electronic device. For example... Figure 1 As shown, the method includes: S110. Acquire the environmental point cloud data, target walking direction and motion posture data corresponding to the target robot.

[0016] The target robot can refer to a robot that needs to control its movement to perform a specific task, such as a humanoid robot. Environmental point cloud data can refer to a dataset describing the three-dimensional geometric information of the robot's surrounding environment. The target walking direction can refer to the robot's current walking direction or desired walking direction. Motion posture data can refer to a set of physical quantities describing the robot's real-time motion state, which may include: body posture, foot state, joint state, etc.

[0017] Specifically, multiple depth cameras and / or LiDARs positioned around the target robot and / or near its lower limbs can acquire 3D environmental information in front of and to the sides of the robot. Noise points are removed using point cloud filtering methods such as statistical filtering, and the denoised 3D environmental information is then segmented into ground clusters based on the DBSCAN point cloud clustering algorithm, distinguishing between ground and non-ground areas to determine the environmental point cloud data of the ground area where the target robot is located. Based on the target robot's path planner and inertial sensors, the target robot's walking direction and motion posture data are acquired. By deploying sensors around the robot, omnidirectional perception of complex terrain and obstacles is achieved, avoiding blind spots caused by a single viewpoint. This provides accurate environmental and pose data for subsequent decision-making, forming the data foundation for robot stability control.

[0018] S120. Based on the preset walking step length, preset maximum step length, environmental point cloud data, target walking direction and motion posture data corresponding to the target robot, determine the target landing point of the target robot's next step and the target terrain feature parameters corresponding to the target landing point.

[0019] The preset walking stride length refers to the pre-set walking stride length of the robot when performing a task. The preset maximum stride length refers to the physically permissible maximum stride length determined by the robot's own mechanical structure (leg length, joint range of motion) and stability limits. The target landing point refers to the optimal landing point for stability, derived by comprehensively considering the landing stability of the target robot. The target terrain feature parameters refer to quantitative parameters used to describe the geometric and physical characteristics of the target landing point.

[0020] Specifically, based on the target robot's preset walking stride length, preset maximum stride length, and target walking direction, combined with the foot position in the motion posture data, the possible landing area of ​​the target robot can be determined, and the possible landing area of ​​the target robot can be sampled uniformly. It should be noted that the number of samples is positively correlated with the size of the possible landing area, and the landing area range is different for robots of different sizes. Based on environmental point cloud data, a comprehensive stability analysis is performed on each sampling point. For example, the distance between each sampling point and a preset landing point can be determined. The preset landing point refers to the expected landing point where the target robot will walk according to a preset walking step length and target walking direction. For each sampling point, the smaller the distance from the preset landing point, the higher the score; conversely, the larger the distance from the preset landing point, the lower the score. At the same time, the stability of each sampling point is analyzed, that is, whether there are any protrusions or depressions in the target sampling point. If they exist, the score is 0. If they do not exist, the inclination of the ground on which the sampling point is located is further determined. The steeper the ground, the lower the score. The distance score and stability score of each sampling point are fused to obtain a comprehensive score for each sampling point. At the same time, the comprehensive score of the preset landing point is determined using the same analysis method (the distance score of the preset landing point is the highest score by default). The point with the highest comprehensive score is the target landing point. After determining the target landing point for the robot's next movement based on the analysis results, the parameters such as the ground height and tilt angle corresponding to the target landing point are recorded, which are the target terrain feature parameters. This enables advance assessment of the terrain, allowing the robot to proactively avoid unstable areas and improve its walking stability.

[0021] S130. Based on the target landing point and the target terrain feature parameters corresponding to the target landing point, determine the target landing dynamic parameters corresponding to the target robot.

[0022] Among them, the target landing foot dynamic parameters can refer to the detailed motion instructions planned to allow the foot to safely and stably reach and contact the target landing point, which may include parameters such as landing position, foot posture angle, stride length, stride width, foot lift height, support phase and swing phase timing.

[0023] Specifically, based on the principles of inverse kinematics and foot trajectory planning, the target landing point and the corresponding terrain feature parameters can be transformed. For example, the target landing point can be directly determined as the landing position of the target robot. Based on the landing position, the target robot's stride length, stride width, and foot lift height can be determined. To ensure full contact between the robot's foot and the ground at the target landing point, increasing stability and grip, the foot coordinate system needs to be aligned with the ground. That is, the target robot's foot posture angle can be determined based on the tilt angle of the target landing point. The target robot's landing position and foot posture angle can be used as inputs for inverse kinematics, substituted into the kinematic model of the robot's legs, and the target angles of each joint can be solved. This allows the foot to accurately reach the target pose, ultimately obtaining the target landing gait parameters of the target robot. This greatly improves the adaptability to complex walking terrain, reduces the reliance on traditional force feedback compensation, and improves the stability and safety of robot walking.

[0024] S140. Based on the robot dynamics model and target landing gait parameters corresponding to the target robot, determine the target joint control commands corresponding to the target robot, so as to perform walking stability control on the target robot based on the target joint control commands.

[0025] The robot dynamics model can refer to a physical model that describes the mathematical relationship between the robot's motion and the forces acting on it. Its constraints include: floating base dynamics, zero-torque point (ZMP) constraints, contact constraints, friction cone constraints, joint position constraints, joint velocity constraints, joint acceleration constraints, and joint torque constraints. The target joint control command can refer to the low-level control signals ultimately sent to each joint actuator (usually a motor) of the robot.

[0026] Specifically, the target robot's foot gait parameters can be input into the corresponding robot dynamics model to generate instructions. The robot dynamics model performs inverse calculations on the expected trajectory of the foot corresponding to the target foot gait parameters based on inverse dynamics. That is, in order to accurately track the expected trajectory, the model outputs the feedforward compensation torque that each joint theoretically needs to provide, which is the target joint control instruction corresponding to the target robot. The target joint control instruction is sent to the servo driver corresponding to the target robot to drive the motor to execute the motion instruction, thereby achieving precise control of the target robot's walking stability, improving the robot's adaptability to complex environments, and ensuring the reliability and safety of the robot's walking.

[0027] In this embodiment, by acquiring the environmental point cloud data, target walking direction, and motion posture data corresponding to the target robot, comprehensive robot environmental and walking state data can be provided, offering a reliable data foundation for subsequent steps. Based on the target robot's preset walking stride length, preset maximum stride length, environmental point cloud data, target walking direction, and motion posture data, the target landing point of the target robot's next movement and the corresponding target terrain feature parameters are determined. This allows for advance terrain assessment, enabling proactive avoidance of unstable areas and improving robot walking stability. Based on the target landing point and the corresponding target terrain feature parameters, the target landing gait parameters of the target robot are determined, significantly improving landing stability, reducing disturbances, and enhancing overall walking stability. Based on the robot's dynamics model and target landing gait parameters, the target joint control commands are determined. Walking stability control of the target robot based on these target joint control commands greatly improves walking stability in various terrain environments. This invention acquires environmental point cloud data, target walking direction, and motion posture data corresponding to the target robot, and combines them with preset walking stride length and preset maximum stride length to determine the target landing gait parameters of the robot, generate target joint control commands, and achieve precise control of the robot's stable walking. This greatly improves the accuracy and intelligence in landing point selection, terrain-adaptive gait generation, and walking control, ensuring the robot's walking stability, safety, and adaptability in complex environments, thereby improving the robot's mobility efficiency and reducing energy consumption and the risk of mission failure.

[0028] Example 2 Figure 2 This is a flowchart of a robot walking stability control method provided in Embodiment 2 of the present invention. Based on the above embodiments, this embodiment optimizes the step of "determining the target landing point of the target robot's next movement and the target terrain feature parameters corresponding to the target landing point based on the target robot's preset walking step length, preset maximum step length, environmental point cloud data, target walking direction, and motion posture data". Explanations of terms that are the same as or corresponding to those in the above embodiments are not repeated here.

[0029] See Figure 2 The alternative robot walking stability control method provided in this embodiment specifically includes the following steps: S210. Acquire the environmental point cloud data, target walking direction and motion posture data corresponding to the target robot.

[0030] S220. Based on the target robot's preset walking step length, preset maximum step length, target walking direction, and motion posture data, determine multiple candidate landing points for the target robot's next step.

[0031] Among them, the candidate landing point can refer to the possible landing point of the target robot in the next step.

[0032] Specifically, the walking support points of the target robot can be determined based on the target robot's motion posture data. For example, if the target robot is a humanoid robot, the walking support point can be the landing point of the target robot in the previous step. Based on the target robot's corresponding walking direction, a geometric region is defined within the range of the preset maximum step length, centered on the preset walking step length. This region can be fan-shaped, circular, or rectangular. This region is the possible landing area of ​​the target robot. The possible landing areas of the target robot can be uniformly or randomly sampled at certain intervals to determine multiple candidate landing points for the target robot's next step. This transforms the infinite ground space into a finite set of landing points, providing a reliable and comprehensive data foundation for subsequent steps while reducing the amount of computation.

[0033] For example, S220 may include: determining the historical landing point of the target robot's previous step based on motion posture data; determining the target landing area of ​​the target robot's next step based on the target robot's preset walking step length, preset maximum step length, target walking direction, and historical landing point; sampling the target landing area to determine multiple sampling points, and determining the multiple sampling points and the center point corresponding to the target landing area as multiple candidate landing points for the target robot's next step.

[0034] Here, "historical landing point" can refer to the three-dimensional spatial coordinates of the actual contact point between the other leg ends of the target robot, which are used to support the leg currently being planned to swing, and the ground. "Target landing area" can refer to the area where the robot may land in the next planned phase.

[0035] Specifically, based on motion posture data, the historical landing point of the target robot's previous step can be determined. Starting from this historical landing point, a predetermined step length can be moved along the target walking direction vector to obtain a desired landing point, which is the center point of the target landing area. A circular area with a predetermined radius can be generated around this point, which is the target landing area for the robot's next step. It should be noted that this target landing area does not exceed the area that the robot can reach with its predetermined maximum step length. Discretized random sampling is performed on the target landing area to determine multiple sampling points. These multiple sampling points and the corresponding center point of the target landing area are determined as multiple candidate landing points for the robot's next step. By using the previous landing point as a reference, the continuity of the robot's movement is ensured, which helps to generate a stable motion trajectory and provides an accurate and reliable range of landing points for subsequent steps, thus providing a data foundation for the robot's walking stability control.

[0036] For example, based on the preset walking step length, preset maximum step length, target walking direction, and historical landing points of the target robot, the target landing area for the next step of the target robot is determined, including: determining the expected landing point for the next step of the target robot based on the target walking direction and historical landing points of the target robot; determining the radius of the landing area based on the preset walking step length and preset maximum step length; and determining the target landing area for the next step of the target robot based on the expected landing point and the radius of the landing area.

[0037] The expected landing point refers to the anticipated landing location of the target robot, starting from its historical landing point and following the target walking direction and preset walking step length. The landing area radius refers to the possible landing range of the target robot centered on the expected landing point.

[0038] Specifically, starting from the historical landing point, the robot can move a preset walking step length along the target walking direction to obtain the expected landing point for the next step. The difference between the preset maximum step length and the preset walking step length is determined as the landing area radius. The landing area radius defines the range within which the robot can sacrifice efficiency for stability. Then, using the expected landing point as the center, a circle is drawn in combination with the landing area radius to generate the target landing area for the next step of the robot, thus providing a clear and reliable landing point range for subsequent landing point optimization.

[0039] For example, sampling the target landing area to determine multiple sampling points includes: dividing the target landing area to generate multiple annular regions and a circular region where the expected landing point is located; and performing gradient sampling on the multiple annular regions and the circular region where the expected landing point is located based on the distance between the multiple annular regions and the circular region where the expected landing point is located and the expected landing point to determine multiple sampling points.

[0040] Specifically, the radius of the target landing area can be evenly divided to generate multiple annular regions from the inside out, as well as a circular region where the expected landing point is located. Different sampling densities can be set for different regions based on the distance between the multiple annular regions and the circular region where the expected landing point is located and the expected landing point. Gradient sampling is performed on the multiple annular regions and the circular region where the expected landing point is located. It should be noted that the sampling density is higher for regions closer to the expected landing point. Determining multiple sampling points can greatly improve the relevance and reliability of the possible landing point determination, while reducing the number of samples and improving computational efficiency.

[0041] S230. Based on environmental point cloud data, multiple candidate landing points are filtered to determine the target landing point and the target terrain feature parameters corresponding to the target landing point.

[0042] Specifically, each candidate landing point can be associated with environmental point cloud data. Centered on this point, and combined with the environmental point cloud data, terrain feature analysis is performed on each candidate landing point. Based on the terrain feature parameters corresponding to each candidate landing point, a comprehensive stability score is given for each candidate landing point. The candidate landing point with the best stability is selected as the target landing point, and the terrain feature parameters corresponding to the target landing point are determined as the target terrain feature parameters for the target landing point. This enables the robot to actively adapt to the terrain, greatly improving the stability and reliability of the robot's movement.

[0043] For example, S230 may include: for each candidate landing point, determining the candidate terrain feature parameters corresponding to the candidate landing point based on environmental point cloud data, wherein the candidate terrain feature parameters include: ground elevation, slope angle, surface flatness, and supportability; comprehensively scoring each candidate landing point based on a preset scoring strategy and the candidate terrain parameters corresponding to each candidate landing point, and filtering multiple candidate landing points according to the scoring results to determine the target landing point and the target terrain feature parameters corresponding to the target landing point.

[0044] Among these, ground height can refer to the vertical height difference between the candidate landing point and historical landing points. Slope angle can refer to the angle between the local ground plane where the candidate landing point is located and the horizontal plane. Surface smoothness can refer to the degree of microscopic undulation of the local ground surface where the candidate landing point is located relative to its fitted plane; it is a statistical measure of terrain roughness. Supportability can refer to a quantitative parameter used to assess whether the local area where the candidate landing point is located can physically stably support the robot's foot without collapsing, slipping, or severe sliding. It can be obtained by analyzing obstacles or depressions in the landing point area and their dimensions. For example, if the ground slope angle of the landing point is greater than a preset angle (e.g., 45 degrees) or there are protrusions or depressions, the supportability is 0; otherwise, the supportability is 1. The preset scoring strategy can refer to a pre-set strategy used to quantify multiple terrain feature parameters into a comprehensive score to represent the stability degree of each candidate landing point.

[0045] Specifically, for each candidate landing point, planar fitting can be performed on the local environmental point cloud data corresponding to the candidate landing point to determine the ground height, slope angle, surface flatness, and supportability of the candidate landing point, i.e., the candidate terrain feature parameters. According to the preset scoring strategy, the candidate terrain parameters can be normalized, mapping each terrain feature parameter to a unified scoring range (e.g., 0 to 1 points). The normalized terrain feature parameters are then weighted and summed according to preset weights. The comprehensive score result, Total = w1S height + w2S slope + w3S flatness + w4S supportability, is obtained by engineers based on the robot's mechanical design, gait characteristics, and task requirements. Finally, the comprehensive score result of each candidate landing point is determined, and the candidate landing point with the highest comprehensive score result is determined as the target landing point. The candidate terrain feature parameters corresponding to this point are determined as the target terrain feature parameters, realizing intelligent adaptive decision-making for the robot's walking stability and efficiency, which can greatly improve the stability of the robot's walking.

[0046] S240. Based on the target landing point and the target terrain feature parameters corresponding to the target landing point, determine the target landing dynamic parameters corresponding to the target robot.

[0047] S250. Based on the robot dynamics model and target landing gait parameters corresponding to the target robot, determine the target joint control commands corresponding to the target robot, and perform walking stability control on the target robot based on the target joint control commands.

[0048] The technical solution of this embodiment determines multiple candidate landing points for the target robot's next step based on the target robot's preset walking stride length, preset maximum stride length, target walking direction, and motion posture data, providing a reliable and comprehensive data foundation for subsequent steps. Based on environmental point cloud data, multiple candidate landing points are filtered to determine the target landing point and its corresponding terrain feature parameters, improving the robot's adaptability to complex terrain. This invention, by determining multiple candidate landing points and identifying the target landing point with the optimal walking stability and its corresponding terrain feature parameters, enables the robot to intelligently and proactively select the most stable landing point, fundamentally improving the robot's walking stability on complex terrain. This ensures the robot's safety and adaptability in complex environments and increases its mobility.

[0049] Example 3 Figure 3 This is a schematic diagram of a robot walking stability control device provided in Embodiment 3 of the present invention. Figure 3As shown, the device includes: a data acquisition module 310, a terrain parameter determination module 320, a gait parameter determination module 330, and a walking stability control module 340; Among them, the data acquisition module 310 is used to acquire the environmental point cloud data, target walking direction and motion posture data corresponding to the target robot; The terrain parameter determination module 320 is used to determine the target landing point of the target robot's next step and the target terrain feature parameters corresponding to the target landing point based on the preset walking step length, preset maximum step length, environmental point cloud data, target walking direction and motion posture data corresponding to the target robot. The gait parameter determination module 330 is used to determine the target landing gait parameters corresponding to the target robot based on the target landing point and the target terrain feature parameters corresponding to the target landing point; The walking stability control module 340 is used to determine the target joint control command corresponding to the target robot based on the robot dynamics model corresponding to the target robot and the target landing foot state parameters, so as to perform walking stability control on the target robot based on the target joint control command.

[0050] In this embodiment, by acquiring the environmental point cloud data, target walking direction, and motion posture data corresponding to the target robot, comprehensive robot environmental and walking state data can be provided, offering a reliable data foundation for subsequent steps. Based on the preset walking stride length, preset maximum stride length, environmental point cloud data, target walking direction, and motion posture data corresponding to the target robot, the target landing point for the next step and the target terrain feature parameters corresponding to the target landing point are determined. This enables advance assessment of the terrain, allowing proactive avoidance of unstable areas and improving robot walking stability. Based on the target landing point and the corresponding target terrain feature parameters, the target landing gait parameters of the target robot are determined, significantly improving landing stability, reducing disturbances, and enhancing overall walking stability. Based on the robot dynamics model and the target landing gait parameters, the target joint control commands for the target robot are determined. Walking stability control of the target robot is then performed based on these target joint control commands, greatly improving the robot's walking stability in different terrain environments. This invention acquires environmental point cloud data, target walking direction, and motion posture data corresponding to the target robot, and combines them with preset walking stride length and preset maximum stride length to determine the target landing gait parameters of the robot, generate target joint control commands, and achieve precise control of the robot's stable walking. This greatly improves the accuracy and intelligence in landing point selection, terrain-adaptive gait generation, and walking control, ensuring the robot's walking stability, safety, and adaptability in complex environments, thereby improving the robot's mobility efficiency and reducing energy consumption and the risk of mission failure.

[0051] Optionally, the terrain parameter determination module 320 includes: The candidate landing point determination unit is used to determine multiple candidate landing points for the next step of the target robot based on the preset walking step length, preset maximum step length, target walking direction and motion posture data corresponding to the target robot. The target landing point determination unit is used to filter multiple candidate landing points based on the environmental point cloud data, and determine the target landing point and the target terrain feature parameters corresponding to the target landing point.

[0052] Optionally, the candidate landing point determination unit includes: The landing area determination subunit is used to determine the historical landing point of the target robot's previous step based on the motion posture data, and to determine the target landing area of ​​the target robot's next step based on the target robot's corresponding preset walking step length, preset maximum step length, target walking direction and the historical landing point. The landing point determination subunit is used to sample the target landing area, determine multiple sampling points, and determine the center point corresponding to the multiple sampling points and the target landing area as multiple candidate landing points for the next movement of the target robot.

[0053] Optionally, the landing area determination subunit is specifically used for: determining the expected landing point of the target robot's next step based on the target walking direction and the historical landing points corresponding to the target robot; determining the landing area radius based on the preset walking step length and the preset maximum step length; and determining the target landing area of ​​the target robot's next step based on the expected landing point and the landing area radius.

[0054] Optionally, the landing point determination subunit is specifically used to: divide the target landing area to generate multiple annular areas and a circular area where the expected landing point is located; and perform gradient sampling on the multiple annular areas and the circular area where the expected landing point is located based on the distance between the multiple annular areas and the circular area where the expected landing point is located and the expected landing point, to determine multiple sampling points.

[0055] Optionally, the target landing point determination unit is specifically used for: for each candidate landing point, determining the candidate terrain feature parameters corresponding to the candidate landing point based on the environmental point cloud data, wherein the candidate terrain feature parameters include: ground height, slope angle, surface flatness, and supportability; comprehensively scoring each candidate landing point based on a preset scoring strategy and the candidate terrain parameters corresponding to each candidate landing point, and filtering multiple candidate landing points according to the scoring results to determine the target landing point and the target terrain feature parameters corresponding to the target landing point.

[0056] Optionally, the target landing gait parameters include: landing position, foot posture angle, stride length, stride width, foot lift height, support phase and swing phase timing.

[0057] The above-described device can execute the robot walking stability control method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the robot walking stability control method.

[0058] Example 4 Figure 4This is a schematic diagram of an electronic device implementing the robot walking stability control method of this invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0059] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded into the RAM 13 from storage unit 18. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0060] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0061] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as robot walking stabilization control methods.

[0062] In some embodiments, the robot walking stabilization control method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded into and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the robot walking stabilization control method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the robot walking stabilization control method by any other suitable means (e.g., by means of firmware).

[0063] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory 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 communication unit 19, or installed from storage unit 18, or installed from ROM 12. When the computer program is executed by processor 11, it performs the functions defined in the methods of the embodiments of the present invention.

[0064] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0065] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0066] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on 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 fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0067] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0068] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0069] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0070] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0071] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for stabilizing robot movement, characterized in that, include: Acquire environmental point cloud data, target walking direction, and motion posture data corresponding to the target robot; Based on the preset walking step length, preset maximum step length, environmental point cloud data, target walking direction and motion posture data of the target robot, the target landing point of the target robot in the next step and the target terrain feature parameters corresponding to the target landing point are determined. Based on the target landing point and the target terrain feature parameters corresponding to the target landing point, the target landing gait parameters corresponding to the target robot are determined; Based on the robot dynamics model corresponding to the target robot and the target landing gait parameters, the target joint control command corresponding to the target robot is determined, so as to perform walking stability control on the target robot based on the target joint control command.

2. The method according to claim 1, characterized in that, The process of determining the target robot's next landing point and the corresponding target terrain feature parameters based on the target robot's preset walking stride length, preset maximum stride length, environmental point cloud data, target walking direction, and motion posture data includes: Based on the preset walking step length, preset maximum step length, target walking direction and motion posture data of the target robot, multiple candidate landing points for the next step of the target robot are determined; Based on the environmental point cloud data, multiple candidate landing points are filtered to determine the target landing point and the target terrain feature parameters corresponding to the target landing point.

3. The method according to claim 2, characterized in that, The process of determining multiple candidate landing points for the next step of the target robot based on the preset walking step length, preset maximum step length, target walking direction, and motion posture data corresponding to the target robot includes: Based on the motion posture data, the historical landing point of the target robot's previous step is determined. Based on the target robot's corresponding preset walking step length, preset maximum step length, target walking direction, and historical landing point, the target landing area of ​​the target robot's next step is determined. The target landing area is sampled to determine multiple sampling points, and the center point corresponding to the multiple sampling points and the target landing area is determined as multiple candidate landing points for the target robot to move next.

4. The method according to claim 3, characterized in that, The step of determining the target landing area for the next movement of the target robot based on the preset walking step length, preset maximum step length, target walking direction, and historical landing points includes: Based on the target walking direction and the historical landing point corresponding to the target robot, the expected landing point of the target robot's next step is determined; Based on the preset walking step length and the preset maximum step length, the radius of the landing area is determined, and based on the expected landing point and the radius of the landing area, the target landing area for the next step of the target robot is determined.

5. The method according to claim 4, characterized in that, The sampling of the target landing area determines multiple sampling points, including: The target landing area is divided into multiple annular areas and the circular area where the expected landing point is located; Based on the multiple annular regions and the distance between the circular region where the expected landing point is located and the expected landing point, gradient sampling is performed on the multiple annular regions and the circular region where the expected landing point is located to determine multiple sampling points.

6. The method according to claim 2, characterized in that, The step of filtering multiple candidate landing points based on the environmental point cloud data to determine the target landing point and the target terrain feature parameters corresponding to the target landing point includes: For each of the candidate landing points, based on the environmental point cloud data, the candidate terrain feature parameters corresponding to the candidate landing point are determined, wherein the candidate terrain feature parameters include: ground height, slope angle, surface flatness and supportability; Based on a preset scoring strategy and the candidate terrain parameters corresponding to each candidate landing point, a comprehensive score is given to each candidate landing point. Based on the scoring results, multiple candidate landing points are filtered to determine the target landing point and the target terrain feature parameters corresponding to the target landing point.

7. The method according to claim 1, characterized in that, The target landing gait parameters include: landing position, foot posture angle, stride length, stride width, foot lift height, support phase and swing phase timing.

8. A robot walking stability control device, characterized in that, include: The data acquisition module is used to acquire environmental point cloud data, target walking direction and motion posture data corresponding to the target robot; The terrain parameter determination module is used to determine the target landing point of the target robot's next step and the target terrain feature parameters corresponding to the target landing point based on the preset walking step length, preset maximum step length, environmental point cloud data, target walking direction and motion posture data corresponding to the target robot; The gait parameter determination module is used to determine the target landing gait parameters corresponding to the target robot based on the target landing point and the target terrain feature parameters corresponding to the target landing point. The walking stability control module is used to determine the target joint control command corresponding to the target robot based on the robot dynamics model corresponding to the target robot and the target landing gait parameters, so as to perform walking stability control on the target robot based on the target joint control command.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the robot walking stability control method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that are used to cause a processor to execute the robot walking stability control method according to any one of claims 1-7.

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