Stable walking control method and device of biped robot and computer equipment

Abstract

The invention relates to a stable walking control method and device of a biped robot and computer equipment. The method comprises the steps that environment image data of a gazing range of the biped robot, current track information of the biped robot and current external force information of the biped robot are acquired, and traveling environment distribution information of the biped robot is recognized based on the environment image data; on the basis of the traveling environment distribution information, current track information of the biped robot is adjusted through a track obstacle avoidance optimization strategy, target track information is obtained, and instantaneous impact force characteristics of the biped robot are recognized on the basis of current external force information; and based on the instantaneous impact force characteristics of the biped robot, an impact resistance scheme of the biped robot is generated through an impact resistance strategy, and based on the impact resistance scheme and the target trajectory information, the biped robot is controlled to walk. By adopting the method, the stable walking control efficiency of the biped steering engine robot can be improved.

Description

Technical Field

[0001] This application relates to the field of intelligent robot technology, and in particular to a method, apparatus and computer equipment for stable walking control of a bipedal robot. Background Technology

[0002] Bipedal servo robots are biomimetic robots that move using two legs. Their mechanical structure mimics the human lower limbs, enabling them to maintain dynamic balance and perform multi-degree-of-freedom movements. Improving the walking stability and speed of bipedal servo robots is a key area for optimizing their biomimetic performance.

[0003] Existing bipedal servo robots are lacking in walking speed, walking stability, and walking anti-interference ability. The control algorithms used for servo robots on rugged terrain have long calculation times and slow walking gaits, resulting in low efficiency in stable walking control of bipedal servo robots. Summary of the Invention

[0004] Therefore, it is necessary to provide a stable walking control method, device, and computer equipment for bipedal robots to address the aforementioned technical problems.

[0005] In a first aspect, this application provides a method for stable walking control of a bipedal robot, including:

[0006] The system acquires environmental image data of the bipedal robot's gaze range, the current trajectory information of the bipedal robot, and the current external force information of the bipedal robot, and identifies the distribution information of the bipedal robot's travel environment based on the environmental image data.

[0007] Based on the travel environment distribution information, the current trajectory information of the bipedal robot is adjusted through a trajectory obstacle avoidance optimization strategy to obtain the target trajectory information, and the instantaneous impact force characteristics of the bipedal robot are identified based on the current external force information.

[0008] Based on the instantaneous impact force characteristics of the bipedal robot, an impact resistance strategy is generated for the bipedal robot, and based on the impact resistance strategy and the target trajectory information, the bipedal robot is controlled to walk.

[0009] Optionally, identifying the distribution information of the bipedal robot's walking environment based on the environmental image data includes:

[0010] The environmental image data is divided into sub-environmental image data corresponding to each travel segment; the travel segment includes a short-range segment, a medium-range segment, and a long-range segment;

[0011] Based on the sub-environmental image data corresponding to each of the travel segments, the environmental distribution information of each of the travel segments is identified through the travel environment analysis strategy of each travel segment;

[0012] The environmental distribution information of each travel segment is arranged in order of distance to obtain the travel environment distribution information of the bipedal robot.

[0013] Optionally, the step of adjusting the current trajectory information of the bipedal robot based on the travel environment distribution information and using a trajectory obstacle avoidance optimization strategy to obtain the target trajectory information includes:

[0014] When the travel segment is a short-distance segment, based on the environmental distribution information, the distribution information of each obstacle range, the distribution information of the road range, and the distribution information of the slope change of the travel segment are identified. Based on the distribution information of each obstacle range and the distribution information of the road range of the travel segment, a first trajectory optimization strategy is applied to the current sub-trajectory of the travel segment to obtain the first optimized trajectory information of the travel segment.

[0015] Based on the slope variation distribution information of the travel section, the first optimized trajectory information of the travel section is adjusted through the trajectory optimization network to obtain the target sub-optimized trajectory of the travel section;

[0016] When the travel segment is a medium-range segment, based on the environmental distribution information, the road range distribution information and the slope distribution information of the travel segment are identified, and based on the road range distribution information and the slope distribution information of the travel segment, the target sub-optimized trajectory of the travel segment is obtained through the second trajectory optimization strategy.

[0017] When the travel segment is a remote segment, based on the environmental distribution information, the slope change distribution information of the travel segment is identified, and based on the slope change distribution information of the travel segment, the current sub-trajectory of the travel segment is adjusted through the trajectory optimization network to obtain the target sub-optimized trajectory of the travel segment;

[0018] The target sub-optimized trajectories of each travel segment are arranged in order of distance from each travel segment to obtain the target trajectory information.

[0019] Optionally, identifying the instantaneous impact force characteristics of the bipedal robot based on the current external force information includes:

[0020] Collect the force position information of the bipedal robot, and based on the current external force information, identify the impact direction of the instantaneous impact force and the impact intensity of the instantaneous impact force;

[0021] Based on the force position information of the bipedal robot and the impact direction of the bipedal robot, the driving interference direction of the bipedal robot is identified. Based on the driving interference direction of the bipedal robot, the impact direction of the instantaneous impact force, and the impact force degree of the instantaneous impact force, the driving interference force degree of the bipedal robot is identified through a dynamic algorithm.

[0022] The driving interference direction and the driving interference force of the bipedal robot are used as the instantaneous impact force characteristics of the bipedal robot.

[0023] Optionally, the step of generating an impact resistance scheme for the bipedal robot based on its instantaneous impact force characteristics and an impact resistance strategy includes:

[0024] The current motion state data of the bipedal robot is acquired, and based on the current motion state data of the bipedal robot, the current direction of travel and the current driving force of the bipedal robot are identified.

[0025] Based on the current direction of travel of the bipedal robot, the current driving force level of the bipedal robot, the driving interference direction of the bipedal robot, and the driving interference force level of the bipedal robot, calculate the driving angle deviation information and the driving force deviation information of the bipedal robot.

[0026] Based on the driving angle deviation information and driving force deviation information of the bipedal robot, an impact resistance scheme for the bipedal robot is generated through a driving control strategy.

[0027] Optionally, controlling the bipedal robot to walk based on the impact resistance scheme and the target trajectory information includes:

[0028] Based on the target trajectory information, the joint trajectory information of the bipedal robot is generated using a trajectory inverse algorithm.

[0029] Based on the aforementioned impact resistance scheme, drive control information for the bipedal robot is generated;

[0030] Based on the joint trajectory information and the drive control information, the bipedal robot is controlled to walk.

[0031] Secondly, this application also provides a stable walking control device for a bipedal robot, comprising:

[0032] The acquisition module is used to acquire environmental image data of the bipedal robot's gaze range, the current trajectory information of the bipedal robot, and the current external force information of the bipedal robot, and to identify the distribution information of the bipedal robot's travel environment based on the environmental image data;

[0033] The identification module is used to adjust the current trajectory information of the bipedal robot based on the travel environment distribution information and through the trajectory obstacle avoidance optimization strategy to obtain the target trajectory information, and to identify the instantaneous impact force characteristics of the bipedal robot based on the current external force information.

[0034] The control module is used to generate an impact resistance scheme for the bipedal robot based on the instantaneous impact force characteristics of the bipedal robot and an impact resistance strategy, and to control the bipedal robot to walk based on the impact resistance scheme and the target trajectory information.

[0035] Optionally, the acquisition module is specifically used for:

[0036] The environmental image data is divided into sub-environmental image data corresponding to each travel segment; the travel segment includes a short-range segment, a medium-range segment, and a long-range segment;

[0037] Based on the sub-environmental image data corresponding to each of the travel segments, the environmental distribution information of each of the travel segments is identified through the travel environment analysis strategy of each travel segment;

[0038] The environmental distribution information of each travel segment is arranged in order of distance to obtain the travel environment distribution information of the bipedal robot.

[0039] Optionally, the identification module is specifically used for:

[0040] When the travel segment is a short-distance segment, based on the environmental distribution information, the distribution information of each obstacle range, the distribution information of the road range, and the distribution information of the slope change of the travel segment are identified. Based on the distribution information of each obstacle range and the distribution information of the road range of the travel segment, a first trajectory optimization strategy is applied to the current sub-trajectory of the travel segment to obtain the first optimized trajectory information of the travel segment.

[0041] Based on the slope variation distribution information of the travel section, the first optimized trajectory information of the travel section is adjusted through the trajectory optimization network to obtain the target sub-optimized trajectory of the travel section;

[0042] When the travel segment is a medium-range segment, based on the environmental distribution information, the road range distribution information and the slope distribution information of the travel segment are identified, and based on the road range distribution information and the slope distribution information of the travel segment, the target sub-optimized trajectory of the travel segment is obtained through the second trajectory optimization strategy.

[0043] When the travel segment is a remote segment, based on the environmental distribution information, the slope change distribution information of the travel segment is identified, and based on the slope change distribution information of the travel segment, the current sub-trajectory of the travel segment is adjusted through the trajectory optimization network to obtain the target sub-optimized trajectory of the travel segment;

[0044] The target sub-optimized trajectories of each travel segment are arranged in order of distance from each travel segment to obtain the target trajectory information.

[0045] Optionally, the identification module is specifically used for:

[0046] Collect the force position information of the bipedal robot, and based on the current external force information, identify the impact direction of the instantaneous impact force and the impact intensity of the instantaneous impact force;

[0047] Based on the force position information of the bipedal robot and the impact direction of the bipedal robot, the driving interference direction of the bipedal robot is identified. Based on the driving interference direction of the bipedal robot, the impact direction of the instantaneous impact force, and the impact force degree of the instantaneous impact force, the driving interference force degree of the bipedal robot is identified through a dynamic algorithm.

[0048] The driving interference direction and the driving interference force of the bipedal robot are used as the instantaneous impact force characteristics of the bipedal robot.

[0049] Optionally, the control module is specifically used for:

[0050] The current motion state data of the bipedal robot is acquired, and based on the current motion state data of the bipedal robot, the current direction of travel and the current driving force of the bipedal robot are identified.

[0051] Based on the current direction of travel of the bipedal robot, the current driving force level of the bipedal robot, the driving interference direction of the bipedal robot, and the driving interference force level of the bipedal robot, calculate the driving angle deviation information and the driving force deviation information of the bipedal robot.

[0052] Based on the driving angle deviation information and driving force deviation information of the bipedal robot, an impact resistance scheme for the bipedal robot is generated through a driving control strategy.

[0053] Optionally, the control module is specifically used for:

[0054] Based on the target trajectory information, the joint trajectory information of the bipedal robot is generated using a trajectory inverse algorithm.

[0055] Based on the aforementioned impact resistance scheme, drive control information for the bipedal robot is generated;

[0056] Based on the joint trajectory information and the drive control information, the bipedal robot is controlled to walk.

[0057] Thirdly, this application provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method described in any one of the first aspects.

[0058] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the method described in any one of the first aspects.

[0059] Fifthly, this application provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the steps of the method described in any one of the first aspects.

[0060] The aforementioned stable walking control method, apparatus, and computer equipment for a bipedal robot acquire environmental image data within the robot's gaze range, the robot's current trajectory information, and the robot's current external force information. Based on the environmental image data, the system identifies the robot's walking environment distribution information. Based on the walking environment distribution information, the system adjusts the robot's current trajectory information using a trajectory obstacle avoidance optimization strategy to obtain target trajectory information. Based on the current external force information, the system identifies the robot's instantaneous impact force characteristics. Based on the instantaneous impact force characteristics, the system generates an impact resistance scheme for the robot using an impact resistance strategy. Based on the impact resistance scheme and the target trajectory information, the system controls the bipedal robot to walk. This solution combines environmental image data of the bipedal robot to optimize its trajectory. The trajectory obstacle avoidance optimization strategy includes segmented trajectory optimization, ensuring the accuracy of the current trajectory while reducing the optimization time for subsequent segments, thus comprehensively reducing the overall trajectory optimization time and improving efficiency. Secondly, when dealing with impact forces, this solution intelligently generates corresponding impact resistance schemes based on the robot's instantaneous impact characteristics, enhancing its resistance to instantaneous impacts and effectively improving stable walking. Furthermore, this solution minimizes the robot's control time, effectively improving the efficiency of stable walking control for bipedal servo robots. Attached Figure Description

[0061] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0062] Figure 1 This is a flowchart illustrating a stable walking control method for a bipedal robot in one embodiment;

[0063] Figure 2 This is a flowchart illustrating an example of stable walking control for a bipedal robot in one embodiment;

[0064] Figure 3 This is a structural block diagram of a stable walking control device for a bipedal robot in one embodiment;

[0065] Figure 4 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0066] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0067] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0068] The stable walking control method for bipedal robots provided in this application embodiment can be applied to a system for stable walking control of bipedal robots. This system can be applied to a terminal, which can be, but is not limited to, various personal computers, laptops, mid-range computers, etc. The terminal combines environmental image data of the bipedal robot to optimize its trajectory. The trajectory obstacle avoidance optimization strategy includes segmented trajectory optimization, ensuring the accuracy of the current trajectory optimization while reducing the subsequent trajectory optimization time for other segments, thereby comprehensively reducing the overall trajectory optimization time and improving trajectory optimization efficiency. Secondly, when dealing with impact forces on the bipedal robot, this solution can intelligently generate corresponding impact resistance schemes based on the instantaneous impact force characteristics of the bipedal robot, improving its anti-interference ability against instantaneous impact forces and effectively enhancing the robot's stable walking performance. Furthermore, this solution minimizes the control time of the robot, effectively improving the stable walking control efficiency of the bipedal servo robot.

[0069] In one exemplary embodiment, such as Figure 1 As shown, a stable walking control method for a bipedal robot is provided. Taking the application of this method to a terminal as an example, the method includes the following steps S101 to S103. Wherein:

[0070] Step S101: Obtain environmental image data of the bipedal robot's gaze range, the current trajectory information of the bipedal robot, and the current external force information of the bipedal robot, and identify the distribution information of the bipedal robot's travel environment based on the environmental image data.

[0071] In this embodiment, the terminal uses a camera device installed on the head of the bipedal robot to collect image data of the robot's gaze range in real time, obtaining environmental image data. Then, the terminal uses force sensors installed on various parts of the robot to collect instantaneous external force data of the robot in real time. Upon collecting instantaneous external force data, the terminal uses the collected instantaneous external force data and the robot limb where the instantaneous external force data was collected as the robot's current external force information. Finally, the terminal obtains the robot's pre-set trajectory information. Then, the terminal combines the image range of each travel segment by dividing the environmental image data into regions using cosine, splitting the environmental image data into sub-environmental image data for each travel segment. This allows for the identification of the sub-environmental distribution information of each travel segment, obtaining the bipedal robot's travel environment distribution information. The travel segment includes, but is not limited to, short-range, medium-range, and long-range segments. The short-range segment is the robot's walking range within 30 seconds during its current movement; the medium-range segment is the walking range from 30 seconds to 2 minutes; and the long-range segment is the walking range after 2 minutes. The sub-environmental distribution information for the near-range section includes road extent information, obstacle extent information, and slope information. The sub-environmental distribution information for the mid-range section includes road extent information and slope information. The sub-environmental distribution information for the long-range section includes slope information. The specific identification process will be explained in detail later.

[0072] Step S102: Based on the distribution information of the travel environment, the current trajectory information of the bipedal robot is adjusted through the trajectory obstacle avoidance optimization strategy to obtain the target trajectory information, and the instantaneous impact force characteristics of the bipedal robot are identified based on the current external force information.

[0073] In this embodiment, the terminal adjusts the current trajectory information of the bipedal robot based on the distribution information of the traveling environment and through a trajectory obstacle avoidance optimization strategy to obtain the target trajectory information. It also identifies the instantaneous impact force characteristics of the bipedal robot based on the current external force information. The trajectory obstacle avoidance optimization strategy is designed to avoid obstacles by selecting routes with a slope no greater than a preset slope range within the road area information. Since the environmental distribution information is incomplete in the mid-range and long-range sections, the current trajectory information is initially adjusted. During the robot's movement, if the mid-range section becomes a near-range section, or the long-range section becomes a mid-range section, the already optimized trajectory information can be further optimized using the newly obtained environmental distribution information, eliminating the need for re-optimization. This improves the efficiency of trajectory optimization and the overall trajectory optimization efficiency. The specific optimization process will be explained in detail later. The instantaneous impact force characteristic refers to the actual direction and degree of driving interference experienced by the bipedal robot. The driving interference direction characterizes the direction that interferes with the robot's movement, and the driving interference degree is the driving force in that direction. Since the robot may experience numerous instantaneous impact forces, if these forces do not interfere with its movement, the robot does not need to respond to the driving interference to counteract the impact. The robot only responds to the driving interference to counteract it when the instantaneous impact force does interfere with its movement. The driving interference direction is a preset interference direction range in the terminal. When the instantaneous impact force experienced by the robot is outside this interference direction range, the terminal uses dynamics technology to break down the instantaneous impact force into sub-impact forces in each interference direction, obtaining each driving interference direction and its degree. The specific identification process will be explained in detail later.

[0074] Step S103: Based on the instantaneous impact force characteristics of the bipedal robot, an impact resistance scheme for the bipedal robot is generated through an impact resistance strategy. Based on the impact resistance scheme and the target trajectory information, the bipedal robot is controlled to walk.

[0075] In this embodiment, the terminal generates an anti-impact scheme for the bipedal robot based on the instantaneous impact force characteristics of the robot and the target trajectory information. The anti-impact strategy is a multi-layer control algorithm based on Whole-Body Control (WBC). It calculates the Jacobian matrix from the legs to the center of mass, and based on the obtained driving disturbance direction and degree, calculates a corrective speed and impact force using a PID (Proportional-Integral-Differential) algorithm. This is then decomposed into the Jacobian matrix for each joint servo, resulting in an incremental strategy for each servo. The bipedal robot's walking is controlled by feedback from the IMU (Inertial Measurement Unit) and each joint servo.

[0076] Based on the above scheme, and combined with the environmental image data of the bipedal robot, the trajectory of the bipedal robot is optimized. The trajectory obstacle avoidance optimization strategy includes segmented trajectory optimization, which ensures the accuracy of the current trajectory optimization while reducing the subsequent trajectory optimization time of other segments, thereby comprehensively reducing the overall trajectory optimization time and improving trajectory optimization efficiency. Secondly, when the bipedal robot is subjected to impact force, this scheme can intelligently generate corresponding impact resistance schemes based on the instantaneous impact force characteristics of the bipedal robot, improving the anti-interference ability of instantaneous impact force and effectively improving the stable walking effect of the robot. Furthermore, this scheme minimizes the control time of the robot, effectively improving the stable walking control efficiency of the bipedal servo robot.

[0077] Optionally, based on environmental image data, the distribution information of the bipedal robot's travel environment is identified, including: splitting the environmental image data into sub-environmental image data corresponding to each travel segment; the travel segment includes a near-range segment, a mid-range segment, and a long-range segment; based on the sub-environmental image data corresponding to each travel segment, identifying the environmental distribution information of each travel segment through a travel environment analysis strategy; and arranging the environmental distribution information of each travel segment according to the order of distance of each travel segment to obtain the travel environment distribution information of the bipedal robot.

[0078] In this embodiment, the terminal divides the environmental image data into sub-environmental image data corresponding to each travel segment. The terminal presets the image range of each travel segment for each image data set, and then performs image segmentation on the environmental image data based on the image range of each travel segment to obtain the sub-environmental image data corresponding to each travel segment. This travel segment includes a near-range segment, a mid-range segment, and a long-range segment. Then, based on the sub-environmental image data corresponding to each travel segment, the terminal identifies the environmental distribution information of each travel segment through a travel environment analysis strategy. The travel environment analysis strategy for each travel segment is a convolutional neural network with an attention mechanism trained using different sample data. Specifically, the sample data for the near-range segment includes labeled image data of various types of obstacles, roads, and slopes; the sample data for the mid-range segment includes labeled road data and slope data; and the sample data for the long-range segment includes labeled slope data.

[0079] Finally, the terminal arranges the environmental distribution information of each travel segment according to the distance of each travel segment to obtain the travel environment distribution information of the bipedal robot.

[0080] Based on the above scheme, by segmenting the images of each travel section and then performing environmental analysis on each segment, the comprehensiveness of the environmental analysis of the robot's travel route is ensured while maximizing the efficiency of environmental analysis.

[0081] Optionally, based on the distribution information of the traveling environment, the current trajectory information of the bipedal robot is adjusted through a trajectory obstacle avoidance optimization strategy to obtain target trajectory information. This includes: when the traveling segment is a short-range segment, based on the environmental distribution information, identifying the distribution information of each obstacle range, the distribution information of the road range, and the distribution information of the slope change in the traveling segment; and based on the distribution information of each obstacle range and the distribution information of the road range, applying a first trajectory optimization strategy to the current sub-trajectory of the traveling segment to obtain the first optimized trajectory information of the traveling segment; and based on the slope change distribution information of the traveling segment, adjusting the first optimized trajectory information of the traveling segment through a trajectory optimization network to obtain the target sub-trajectory of the traveling segment. The trajectory is optimized as follows: When the travel segment is a medium-range segment, based on environmental distribution information, the road range distribution information and slope distribution information of the travel segment are identified. Based on the road range distribution information and slope distribution information of the travel segment, a second trajectory optimization strategy is used to obtain the target sub-optimized trajectory of the travel segment. When the travel segment is a long-range segment, based on environmental distribution information, the slope change distribution information of the travel segment is identified. Based on the slope change distribution information of the travel segment, the current sub-trajectory of the travel segment is adjusted through the trajectory optimization network to obtain the target sub-optimized trajectory of the travel segment. The target sub-optimized trajectories of each travel segment are arranged in order of distance from each travel segment to obtain the target trajectory information.

[0082] In this embodiment, when the travel segment is a short-range segment, the terminal identifies the distribution information of obstacles, roads, and slope changes within the travel segment based on environmental distribution information. Then, based on these information, the terminal applies a first trajectory optimization strategy to the current sub-trajectory of the travel segment, obtaining first optimized trajectory information. Next, based on the slope change information, the terminal adjusts the first optimized trajectory information using a trajectory optimization network to obtain the target sub-optimized trajectory. Specifically, the first trajectory optimization strategy involves first filtering the sub-environmental image of the short-range segment for unobstructed road coverage based on road and obstacle distribution information. Finally, the terminal uses this unobstructed road coverage information as the first optimized trajectory information. Next, based on the slope variation distribution information, the terminal identifies target slope ranges that are lower than the preset range on the terminal. Based on these target slope ranges, the terminal filters the target road range distribution information within the barrier-free road range distribution information. This target road range distribution information does not include obstacles or slope ranges exceeding the preset range on the terminal. Finally, the terminal uses the target road range distribution information as input and, through a trajectory optimization network, generates the nearest trajectory information from the target road range distribution information, serving as the target sub-optimized trajectory. This trajectory optimization network is an artificial neural network based on a trajectory planning algorithm.

[0083] When the travel segment is a medium-range segment, the terminal identifies the road range distribution information and slope distribution information of the travel segment based on environmental distribution information. Based on this information, a second trajectory optimization strategy is employed to obtain the target sub-optimized trajectory for the travel segment. This second trajectory optimization strategy involves combining the road range distribution information and slope distribution information to select road ranges with slopes exceeding a preset slope range for the terminal as the target road range. The trajectory is then generated using the trajectory optimization network described above to obtain the target sub-optimized trajectory.

[0084] When the travel segment is a remote segment, the terminal identifies the slope variation distribution information of the travel segment based on environmental distribution information. Based on this slope variation distribution information, it adjusts the current sub-trajectory of the travel segment through a trajectory optimization network to obtain the target sub-optimized trajectory for the travel segment. Specifically, during trajectory optimization, the terminal uses the current trajectory information of the travel segment as the initial trajectory information. Then, it filters the slope variation distribution information to include slopes that do not exceed a preset slope range within the terminal's range as the trajectory range to be optimized. Finally, it optimizes each trajectory range to be optimized through the aforementioned trajectory optimization network to obtain the target sub-optimized trajectory.

[0085] Finally, the terminal arranges the target sub-optimized trajectories of each travel segment according to the distance of each travel segment to obtain the target trajectory information.

[0086] Based on the above scheme, by optimizing the trajectory in stages, the trajectory optimization time during the subsequent robot movement process is reduced, and the accuracy of trajectory generation for the robot can be effectively improved.

[0087] Optionally, based on the current external force information, the instantaneous impact force characteristics of the bipedal robot are identified, including: collecting the force-bearing position information of the bipedal robot, and based on the current external force information, identifying the impact direction and impact intensity of the instantaneous impact force; based on the force-bearing position information and the impact direction of the bipedal robot, identifying the driving interference direction of the bipedal robot, and based on the driving interference direction, the impact direction, and the impact intensity of the instantaneous impact force, identifying the driving interference intensity of the bipedal robot through a dynamic algorithm; and using the driving interference direction and the driving interference intensity of the bipedal robot as the instantaneous impact force characteristics of the bipedal robot.

[0088] In this embodiment, the terminal collects the force position information of the bipedal robot and, based on the current external force information, identifies the impact direction and the impact intensity of the instantaneous impact force. The impact direction refers to the direction in which the impact force affects the force position information of the bipedal robot.

[0089] Then, based on the force position information of the bipedal robot and the direction of its impact force, the terminal identifies the driving interference direction of the bipedal robot. This driving interference direction is obtained by mapping the impact force direction onto the bipedal robot's coordinate system. The terminal then breaks down this impact force direction into directions within a preset range to obtain the driving interference direction.

[0090] Next, based on the bipedal robot's driving disturbance direction, the impact force direction of the instantaneous impact force, and the impact force intensity of the instantaneous impact force, the terminal identifies the degree of driving disturbance force of the bipedal robot through a dynamic algorithm. This dynamic algorithm is a force analysis algorithm. This force analysis algorithm is used to calculate the actual force value corresponding to each driving disturbance direction after the impact force direction is broken down into individual driving disturbance directions.

[0091] Finally, the terminal uses the direction of the bipedal robot's driving interference and the degree of the driving interference force of the bipedal robot as the instantaneous impact force characteristics of the bipedal robot.

[0092] Based on the above scheme, by performing force analysis on the impact force and screening the driving interference direction and driving interference force that affect the robot's drive, the accuracy of identifying the instantaneous impact force on the robot is improved.

[0093] Optionally, based on the instantaneous impact force characteristics of the bipedal robot, an impact resistance strategy is generated for the bipedal robot, including: acquiring the current motion state data of the bipedal robot, and based on the current motion state data, identifying the current direction of travel and the current driving force level of the bipedal robot; calculating the driving angle deviation information and driving force deviation information of the bipedal robot based on the current direction of travel, the current driving force level, the driving interference direction, and the driving interference force level of the bipedal robot; and generating the impact resistance strategy for the bipedal robot through a driving control strategy based on the driving angle deviation information and the driving force deviation information of the bipedal robot.

[0094] In this embodiment, the terminal acquires the current motion state data of the bipedal robot and, based on this data, identifies the current direction of travel and the current driving force level. Then, based on the current direction of travel, the current driving force level, the driving interference direction, and the driving interference force level, the terminal calculates the driving angle deviation information and the driving force deviation information of the bipedal robot. The driving angle deviation information is the deviation angle between the current direction of travel and each driving interference direction, while the driving force deviation information is the amount by which the driving interference force level of each driving interference direction cancels out the current driving force level. This cancellation amount is calculated by using a dynamic algorithm based on the driving interference force level and the driving angle deviation information to calculate the interference force level of the robot in the opposite direction of the current direction of travel, and this interference level is used as the driving force deviation information corresponding to the driving angle deviation information of the driving force interference direction.

[0095] Finally, based on the bipedal robot's drive angle deviation information and drive force deviation information, the terminal generates an impact resistance scheme for the bipedal robot through a drive control strategy. This drive control strategy consists of incremental data for the servo motors of each joint of the bipedal robot generated by a WBC-based multi-layer control algorithm.

[0096] Based on the above scheme, by further analyzing the force on each driving interference direction and the robot's travel direction, the actual driving angle deviation information and driving force deviation information of the robot can be identified. Finally, incremental data of each joint servo motor is generated according to WBC's multi-layer control algorithm, thereby improving the control accuracy of the robot.

[0097] Optionally, based on the impact resistance scheme and the target trajectory information, the bipedal robot is controlled to walk, including: generating the joint trajectory information of the bipedal robot through the trajectory inverse algorithm based on the target trajectory information; generating the drive control information of the bipedal robot based on the impact resistance scheme; and controlling the bipedal robot to walk based on the joint trajectory information and the drive control information.

[0098] In this embodiment, the terminal generates joint trajectory information of the bipedal robot based on the target trajectory information using a trajectory inverse algorithm; it also generates drive control information for the bipedal robot based on an impact resistance scheme; and finally, it controls the bipedal robot to walk based on the joint trajectory information and the drive control information. The trajectory inverse algorithm involves converting the target trajectory information into the robot's leg joint trajectories, then calculating the current centroid position of the bipedal robot through IMU feedback and feedback from each joint servo. Next, the terminal converts the leg joint trajectories to the current centroid coordinate system to obtain the trajectory information of each joint servo. Finally, the terminal superimposes the trajectory information of each joint servo and the incremental data of each joint servo to control the bipedal robot to walk.

[0099] Based on the above scheme, when resisting impact forces on bipedal robots, the instantaneous impact force characteristics of bipedal robots can be combined to intelligently generate corresponding anti-impact schemes, which improves the anti-interference ability against instantaneous impact forces, effectively improves the stable walking effect of the robot, and this scheme minimizes the control time of the robot, effectively improving the stable walking control efficiency of bipedal servo robots.

[0100] This application also provides an example of stable walking control for a bipedal robot, such as... Figure 2 As shown, the specific processing procedure includes the following steps:

[0101] Step S201: Obtain environmental image data of the bipedal robot's gaze range, the current trajectory information of the bipedal robot, and the current external force information of the bipedal robot.

[0102] Step S202: The environmental image data is split into sub-environmental image data corresponding to each travel segment.

[0103] Step S203: Based on the sub-environmental image data corresponding to each travel segment, the environmental distribution information of each travel segment is identified through the travel environment analysis strategy of each travel segment.

[0104] Step S204: Arrange the environmental distribution information of each travel segment according to the order of distance of each travel segment to obtain the travel environment distribution information of the bipedal robot.

[0105] Step S205: When the travel segment is a short-distance segment, based on the environmental distribution information, identify the distribution information of each obstacle range, the distribution information of the road range, and the distribution information of the slope change in the travel segment. Based on the distribution information of each obstacle range and the distribution information of the road range in the travel segment, apply a first trajectory optimization strategy to the current sub-trajectory of the travel segment to obtain the first optimized trajectory information of the travel segment.

[0106] Step S206: Based on the slope change distribution information of the travel section, the first optimized trajectory information of the travel section is adjusted through the trajectory optimization network to obtain the target sub-optimized trajectory of the travel section.

[0107] Step S207: When the travel segment is a medium-range segment, based on the environmental distribution information, identify the road range distribution information and the slope distribution information of the travel segment. Based on the road range distribution information and the slope distribution information of the travel segment, obtain the target sub-optimized trajectory of the travel segment through the second trajectory optimization strategy.

[0108] Step S208: When the travel segment is a remote segment, the slope change distribution information of the travel segment is identified based on the environmental distribution information. Based on the slope change distribution information of the travel segment, the current sub-trajectory of the travel segment is adjusted through the trajectory optimization network to obtain the target sub-optimized trajectory of the travel segment.

[0109] Step S209: Arrange the target sub-optimized trajectories of each travel segment according to the order of distance between each travel segment to obtain the target trajectory information.

[0110] Step S210: Collect the force position information of the bipedal robot, and based on the current external force information, identify the impact direction and impact degree of the instantaneous impact force.

[0111] Step S211: Based on the force position information of the bipedal robot and the impact direction of the bipedal robot, identify the driving interference direction of the bipedal robot, and based on the driving interference direction of the bipedal robot, the impact direction of the instantaneous impact force, and the impact force degree of the instantaneous impact force, identify the driving interference force degree of the bipedal robot through a dynamic algorithm.

[0112] Step S212: The driving interference direction and the driving interference force of the bipedal robot are used as the instantaneous impact force characteristics of the bipedal robot.

[0113] Step S213: Obtain the current motion state data of the bipedal robot, and based on the current motion state data of the bipedal robot, identify the current direction of travel of the bipedal robot and the current driving force of the bipedal robot.

[0114] Step S214: Based on the current direction of travel of the bipedal robot, the current driving force level of the bipedal robot, the driving interference direction of the bipedal robot, and the driving interference force level of the bipedal robot, calculate the driving angle deviation information and the driving force deviation information of the bipedal robot.

[0115] Step S215: Based on the driving angle deviation information and driving force deviation information of the bipedal robot, an impact resistance scheme for the bipedal robot is generated through a driving control strategy.

[0116] Step S216: Based on the target trajectory information, generate the joint trajectory information of the bipedal robot using the trajectory inverse algorithm.

[0117] Step S217: Based on the impact resistance scheme, generate the drive control information for the bipedal robot.

[0118] Step S218: Based on joint trajectory information and drive control information, control the bipedal robot to walk.

[0119] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0120] Based on the same inventive concept, this application also provides a bipedal robot stable walking control device for implementing the above-mentioned bipedal robot stable walking control method. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the bipedal robot stable walking control device provided below can be found in the limitations of the bipedal robot stable walking control method above, and will not be repeated here.

[0121] In one exemplary embodiment, such as Figure 3 As shown, a stable walking control device for a bipedal robot is provided, comprising: an acquisition module 310, an identification module 320, and a control module 330, wherein:

[0122] The acquisition module 310 is used to acquire environmental image data of the bipedal robot's gaze range, the current trajectory information of the bipedal robot, and the current external force information of the bipedal robot, and to identify the bipedal robot's travel environment distribution information based on the environmental image data;

[0123] The identification module 320 is used to adjust the current trajectory information of the bipedal robot based on the travel environment distribution information and through a trajectory obstacle avoidance optimization strategy to obtain target trajectory information, and to identify the instantaneous impact force characteristics of the bipedal robot based on the current external force information.

[0124] The control module 330 is used to generate an anti-impact scheme for the bipedal robot based on the instantaneous impact force characteristics of the bipedal robot and an anti-impact strategy, and to control the bipedal robot to walk based on the anti-impact scheme and the target trajectory information.

[0125] Optionally, the acquisition module 310 is specifically used for:

[0126] The environmental image data is divided into sub-environmental image data corresponding to each travel segment; the travel segment includes a short-range segment, a medium-range segment, and a long-range segment;

[0127] Based on the sub-environmental image data corresponding to each of the travel segments, the environmental distribution information of each of the travel segments is identified through the travel environment analysis strategy of each travel segment;

[0128] The environmental distribution information of each travel segment is arranged in order of distance to obtain the travel environment distribution information of the bipedal robot.

[0129] Optionally, the identification module 320 is specifically used for:

[0130] When the travel segment is a short-distance segment, based on the environmental distribution information, the distribution information of each obstacle range, the distribution information of the road range, and the distribution information of the slope change of the travel segment are identified. Based on the distribution information of each obstacle range and the distribution information of the road range of the travel segment, a first trajectory optimization strategy is applied to the current sub-trajectory of the travel segment to obtain the first optimized trajectory information of the travel segment.

[0131] Based on the slope variation distribution information of the travel section, the first optimized trajectory information of the travel section is adjusted through the trajectory optimization network to obtain the target sub-optimized trajectory of the travel section;

[0132] When the travel segment is a medium-range segment, based on the environmental distribution information, the road range distribution information and the slope distribution information of the travel segment are identified, and based on the road range distribution information and the slope distribution information of the travel segment, the target sub-optimized trajectory of the travel segment is obtained through the second trajectory optimization strategy.

[0133] When the travel segment is a remote segment, based on the environmental distribution information, the slope change distribution information of the travel segment is identified, and based on the slope change distribution information of the travel segment, the current sub-trajectory of the travel segment is adjusted through the trajectory optimization network to obtain the target sub-optimized trajectory of the travel segment;

[0134] The target sub-optimized trajectories of each travel segment are arranged in order of distance from each travel segment to obtain the target trajectory information.

[0135] Optionally, the identification module 320 is specifically used for:

[0136] Collect the force position information of the bipedal robot, and based on the current external force information, identify the impact direction of the instantaneous impact force and the impact intensity of the instantaneous impact force;

[0137] Based on the force position information of the bipedal robot and the impact direction of the bipedal robot, the driving interference direction of the bipedal robot is identified. Based on the driving interference direction of the bipedal robot, the impact direction of the instantaneous impact force, and the impact force degree of the instantaneous impact force, the driving interference force degree of the bipedal robot is identified through a dynamic algorithm.

[0138] The driving interference direction and the driving interference force of the bipedal robot are used as the instantaneous impact force characteristics of the bipedal robot.

[0139] Optionally, the control module 330 is specifically used for:

[0140] The current motion state data of the bipedal robot is acquired, and based on the current motion state data of the bipedal robot, the current direction of travel and the current driving force of the bipedal robot are identified.

[0141] Based on the current direction of travel of the bipedal robot, the current driving force level of the bipedal robot, the driving interference direction of the bipedal robot, and the driving interference force level of the bipedal robot, calculate the driving angle deviation information and the driving force deviation information of the bipedal robot.

[0142] Based on the driving angle deviation information and driving force deviation information of the bipedal robot, an impact resistance scheme for the bipedal robot is generated through a driving control strategy.

[0143] Optionally, the control module 330 is specifically used for:

[0144] Based on the target trajectory information, the joint trajectory information of the bipedal robot is generated using a trajectory inverse algorithm.

[0145] Based on the aforementioned impact resistance scheme, drive control information for the bipedal robot is generated;

[0146] Based on the joint trajectory information and the drive control information, the bipedal robot is controlled to walk.

[0147] The modules in the aforementioned bipedal robot's stable walking control device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the computer device's memory as software, so that the processor can call and execute the corresponding operations of each module.

[0148] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 4 As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When executed by the processor, the computer program implements a stable walking control method for a bipedal robot. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0149] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0150] In one exemplary embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of a beer warehouse inventory optimization method.

[0151] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of a beer warehouse inventory optimization method.

[0152] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of a beer warehouse inventory optimization method.

[0153] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0154] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0155] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0156] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for stable walking control of a bipedal robot, characterized in that, The method includes: The system acquires environmental image data of the bipedal robot's gaze range, the current trajectory information of the bipedal robot, and the current external force information of the bipedal robot, and identifies the distribution information of the bipedal robot's travel environment based on the environmental image data. Based on the travel environment distribution information, the current trajectory information of the bipedal robot is adjusted through a trajectory obstacle avoidance optimization strategy to obtain the target trajectory information, and the instantaneous impact force characteristics of the bipedal robot are identified based on the current external force information. Based on the instantaneous impact force characteristics of the bipedal robot, an impact resistance strategy is generated for the bipedal robot, and based on the impact resistance strategy and the target trajectory information, the bipedal robot is controlled to walk.

2. The method according to claim 1, characterized in that, The step of identifying the bipedal robot's travel environment distribution information based on the environmental image data includes: The environmental image data is divided into sub-environmental image data corresponding to each travel segment; the travel segment includes a short-range segment, a medium-range segment, and a long-range segment; Based on the sub-environmental image data corresponding to each of the travel segments, the environmental distribution information of each of the travel segments is identified through the travel environment analysis strategy of each travel segment; The environmental distribution information of each travel segment is arranged in order of distance to obtain the travel environment distribution information of the bipedal robot.

3. The method according to claim 2, characterized in that, The step of adjusting the current trajectory information of the bipedal robot based on the travel environment distribution information and using a trajectory obstacle avoidance optimization strategy to obtain target trajectory information includes: When the travel segment is a short-distance segment, based on the environmental distribution information, the distribution information of each obstacle range, the distribution information of the road range, and the distribution information of the slope change of the travel segment are identified. Based on the distribution information of each obstacle range and the distribution information of the road range of the travel segment, a first trajectory optimization strategy is applied to the current sub-trajectory of the travel segment to obtain the first optimized trajectory information of the travel segment. Based on the slope variation distribution information of the travel section, the first optimized trajectory information of the travel section is adjusted through the trajectory optimization network to obtain the target sub-optimized trajectory of the travel section; When the travel segment is a medium-range segment, based on the environmental distribution information, the road range distribution information and the slope distribution information of the travel segment are identified, and based on the road range distribution information and the slope distribution information of the travel segment, the target sub-optimized trajectory of the travel segment is obtained through the second trajectory optimization strategy. When the travel segment is a remote segment, based on the environmental distribution information, the slope change distribution information of the travel segment is identified, and based on the slope change distribution information of the travel segment, the current sub-trajectory of the travel segment is adjusted through the trajectory optimization network to obtain the target sub-optimized trajectory of the travel segment; The target sub-optimized trajectories of each travel segment are arranged in order of distance from each travel segment to obtain the target trajectory information.

4. The method according to claim 1, characterized in that, The step of identifying the instantaneous impact force characteristics of the bipedal robot based on the current external force information includes: The system collects the force position information of the bipedal robot and, based on the current external force information, identifies the impact direction and the impact intensity of the instantaneous impact force. Based on the force position information of the bipedal robot and the impact direction of the bipedal robot, the driving interference direction of the bipedal robot is identified. Based on the driving interference direction of the bipedal robot, the impact direction of the instantaneous impact force, and the impact force degree of the instantaneous impact force, the driving interference force degree of the bipedal robot is identified through a dynamic algorithm. The driving interference direction and the driving interference force of the bipedal robot are used as the instantaneous impact force characteristics of the bipedal robot.

5. The method according to claim 4, characterized in that, Based on the instantaneous impact force characteristics of the bipedal robot, an impact resistance strategy is generated to create an impact resistance scheme for the bipedal robot, including: The current motion state data of the bipedal robot is acquired, and based on the current motion state data of the bipedal robot, the current direction of travel and the current driving force of the bipedal robot are identified. Based on the current direction of travel of the bipedal robot, the current driving force level of the bipedal robot, the driving interference direction of the bipedal robot, and the driving interference force level of the bipedal robot, calculate the driving angle deviation information and the driving force deviation information of the bipedal robot. Based on the driving angle deviation information and driving force deviation information of the bipedal robot, an impact resistance scheme for the bipedal robot is generated through a driving control strategy.

6. The method according to claim 1, characterized in that, The step of controlling the bipedal robot to walk based on the impact resistance scheme and the target trajectory information includes: Based on the target trajectory information, the joint trajectory information of the bipedal robot is generated using a trajectory inverse algorithm. Based on the aforementioned impact resistance scheme, drive control information for the bipedal robot is generated; Based on the joint trajectory information and the drive control information, the bipedal robot is controlled to walk.

7. A stable walking control device for a bipedal robot, characterized in that, The device includes: The acquisition module is used to acquire environmental image data of the bipedal robot's gaze range, the current trajectory information of the bipedal robot, and the current external force information of the bipedal robot, and to identify the distribution information of the bipedal robot's travel environment based on the environmental image data; The identification module is used to adjust the current trajectory information of the bipedal robot based on the travel environment distribution information and through the trajectory obstacle avoidance optimization strategy to obtain the target trajectory information, and to identify the instantaneous impact force characteristics of the bipedal robot based on the current external force information. The control module is used to generate an impact resistance scheme for the bipedal robot based on the instantaneous impact force characteristics of the bipedal robot and an impact resistance strategy, and to control the bipedal robot to walk based on the impact resistance scheme and the target trajectory information.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

Images