Robot control method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a robot control method and device, electronic equipment and a storage medium, and relates to the technical field of robots. The method comprises the following steps: acquiring formation information of a plurality of leader robots, a first dynamic model of the plurality of leader robots and a second dynamic model of a follower robot; acquiring a first state observation value of each leader robot; for each leader robot, controlling the leader robot to move to a target position according to the formation information, the first dynamic model and the first state observation value of the leader robot; acquiring a second state observation value of each follower robot; and for each follower robot, controlling the follower robot to move into the minimum convex polygon formed by the plurality of leader robots according to the second state observation value and the second dynamic model of the follower robot. According to the embodiment of the invention, the formation precision of the multi-robot formation control process can be improved.

Description

Technical Field

[0001] This application relates to the field of robot technology, and in particular, to a robot control method, device, electronic device, and storage medium. Background Art

[0002] To further enhance the functions of robots, when performing tasks with higher complexity, multiple robots can coordinate their actions through formation control technology. And through precise position synchronization and time coordination, multiple robots can form a formation to jointly overcome environmental challenges and improve the execution efficiency and safety of tasks. Multiple robots usually include a leader robot and follower robots. By performing formation control on the leader robot and then controlling the follower robots to enter the convex hull area (i.e., the minimum convex polygon) formed by the leader robot, the follower robots can effectively move within the convex hull area formed by the leader robot, realizing formation maintenance in the formation and effective response of each robot to the leader robot, thereby enhancing the flexibility of the formation and improving the ability of multiple robots to adapt to different environments.

[0003] However, although existing formation control technologies have made progress in many aspects, most formation control technologies have the problem of low formation accuracy when applied to scenarios with high dynamic and high real-time requirements. Summary of the Invention

[0004] The main purpose of the embodiments of this application is to propose a robot control method, device, electronic device, and storage medium, aiming to improve the formation accuracy in the process of multi-robot formation control.

[0005] To achieve the above object, a first aspect of the embodiments of this application proposes a robot control method, which is used to control a robot system. The robot system includes multiple leader robots and at least one follower robot, and the follower robot moves following the multiple leader robots. The method includes: Obtain the formation information of multiple leader robots, a pre-constructed first dynamic model of the multiple leader robots, and a pre-constructed second dynamic model of the follower robot; the formation information is used to indicate the target position of each leader robot; Obtain a first state observation value of each leader robot, where the first state observation value is obtained by the leader robot estimating the motion state of a virtual leader based on a pre-constructed third dynamic model of the virtual leader and a preset adjacency matrix. The virtual leader is used to guide the movement of the multiple leader robots, and the preset adjacency matrix is used to describe the communication connection relationship between the robots included in the robot system; For each of the leader robots, control the leader robot to move to the target position according to the formation information, the first dynamic model, and the first state observation value of the leader robot; Obtain the second state observation value of each follower robot, where the second state observation value is an estimation of the motion state of the follower robot by the follower robot based on the first state observation value and the formation information; For each of the follower robots, control the follower robot to move into the minimum convex polygon formed by the multiple leader robots according to the second state observation value of the follower robot and the second dynamic model.

[0006] To achieve the above object, a second aspect of the embodiments of the present application provides a robot control device, which is used to control a robot system. The robot system includes multiple leader robots and at least one follower robot, and the follower robot moves following the multiple leader robots. The device includes: A first acquisition module, configured to acquire the formation information of the multiple leader robots, the pre-constructed first dynamic model of the multiple leader robots, and the pre-constructed second dynamic model of the follower robots; the formation information is used to indicate the target position of each leader robot; A second acquisition module, configured to acquire the first state observation value of each leader robot, where the first state observation value is an estimation of the motion state of the virtual leader by the leader robot based on the pre-constructed third dynamic model of the virtual leader and the preset adjacency matrix. The virtual leader is used to guide the movement of the multiple leader robots, and the preset adjacency matrix is used to describe the communication connection relationship between the robots included in the robot system; A first control module, configured to, for each of the leader robots, control the leader robot to move to the target position according to the formation information, the first dynamic model, and the first state observation value of the leader robot; A third acquisition module, configured to acquire the second state observation value of each follower robot, where the second state observation value is an estimation of the motion state of the follower robot by the follower robot based on the first state observation value and the formation information; A second control module, configured to, for each of the follower robots, control the follower robot to move into the minimum convex polygon formed by the multiple leader robots according to the second state observation value of the follower robot and the second dynamic model.

[0007] To achieve the above object, a third aspect of the embodiments of the present application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the robot control method described in the first aspect above is implemented.

[0008] To achieve the above object, a fourth aspect of the embodiments of the present application provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the robot control method described in the first aspect above is implemented.

[0009] The robot control method, device, electronic device, and storage medium provided by the present application obtain the formation information of multiple leader robots, the first dynamic models of multiple leader robots, and the second dynamic model of a follower robot, and obtain the first state observation value of each leader robot. Subsequently, for each leader robot, according to the formation information, the first dynamic model, and the first state observation value of the leader robot, the leader robot is controlled to move to the target position indicated by the formation information. After each leader robot moves to the target position, the second state observation value of the follower robot is obtained. Furthermore, for each follower robot, according to the second state observation value and the second dynamic model of the follower robot, the follower robot is controlled to move into the minimum convex polygon formed by multiple leader robots. Through the above steps, the state of the target (virtual leader or follower robot) can be estimated inside each robot in the robot system, and then the formation-inclusion control of the robot system can be completed based on the estimated state observation value. In this way, when applied to scenarios with high dynamic and high real-time requirements, the formation accuracy of the multi-robot formation control process can be improved. Description of the Drawings

[0010] Figure 1 is a flowchart of the robot control method provided by the embodiments of the present application; Figure 2 is a schematic structural diagram of the robot control device provided by the embodiments of the present application; Figure 3 is a schematic hardware structure diagram of the electronic device provided by the embodiments of the present application. Detailed Embodiments

[0011] In order to make the purpose, technical solutions, and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0012] It should be noted that although the functional modules are divided in the device schematic diagram and the logical sequence is shown in the flowchart, in some cases, the steps shown or described can be executed in a different module division from that in the device or a different order from that in the flowchart. Terms such as "first" and "second" in the specification, claims, and the above-mentioned drawings are used to distinguish similar objects and do not necessarily describe a specific order or sequence.

[0013] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0014] To solve the problems of the prior art, embodiments of this application provide a robot control method, device, electronic device, and storage medium, aiming to improve the formation accuracy in the multi-robot formation control process.

[0015] The robot control method, device, electronic device, and storage medium provided by the embodiments of this application are specifically described through the following embodiments. First, the robot control method in the embodiments of this application is described.

[0016] Embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology, and application system that uses a digital computer or a machine controlled by a digital computer to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to obtain the best results.

[0017] Artificial intelligence basic technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, and mechatronics. Artificial intelligence software technologies mainly include several major directions such as computer vision technology, robotics, biometric technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0018] The robot control method provided by the embodiments of this application relates to the field of robot technology. The robot control method provided by the embodiments of this application can be applied to a terminal, or to a server side, or can also be software running on a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or can be configured as a server cluster or a distributed system composed of multiple physical servers, or can also be configured as a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks, and big data and artificial intelligence platforms; the software can be an application that implements the robot control method, etc., but is not limited to the above forms.

[0019] This application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, small computers, large computers, distributed computing environments including any of the above systems or devices, and so on. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This application can also be practiced in a distributed computing environment where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0020] Figure 1 is a flowchart of the robot control method provided by the embodiments of this application. Please refer to Figure 1 . The robot control method provided by the embodiments of this application can be applied to an electronic device. The robot control method is used to control a robot system, and the robot system includes multiple leader robots and at least one follower robot, and the follower robot moves following the multiple leader robots. Figure 1 The method in

[0021] Step 101, obtain the formation information of multiple leader robots, a pre-constructed first dynamic model of the multiple leader robots, and a pre-constructed second dynamic model of the follower robot; the formation information is used to indicate the target position of each leader robot; Step 102: Obtain the first state observation value of each of the leader robots. The first state observation value is obtained by the leader robot estimating the motion state of the virtual leader based on the pre-constructed third dynamic model of the virtual leader and the preset adjacency matrix. The virtual leader is used to guide the movement of multiple leader robots, and the preset adjacency matrix is used to describe the communication connection relationship between the robots included in the robot system. Step 103: For each of the leader robots, control the leader robot to move to the target position according to the formation information, the first dynamic model, and the first state observation value of the leader robot. Step 104: Obtain the second state observation value of each of the follower robots. The second state observation value is obtained by the follower robot estimating the motion state of the follower robot based on the first state observation value and the formation information. Step 105: For each of the follower robots, control the follower robot to move into the minimum convex polygon formed by multiple leader robots according to the second state observation value of the follower robot and the second dynamic model.

[0022] The multiple robots included in the robot system can be quadruped robots, can be wheeled robots, or can also be tracked robots, which is not limited here. During the control process, the formation information of multiple leader robots, the pre-constructed first dynamic model of multiple leader robots, and the pre-constructed second dynamic model of follower robots can be obtained. Specifically, assume that the robot system can include n follower robots and m leader robots. Among them, n is an integer greater than or equal to 1, and m is an integer greater than 1. Then, the number of the follower robot can be an integer in the range from 1 to n, and the number of the leader robot can be any integer in n + 1, n + 2,......, n + m. The formation information is used to indicate the target position of each leader robot. The formation information can include the formation offset of each leader robot relative to the virtual leader, that is , where is the formation information at the current moment , respectively represent the formation offsets of each leader robot relative to the virtual leader at the current moment , , is any integer in this range.

[0023] The first dynamic model is used to describe the relationship between the input of the leader robot and its motion state, and the relationship between the motion state of the leader robot and its output. The first dynamic model of each leader robot satisfies the following formula (1): (1); Wherein, is the current moment, is the current moment the motion state of the -th leader robot at time , represents a real column vector with a dimension of ; is the input of the -th leader robot at time , , represents a real column vector with a dimension of , is the input time delay of the -th leader robot; is the output of the -th leader robot at the current moment , , represents a real column vector with a dimension of ; is the state matrix of the -th leader robot, reflecting the evolution relationship of the motion state (such as position, speed, attitude angle) of the -th leader robot, , represents a real matrix with rows and columns; is the input matrix of the -th leader robot, reflecting the action path of the input on the motion state, , represents a real matrix with rows and columns; is the output matrix of the -th leader robot, , represents a real matrix with rows and columns.

[0024] The second dynamic model is used to describe the relationship between the input of the follower robot and the motion state of the follower robot, as well as the relationship between the motion state of the follower robot and the output of the follower robot. The second dynamic model of each follower robot satisfies the following formula (2): (2); Wherein, is the motion state of the -th follower robot at the current moment , is any integer in , , represents a real column vector with a dimension of ; is the input of the -th follower robot at the moment , , represents a real column vector with a dimension of , is the input time delay of the -th follower robot; is the output of the -th follower robot at the current moment , , represents a real column vector with a dimension of ; is the state matrix of the -th follower robot, reflecting the evolution relationship of the motion state (such as position, speed, attitude angle) of the -th follower robot, , represents a real matrix with rows and columns; is the input matrix of the -th follower robot, reflecting the action path of the input on the motion state, , represents a real matrix with rows and columns; is the output matrix of the -th follower robot, , represents a real matrix with rows and columns.

[0025] The virtual leader is used to guide the movement of multiple leader robots and is not a physically existing robot. Based on the motion state of the virtual leader and the formation information of the multiple leader robots, the target position of each leader robot can be determined. Therefore, each leader robot can estimate the motion state of the virtual leader, thereby obtaining a first state observation value for each leader robot. Specifically, each leader robot can estimate the motion state of the virtual leader based on the third dynamic model of the virtual leader and a preset adjacency matrix, thereby obtaining the first state observation value, that is, the estimated value of the motion state of the virtual leader obtained after the leader robot estimates the motion state of the virtual leader. Among them, the preset adjacency matrix is used to describe the communication connection relationship between the robots included in the robot system, that is, the communication connection relationship between the multiple leader robots, or the communication connection relationship between the leader robot and the follower robot. The third dynamic model is used to describe the motion state and output of the virtual leader. The third dynamic model satisfies the following formula (3): (3); Wherein, represents the motion state of the virtual leader at the current time when, , represents a real column vector of dimension ; represents the output of the virtual leader at the current time when, ; represents the state matrix of the virtual leader, which is a matrix used to describe how the motion state of the virtual leader changes with time, , represents a real matrix with rows and columns; represents the output mapping matrix of the virtual leader, which is a matrix used to describe how to map the output from the motion state of the virtual leader, , represents a real matrix with rows and columns.

[0026] And, . The state matrix of the virtual leader satisfies the following formula (4): (4); Wherein, is the The first relative motion matrix of the leader robot, which is used to represent the relative motion relationship of the target between the th leader robot and the virtual leader, represents the control compensation term of the th leader robot, which is used to describe the dynamic compensation amount applied by the

[0027] th leader robot to correct the influence of disturbances. th leader robot, according to the formation offset corresponding to the leader robot in the formation information , the first dynamic model of the leader robot, and the first state observation value of the leader robot, the movement of the leader robot can be controlled to move to the target position. Referring to the above steps, the control of the position of each leader robot can be completed, so that multiple leader robots form the formation indicated by the formation information.

[0028] Subsequently, the electronic device can obtain the second state observation value of each follower robot for subsequent control of the follower robot. Among them, the second state observation value is obtained by the follower robot estimating the motion state of the follower robot based on the first state observation value and the formation information. For the th follower robot, according to the second state observation value of the th follower robot and the second dynamic model of the th follower robot, the follower robot can be controlled to move into the smallest convex polygon formed after the formation indicated by the formation information composed of multiple leader robots, thereby completing the formation-inclusion control process of the robot system, enabling multiple robots included in the robot system to jointly execute tasks and improving the execution efficiency of the tasks.

[0029] Steps 101 to 105 illustrated in the embodiments of the present application, by obtaining the formation information of multiple leader robots, the first dynamic model of multiple leader robots, and the second dynamic model of follower robots, and obtaining the first state observation value of each leader robot. Subsequently, for each leader robot, according to the formation information, the first dynamic model, and the first state observation value of the leader robot, control the leader robot to move to the target position indicated by the formation information. After each leader robot moves to the target position, obtain the second state observation value of the follower robot. Furthermore, for each follower robot, according to the second state observation value and the second dynamic model of the follower robot, control the follower robot to move into the minimum convex polygon formed by multiple leader robots. Through the above steps, the state of the target (virtual leader or follower robot) can be estimated inside each robot in the robot system, and then the formation-including control of the robot system can be completed based on the estimated state observation values. In this way, when applied to scenarios with high dynamic and high real-time requirements, the formation accuracy of the multi-robot formation control process can be improved.

[0030] In some embodiments, the first state observation value is obtained according to the following process: Estimate the state matrix of the virtual leader according to the third dynamic model and the preset adjacency matrix to obtain the first state matrix observation value of the leader robot; Estimate the motion state of the virtual leader according to the third dynamic model, the first state matrix observation value, and the preset adjacency matrix to obtain the first state observation value.

[0031] To achieve effective tracking of the desired motion trajectory by multiple leader robots, each leader robot can estimate the motion state of the virtual leader based on the third dynamic model and the preset adjacency matrix to obtain the first state observation value. Specifically, according to the third dynamic model and the preset adjacency matrix, estimate the state matrix of the virtual leader to obtain the first state matrix observation value of the leader robot, that is, the estimated value of the state matrix obtained after the leader robot estimates the state matrix of the virtual leader. Among them, the state matrix of the virtual leader can include the motion states of the virtual leader at multiple moments. The first state matrix observation value of the leader robot satisfies Equation (5): (5); Wherein, is the first state matrix observation value of the th leader robot, is the first preset control parameter, is the current moment in the preset adjacency matrix when the The adjacency matrix of a leader robot and the virtual leader, The state matrix representing the virtual leader, At the current moment in the preset adjacency matrix when the th leader robot and the th leader robot's adjacency matrix, For the th leader robot's first state matrix observation value.

[0032] According to the third dynamic model, the first state matrix observation value, and the preset adjacency matrix, the motion state of the virtual leader can be estimated to obtain the first state observation value. The first state observation value of the leader robot satisfies Equation (6): (6); where, For the th leader robot's first state observation value, Is the second preset control parameter, Is the motion state of the virtual leader, For the th leader robot's first state observation value.

[0033] Through the above steps, each leader robot can observe the first state observation value. Based on the first state observation value, each leader robot can be controlled to move to the target position of each leader robot, so that multiple leader robots form the formation indicated by the formation information.

[0034] In some embodiments, for each of the leader robots, controlling the leader robot to move to the target position according to the formation information, the first dynamic model, and the first state observation value of the leader robot includes: For each of the leader robots, determine the trajectory deviation corresponding to the leader robot according to the motion state of the leader robot, the formation information, the first state observation value, and the first relative motion matrix; the first relative motion matrix is used to represent the relative motion relationship of the target between the leader robot and the virtual leader, the trajectory deviation is the deviation between the motion state of the leader robot and the target trajectory of the leader robot, and the target trajectory of the leader robot is determined based on the first state observation value and the formation information; Determine the first target input quantity of the leader robot according to the first gain matrix of the leader robot, the trajectory deviation corresponding to the leader robot, the input quantity of the leader robot, and the input time delay of the leader robot; Control the leader robot to move to the target position according to the first target input quantity of the leader robot; Among them, the input quantity of the leader robot and the input time delay of the leader robot are determined according to the first dynamic model of the leader robot.

[0035] For each leader robot, according to the formation information, the first dynamic model, and the first state observation value of the leader robot, the leader robot can be controlled to move to the target position. Specifically, in order to form an expected time-varying formation under any bounded initial conditions, that is, the formation indicated by the formation information, the formula (7) needs to be satisfied: (7); Then according to formula (4), it can be obtained that: .

[0036] If , then there is .

[0037] Based on this, let , be the trajectory deviation corresponding to the th leader robot at the current time .

[0038] However, since each robot cannot obtain the motion state of the leader robot in real time, therefore, the motion state of the leader robot can be estimated, that is, the trajectory deviation corresponding to the leader robot can be estimated. Specifically, according to the motion state of the leader robot, the formation information, the first state observation value, and the first relative motion matrix, the trajectory deviation corresponding to the leader robot can be estimated. Among them, the trajectory deviation is the deviation between the motion state of the leader robot and the target trajectory of the leader robot, and the target trajectory of the leader robot is determined based on the first state observation value and the formation information. The target trajectory of the leader robot at the current time . In the actual control process, the first state observation value of the th leader robot can be approximately regarded as the motion state of the virtual leader. Thus, the estimated value of the trajectory deviation satisfies formula (8): (8); Among them, is the estimated value of the trajectory deviation of the th leader robot at the current time .

[0039] Subsequently, based on the first gain matrix of the leader robot, the estimated value of the trajectory deviation corresponding to the leader robot (which can be approximately regarded as the trajectory deviation corresponding to the leader robot), the input quantity of the leader robot, and the input time delay of the leader robot, the first target input quantity of the leader robot can be determined. Subsequently, based on the first target input quantity of the leader robot, the leader robot can be controlled to move to the target position. Moreover, since the first target input quantity has compensated for the input time delay, the formation formed by multiple leader robots can be made more stable, and the formation accuracy and response speed of the robots in a complex environment can be improved.

[0040] In some embodiments, the determining the first target input quantity of the leader robot according to the first gain matrix of the leader robot, the trajectory deviation corresponding to the leader robot, the input quantity of the leader robot, and the input time delay of the leader robot includes: Determining the trajectory deviation after a first preset time duration according to the input time delay of the leader robot and the trajectory deviation corresponding to the leader robot, where the first preset time duration is determined based on the input time delay of the leader robot; Performing time-delay correction on the input quantity of the leader robot according to the first gain matrix and the trajectory deviation after the first preset time duration to obtain the first target input quantity of the leader robot.

[0041] According to the first gain matrix of the leader robot, the trajectory deviation corresponding to the leader robot, the input quantity of the leader robot, and the input time delay of the leader robot, the first target input quantity of the leader robot can be determined. Specifically, after taking the derivative of the trajectory deviation the following formula (9) can be obtained: (9); Wherein, , is the corrected first input quantity of the th leader robot, that is, the input quantity of the th leader robot at after removing the virtual leader motion state compensation term and the formation information compensation term. Then, through the partial differential transformation as shown in formula (10), and can be obtained.

[0042] The following is formula (10): (10); Wherein, is a spatio-temporal variable, which is a redefinition of the original time axis by .

[0043] In this way, the following equation (11) can be obtained: (11); where is the natural constant.

[0044] After transforming equation (9), the following can be obtained . By using the constant transformation equation, the future motion state can be predicted. Therefore, the following equation (12) can be obtained: (12); At this time, the following can be designed . Where is the first gain matrix, is the integral variable, which is an intermediate variable replacing time in the integral term.

[0045] Since is a Hurwitz matrix, the following equation (13) can be further derived: (13); Therefore, the following equation (14) can be obtained: (14); Then, can be derived as . Since is a Hurwitz matrix, then . In this way, multiple leader robots can form a formation indicated by the formation information under arbitrary bounded initial conditions.

[0046] Based on the above derivation process, it can be known that after taking the derivative of the estimated value of the trajectory deviation of the -th leader robot at the current time , the following equation (15) can be obtained: (15); where , is the corrected second input quantity of the -th leader robot, that is, the input quantity after removing the first state observation value compensation term and the formation information compensation term from the input quantity of the -th leader robot at the -th moment. Also, for the -th leader robot at the current time , the first intermediate variable can be constructed, that is: (16); where is for the -th leader robot at the current time The first intermediate variable constructed by a leader robot.

[0047] Meanwhile, it can be defined that , is the first deviation value, that is, the deviation between the first state matrix observation value of the th leader robot and the state matrix of the virtual leader. And define , as the second deviation value, that is, the deviation between the first state observation value of the th leader robot and the motion state of the virtual leader. It can be understood that to ensure the control accuracy, , , so it can be obtained that . Then, after deriving Equation (15), Equation (17) can be obtained: (17); Subsequently, perform partial differential transformation on the first state observation value of the th leader robot at the current moment , and perform partial differential transformation on , and Equation (18) can be obtained: (18); Then, it can be calculated that . Among them, .

[0048] is the second intermediate variable constructed for the th leader robot at the current moment . From , it can be obtained that .

[0049] Based on this, according to the processing method of Equation (13), Equation (19) can be designed as: (19); Therefore, it can be obtained that the first target input quantity of the leader robot should satisfy Equation (20): (20); That is, the input quantity sent by the electronic device to the th leader robot at the th moment to control the th leader robot, after passing through the input time delay , acts on the th leader robot. And is the The first target input quantity of the leader robot at the current moment when the input time delay of the th leader robot has been compensated, and it is the actual control input quantity acting on the leader robot.

[0050] In this way, the first target input quantity of the leader robot can be determined, so that subsequently, according to the first target input quantity, the leader robot can be controlled to move to the target position.

[0051] In some embodiments, the second state observation value is obtained according to the following process: Estimate the state matrix of the virtual leader to obtain the second state matrix observation value of the follower robot; Based on the second state matrix observation value, the target trajectory of each leader robot, the preset adjacency matrix, and the preset communication matrix, estimate the motion state of the follower robot to obtain the second state observation value of the follower robot; wherein, the target trajectory of each leader robot is determined based on the first state observation value and the formation information, and the preset communication matrix is used to describe the communication state between the robots included in the robot system.

[0052] According to the first state observation value and the formation information, the follower robot can estimate the motion state of the follower robot, thereby obtaining the second state observation value. Specifically, first, the follower robot can estimate the state matrix of the virtual leader to obtain the second state matrix observation value of the follower robot. It should be noted that the second state matrix observation value of the follower robot refers to the estimated value of the virtual leader state matrix obtained by the follower robot's estimation of the virtual leader's state matrix, rather than the estimated value of the follower robot's own state matrix. The second state matrix observation value of the follower robot satisfies Equation (21): (21); where is the second state matrix observation value of the th follower robot, is the third preset control parameter, is the adjacency matrix of the th follower robot and the th follower robot in the preset adjacency matrix, equals 1, indicating that communication occurs between the two robots, otherwise equals 0, is the second state matrix observation value of the th follower robot​ is the communication status between the th follower robot and the th leader robot in the preset communication matrix.

[0053] Based on the second state matrix observation value, the target trajectory of each leader robot, the preset adjacency matrix, and the preset communication matrix, the motion state of the follower robot can be estimated to obtain the second state observation value of the follower robot. The second state observation value of the follower robot satisfies Equation (22): (22); where is the second state observation value of the th follower robot, is the fourth preset control parameter, is the second state observation value of the th follower robot, is the th leader robot's target trajectory. Through the above steps, the follower robot can obtain the second state observation value after estimating the motion state of the follower robot. In this way, the electronic device can obtain the second state observation value of each follower robot, which is convenient for subsequent control of the follower robot based on the second state observation value.

[0054] In some embodiments, for each of the follower robots, according to the second state observation value of the follower robot and the second dynamic model, controlling the follower robot to move into the minimum convex polygon formed by the multiple leader robots includes: For each of the follower robots, determine the first error of the follower robot according to the second state matrix observation value and the target state of the follower robot, where the target state of the follower robot is determined based on the motion states of the multiple leader robots; Determine the second error of the follower robot according to the state matrix of the follower robot, the input time delay of the follower robot, the input quantity of the follower robot, the input matrix of the follower robot, the second state observation value, the second relative motion matrix of the follower robot, the first error, and the second state matrix observation value, where the second relative motion matrix is used to represent the relative motion relationship of the target between the follower robot and the multiple leader robots; Determine the second target input quantity of the follower robot according to the second gain matrix of the follower robot, the input quantity of the follower robot, the input time delay of the follower robot, and the second error; Control the follower robot to move into the minimum convex polygon formed by multiple leader robots according to the second target input quantity; Among them, the state matrix of the follower robot, the input time delay of the follower robot, the input quantity of the follower robot, and the input matrix of the follower robot are all determined according to the second dynamic model.

[0055] For each follower robot, according to the second state observation value and the second dynamic model, the follower robot can be controlled to move into the minimum convex polygon formed by multiple leader robots, thereby completing the formation-inclusion control process of the robot system. Specifically, in order for the follower robot to enter the minimum convex polygon formed by multiple leader robots, it is necessary to satisfy , is the output of any leader robot, represents the column space, is the set of outputs of the leader robots, , .

[0056] Since multiple leader robots have formed the formation indicated by the formation information, Equation (7) is already satisfied. Then, at this time, it can be set that , where is the th target output of the leader robot. Then, it can be obtained that , is the set of target outputs of the leader robots, , .

[0057] And, assume that the real part of the eigenvalue of the state matrix of the virtual leader is non-negative (to ensure that the second target input quantity of the follower robot is solvable). Assume that the matrix is stabilizable, observable (indicating that the virtual leader is marginally stable and can generate bounded signals such as sine signals, cosine signals, and their combinations).

[0058] Assume: , to ensure that the second target input quantity of the follower robot is solvable, where represents the identity matrix of dimension 1, represents the set of eigenvalues of the state matrix of the virtual leader.

[0059] Subsequently, the relative output information of the robot system can be defined to satisfy Equation (23): (23); Among them, The output matrix among multiple robots included in the robot system is the output of the th follower robot, and is the output of the th follower robot, and

[0060] is the output of the th leader robot. At this time, according to the above formula, formula (24) can be obtained: where is a matrix including the output matrix among multiple robots, is the first Kronecker product matrix, and represents the Kronecker product, is a matrix including the output of each follower robot, .

[0061] Then, the third error (i.e., the inclusion error when the follower robot enters the minimum convex polygon formed by multiple leader robots) can be defined to satisfy formula (25): (25); where is the third error.

[0062] In order for the follower robot to enter the minimum convex polygon formed by multiple leader robots, formula (25) can be transformed to obtain formula (26): (26); where is the second Kronecker product matrix, .

[0063] At , the follower robot can enter the minimum convex polygon formed by multiple leader robots.

[0064] Moreover, since the target trajectory of the leader robot at the current time , there exists formula (27): (27); Therefore, an output regulation equation with a unique solution as formula (28) can be designed: (28); where is the second relative motion matrix, used to represent the The relative motion relationship of the target between a follower robot and multiple leader robots is the control compensation term for the th follower robot.

[0065] Based on the second state matrix observation value and the target state of the follower robot, the first error of the follower robot can be determined. Among them, the target state of the follower robot is estimated based on the motion states of multiple leader robots. The target state of the follower robot satisfies Equation (29): (29); where is the target state of the follower robot, and the motion state of the follower robot can be adjusted with reference to the target state, so as to gradually approach the minimum convex polygon formed by multiple leader robots; is a matrix with a dimension of ; is the communication parameter matrix of the th leader robot in the directed switching topology graph , , is the communication state of the th leader robot in the preset communication matrix, is the Laplacian matrix; is the communication parameter matrix of the th leader robot in the directed switching topology graph , , is the communication state of the th leader robot in the preset communication matrix; represents the third Kronecker product matrix, , is a 1 matrix with a dimension of . The directed switching topology graph is used to describe the conversion relationship between different states or nodes in the robot system. Assuming that in the directed switching topology graph , there is a directed path from each leader robot to each follower robot, then and in the directed switching topology graph are both positive definite and non-singular virtual leader state matrices, and exist and are non-singular.

[0066] Then, define the first error , where is the set of second state observation values of the follower robot , then, after taking the derivative of the first error, Equation (30) can be obtained: (30); Wherein, , is the third deviation value, that is, the deviation between the second state matrix observation value of the -th follower robot and the state matrix of the virtual leader, , is a matrix including the second state matrix observation values of each follower robot, , is a matrix including the third deviation values corresponding to each follower robot, represents extracting the diagonal elements in the matrix. Then there is Equation (31): (31); At this time, is a positive definite matrix, is a matrix with a dimension of , then . According to Equation (31), it can be obtained that approaches 0, then an appropriate fourth preset control parameter can be determined to ensure that is stable.

[0067] According to the state matrix of the follower robot, the input time delay of the follower robot, the input quantity of the follower robot, the input matrix of the follower robot, the second state observation value, the second relative motion matrix of the follower robot, the first error, and the second state matrix observation value, the second error of the follower robot can be determined. Specifically, the second error can be defined, where is a matrix including the second errors of each follower robot, , is a matrix including the motion states of each follower robot, , is the second relative motion matrix of the -th follower robot.

[0068] At this time, Equation (26) can be rewritten as the following Equation (32): (32); It is known that approaches 0. If approaches 0, then approaches 0. Therefore, the second target input quantity should make approach 0.

[0069] Let , To include The matrix of the input quantity of each follower robot at the moment, then we can get formula (33): (33); Then, we can get formula (34): (34); in, For the The first error of the follower robot.

[0070] Can be defined , For the The third input after the follower robot is corrected, that is, The moment The input of the follower robot after removing the second state observation value compensation term, , For the current moment Time The third intermediate variable of the construction of the follower robot, then equation (34) becomes In this way, the second error of the follower robot can be obtained.

[0071] Subsequently, the electronic device can determine the second target input of the follower robot according to the second gain matrix of the follower robot, the input of the follower robot, the input time lag of the follower robot and the second error. According to the second target input, each follower robot can be controlled to move into the minimum convex polygon formed by the multiple leader robots, thereby completing the containment control of the follower robot.

[0072] In some embodiments, determining the second target input amount of the follower robot according to the second gain matrix of the follower robot, the input amount of the follower robot, the input time lag of the follower robot, and the second error comprises: determining a second error after a second preset time period according to the input time lag of the follower robot and the second error, wherein the second preset time period is determined based on the input time lag of the follower robot; According to the second gain matrix and the second error after the second preset time length, a time lag correction is performed on the input amount of the follower robot to obtain a second target input amount of the follower robot.

[0073] According to the second gain matrix of the follower robot, the input of the follower robot, the input time delay of the follower robot, and the second error, the second target input of the follower robot can be determined. Specifically, in order for the follower robot to enter the smallest convex polygon formed by multiple leader robots, it is necessary to satisfy , where is the second gain matrix. By adjusting the second gain matrix , it can be made that is a Hurwitz matrix, and approaches 0, approaches 0, so approaches 0.

[0074] According to the input time delay and the second error of the follower robot, determine the second error after the second preset time. That is, according to the input time delay and the second error of the follower robot, can be obtained, where is the second target input of the th follower robot, is at time the th follower robot's second error.

[0075] The partial differential transformation is as shown in Equation (35): (35); Therefore, it can be determined that after performing partial differential transformation on , can be obtained.

[0076] Furthermore, the following Equation (36) can be obtained: (36); Thus, according to the second gain matrix and the second error after the second preset time, perform time delay correction on the input of the follower robot to obtain the second target input of the follower robot. It can be obtained that at the current time the th follower robot's second target input satisfies Equation (37): (37); In this way, the second target input of the follower robot can be determined, which is convenient for subsequent control of the follower robot to make the follower robot enter the smallest convex polygon formed by multiple leader robots.

[0077] The following is an example of the robot control method provided by the embodiments of the present application.

[0078] It is assumed that the robotic system includes four follower robots (i.e., the first robot, the second robot, the third robot, and the fourth robot) and three leader robots (i.e., the fifth robot, the sixth robot, and the seventh robot), and there is a virtual leader to guide the motion of the three leader robots.

[0079] At this time, it is set that the switching signal of the robotic system satisfies Equation (38): (38); Where, is the switching signal at the current time , is an integer greater than or equal to 0, is the switching period of the switching topological network.

[0080] The input time delays of the four follower robots ( , , , and ) are 0.2 seconds, and the input time delays of the three leader robots ( , , and ) are 0.25 seconds. The state matrices of the first robot (i.e., ) and the third robot (i.e., ) are: , and the input matrices of the first robot (i.e., ) and the third robot (i.e., ) are: , and the output matrices of the first robot (i.e., ) and the third robot (i.e., ) are: . The state matrices of the second robot (i.e., ) and the fourth robot (i.e., ) are: , and the input matrices of the second robot (i.e., ) and the fourth robot (i.e., ) are: , and the output matrices of the second robot (i.e., ) and the fourth robot (i.e., ) are: . The state matrices of the fifth robot (i.e., ), the sixth robot (i.e., ), and the seventh robot (i.e., ) are all: , and the output matrix of the fifth robot (i.e., ), the output matrix of the sixth robot (i.e., ), and the output matrix of the seventh robot (i.e., ) are both: . The state matrix of the virtual leader (i.e., ) is: , and the output matrix of the virtual leader (i.e., ) is: . And, a first preset control parameter , a second preset control parameter , a first gain matrix (including , or ) is: . A third preset control parameter , a fourth preset control parameter , a second gain matrix (including , , or ) is: .

[0081] Then, it can be known that the state generation signal of the virtual leader is a sine signal and can be successfully tracked by three leader robots. And, four follower robots are surrounded in the triangular formation formed by the three leader robots, and the control objective of formation-inclusion control can be achieved.

[0082] The robot control method provided by the embodiment of the present application designs an observer inside each robot, and the observer observes the motion state, precisely processes and overcomes the control challenges brought by time-delay heterogeneity. Even in a complex environment with different input time delays, it can ensure the accurate transmission and processing of information. And, by transforming the robot system into a first-order system, the design and implementation of the control strategy are simplified, making the formation control more stable and efficient. At the same time, the motion state of the virtual leader and the formation information of multiple leader robots are used to guide the actions of the follower robots, ensuring that the follower robots can effectively enter and maintain in the formation convex hull (i.e., the minimum convex polygon) formed by the leader robots. In addition, a feedback mechanism is designed, which can adjust the control strategy according to the real-time situation of the robot system to adapt to environmental changes and system errors.

[0083] Figure 2 is a schematic structural diagram of the robot control device provided by the embodiment of the present application. Please refer to Figure 2。The embodiments of the present application further provide a robot control device 200, which can implement the above-mentioned robot control method. The device 200 is used to control a robot system, the robot system includes multiple leader robots and at least one follower robot, and the follower robot moves following the multiple leader robots. The device includes: A first acquisition module 201, configured to acquire the formation information of the multiple leader robots, the pre-constructed first dynamic models of the multiple leader robots, and the pre-constructed second dynamic model of the follower robot; the formation information is used to indicate the target position of each leader robot; A second acquisition module 202, configured to acquire the first state observation value of each leader robot, where the first state observation value is obtained by the leader robot estimating the motion state of the virtual leader based on the pre-constructed third dynamic model of the virtual leader and a preset adjacency matrix, the virtual leader is used to guide the movement of the multiple leader robots, and the preset adjacency matrix is used to describe the communication connection relationship between the robots included in the robot system; A first control module 203, configured to, for each leader robot, control the leader robot to move to the target position according to the formation information, the first dynamic model, and the first state observation value of the leader robot; A third acquisition module 204, configured to acquire the second state observation value of each follower robot, where the second state observation value is obtained by the follower robot estimating the motion state of the follower robot based on the first state observation value and the formation information; A second control module 205, configured to, for each follower robot, control the follower robot to move into the minimum convex polygon formed by the multiple leader robots according to the second state observation value of the follower robot and the second dynamic model.

[0084] In some embodiments, the first state observation value is obtained according to the following process: Estimate the state matrix of the virtual leader according to the third dynamic model and the preset adjacency matrix to obtain the first state matrix observation value of the leader robot; Estimate the motion state of the virtual leader according to the third dynamic model, the first state matrix observation value, and the preset adjacency matrix to obtain the first state observation value.

[0085] In some embodiments, the first control module 203 includes: A first determination sub-module, configured to, for each of the leader robots, determine a trajectory deviation corresponding to the leader robot according to the motion state of the leader robot, the formation information, the first state observation value, and a first relative motion matrix; the first relative motion matrix is used to represent the relative motion relationship of the target between the leader robot and the virtual leader, the trajectory deviation is the deviation between the motion state of the leader robot and the target trajectory of the leader robot, and the target trajectory of the leader robot is determined based on the first state observation value and the formation information; A second determination sub-module, configured to determine a first target input quantity of the leader robot according to the first gain matrix of the leader robot, the trajectory deviation corresponding to the leader robot, the input quantity of the leader robot, and the input time delay of the leader robot; A first control sub-module, configured to control the leader robot to move to the target position according to the first target input quantity of the leader robot; Wherein, the input quantity and the input time delay of the leader robot are determined according to the first dynamic model of the leader robot.

[0086] In some embodiments, the second determination sub-module includes: A first determination unit, configured to determine a trajectory deviation after a first preset duration according to the input time delay of the leader robot and the trajectory deviation corresponding to the leader robot, and the first preset duration is determined based on the input time delay of the leader robot; A first correction unit, configured to perform time delay correction on the input quantity of the leader robot according to the first gain matrix and the trajectory deviation after the first preset duration, so as to obtain the first target input quantity of the leader robot.

[0087] In some embodiments, the second state observation value is obtained according to the following process: Estimate the state matrix of the virtual leader to obtain a second state matrix observation value of the follower robot; Based on the second state matrix observation value, the target trajectory of each leader robot, the preset adjacency matrix, and the preset communication matrix, estimate the motion state of the follower robot to obtain the second state observation value of the follower robot; Wherein, the target trajectory of each leader robot is determined based on the first state observation value and the formation information, and the preset communication matrix is used to describe the communication state between the robots included in the robot system.

[0088] In some embodiments, the second control module 205 includes: A third determination sub-module, configured to, for each of the follower robots, determine a first error of the follower robot according to the second state matrix observation value and the target state of the follower robot, where the target state of the follower robot is determined based on the motion states of a plurality of the leader robots; A fourth determination sub-module, configured to determine a second error of the follower robot according to the state matrix of the follower robot, the input time delay of the follower robot, the input quantity of the follower robot, the input matrix of the follower robot, the second state observation value, the second relative motion matrix of the follower robot, the first error, and the second state matrix observation value, where the second relative motion matrix is used to represent the relative motion relationship of the target between the follower robot and a plurality of the leader robots; A fifth determination sub-module, configured to determine a second target input quantity of the follower robot according to the second gain matrix of the follower robot, the input quantity of the follower robot, the input time delay of the follower robot, and the second error; A second control sub-module, configured to control the follower robot to move into a minimum convex polygon formed by a plurality of the leader robots according to the second target input quantity; Wherein, the state matrix of the follower robot, the input time delay of the follower robot, the input quantity of the follower robot, and the input matrix of the follower robot are all determined according to the second dynamic model.

[0089] In some embodiments, the fifth determination sub-module includes: A second determination unit, configured to determine a second error after a second preset time period according to the input time delay of the follower robot and the second error, where the second preset time period is determined based on the input time delay of the follower robot; A second correction unit, configured to perform time delay correction on the input quantity of the follower robot according to the second gain matrix and the second error after the second preset time period, to obtain the second target input quantity of the follower robot.

[0090] For the specific implementation manners of the robot control device 200, reference may be made to the specific embodiments of the above robot control method, which will not be elaborated herein.

[0091] An embodiment of the present application further provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the above robot control method is implemented. The electronic device may be any intelligent terminal including a tablet computer, a vehicle-mounted computer, etc.

[0092] Figure 3This is a schematic diagram of the hardware structure of the electronic device provided by the embodiments of the present application. Please refer to Figure 3 . The electronic device includes: A processor 301, which can be implemented in ways such as a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided by the embodiments of the present application; A memory 302, which can be implemented in forms such as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 302 can store an operating system and other application programs. When implementing the technical solutions provided by the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 302 and are called by the processor 301 to execute the robot control method of the embodiments of the present application; An input / output interface 303, which is used to implement information input and output; A communication interface 304, which is used to implement communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or through wireless means (such as mobile network, WIFI, Bluetooth, etc.); A bus 305, which transmits information between the various components of the device (such as the processor 301, the memory 302, the input / output interface 303, and the communication interface 304); Among them, the processor 301, the memory 302, the input / output interface 303, and the communication interface 304 achieve communication connections with each other inside the device through the bus 305.

[0093] The embodiments of the present application also provide a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the above-mentioned robot control method is implemented.

[0094] As a non-transitory computer-readable storage medium, the memory can be used to store non-transitory software programs and non-transitory computer-executable programs. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories can be connected to the processor through a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0095] The robot control method, device, electronic device, and storage medium provided by the embodiments of the present application obtain the formation information of multiple leader robots, the first dynamic models of multiple leader robots, and the second dynamic model of the follower robot, and obtain the first state observation value of each leader robot. Subsequently, for each leader robot, according to the formation information, the first dynamic model, and the first state observation value of the leader robot, the leader robot is controlled to move to the target position indicated by the formation information. After each leader robot moves to the target position, the second state observation value of the follower robot is obtained. Furthermore, for each follower robot, according to the second state observation value of the follower robot and the second dynamic model, the follower robot is controlled to move into the minimum convex polygon formed by the multiple leader robots. Through the above steps, the state of the target (virtual leader or follower robot) can be estimated inside each robot in the robot system, and then the formation-inclusion control of the robot system can be completed based on the estimated state observation value. In this way, when applied to scenarios with high dynamic and high real-time requirements, the formation accuracy of the multi-robot formation control process can be improved.

[0096] The embodiments described in the embodiments of the present application are for more clearly illustrating the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art will know that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of the present application are equally applicable to similar technical problems.

[0097] Those skilled in the art can understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than those shown in the figures, or combine certain steps, or different steps.

[0098] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0099] Those of ordinary skill in the art can understand that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or a suitable combination thereof.

[0100] As used in the specification of this application and the above-mentioned drawings, the terms "first", "second", "third", "fourth", etc. (if any) are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances so that the embodiments of this application described herein can be implemented in an order different from those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that comprises a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0101] It should be understood that in this application, "at least one (item)" means one or more, and "a plurality" means two or more. "And / or" is used to describe the association relationship of associated objects and indicates that there can be three relationships. For example, "A and / or B" can mean: only A exists, only B exists, and both A and B exist at the same time. Here, A and B can be singular or plural. The character " / " generally means that the associated objects before and after are in an "or" relationship. "At least one (one) of the following" or its similar expression means any combination of these items, including any combination of single items (ones) or plural items (ones). For example, at least one (one) of a, b, or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0102] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the above-mentioned unit division is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces, and the indirect coupling or communication connection of devices or units can be in an electrical, mechanical, or other form.

[0103] The units described above as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed over multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0104] In addition, each functional unit in various embodiments of the present application may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0105] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in various embodiments of the present application. The foregoing storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs and other various media that can store programs.

[0106] The preferred embodiments of the embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of rights of the embodiments of the present application. Any modifications, equivalent replacements, and improvements made by those skilled in the art without departing from the scope and essence of the embodiments of the present application shall be within the scope of rights of the embodiments of the present application.

Claims

1. A robot control method, characterized in that: The method is used to control a robot system, wherein the robot system includes a plurality of leader robots and at least one follower robot, wherein the follower robot follows the movement of the plurality of leader robots, and the method includes: Acquire formation information of a plurality of leader robots, pre-constructed first dynamic models of a plurality of the leader robots, and pre-constructed second dynamic models of follower robots; the formation information is used to indicate a target position of each of the leader robots; Acquire a first state observation value of each leader robot, wherein the first state observation value is obtained by estimating the motion state of the virtual leader based on a third dynamic model of a pre-built virtual leader and a preset adjacency matrix, wherein the virtual leader is used to guide the motion of the plurality of leader robots, and the preset adjacency matrix is ​​used to describe the communication connection relationship between the robots included in the robot system; For each of the leader robots, controlling the leader robot to move to the target position according to the formation information, the first dynamics model, and the first state observation value of the leader robot; Acquire a second state observation value of each of the follower robots, where the second state observation value is obtained by estimating the motion state of the follower robot based on the first state observation value and the formation information; For each of the follower robots, the follower robot is controlled to move into a minimum convex polygon formed by a plurality of the leader robots according to the second state observation value of the follower robot and the second dynamics model.

2. The method according to claim 1, characterized in that The first state observation value is obtained according to the following process: According to the third dynamic model and the preset adjacency matrix, the state matrix of the virtual leader is estimated to obtain a first state matrix observation value of the leader robot; The motion state of the virtual leader is estimated according to the third dynamics model, the first state matrix observation value and the preset adjacency matrix to obtain the first state observation value.

3. The method according to claim 1, characterized in that The step of controlling each leader robot to move to the target position according to the formation information, the first dynamic model, and the first state observation value of the leader robot comprises: For each of the leader robots, the trajectory deviation corresponding to the leader robot is determined according to the motion state of the leader robot, the formation information, the first state observation value and the first relative motion matrix; the first relative motion matrix is ​​used to represent the relative motion relationship between the target of the leader robot and the virtual leader, the trajectory deviation is the deviation between the motion state of the leader robot and the target trajectory of the leader robot, and the target trajectory of the leader robot is determined based on the first state observation value and the formation information; Determine a first target input amount of the leader robot according to a first gain matrix of the leader robot, a trajectory deviation corresponding to the leader robot, an input amount of the leader robot, and an input time lag of the leader robot; Controlling the leader robot to move to the target position according to the first target input amount of the leader robot; Wherein, the input amount of the leader robot and the input time lag of the leader robot are determined according to the first dynamics model of the leader robot.

4. The method according to claim 3, characterized in that: Determining a first target input amount of the leader robot according to a first gain matrix of the leader robot, a trajectory deviation corresponding to the leader robot, an input amount of the leader robot, and an input time lag of the leader robot comprises: Determining a trajectory deviation after a first preset time period according to an input time lag of the leader robot and a trajectory deviation corresponding to the leader robot, wherein the first preset time period is determined based on the input time lag of the leader robot; According to the first gain matrix and the trajectory deviation after the first preset time length, a time lag correction is performed on the input of the leader robot to obtain a first target input of the leader robot.

5. The method according to claim 1, characterized in that The second state observation value is obtained according to the following process: estimating the state matrix of the virtual leader to obtain a second state matrix observation value of the follower robot; Based on the second state matrix observation value, the target trajectory of each of the leader robots, the preset adjacency matrix and the preset communication matrix, the motion state of the follower robot is estimated to obtain the second state observation value of the follower robot; Among them, the target trajectory of each leader robot is determined based on the first state observation value and the formation information, and the preset communication matrix is ​​used to describe the communication state between the robots included in the robot system.

6. The method according to claim 5, characterized in that For each of the follower robots, according to the second state observation value of the follower robot and the second dynamic model, controlling the follower robot to move into the minimum convex polygon formed by the plurality of leader robots comprises: For each of the follower robots, determining a first error of the follower robot according to the second state matrix observation value and the target state of the follower robot, wherein the target state of the follower robot is determined based on the motion states of the plurality of leader robots; Determining a second error of the follower robot according to a state matrix of the follower robot, an input time lag of the follower robot, an input amount of the follower robot, an input matrix of the follower robot, a second state observation value, a second relative motion matrix of the follower robot, the first error, and a second state matrix observation value, wherein the second relative motion matrix is ​​used to represent a relative motion relationship between targets of the follower robot and a plurality of the leader robots; determining a second target input amount of the follower robot according to a second gain matrix of the follower robot, an input amount of the follower robot, an input time lag of the follower robot, and the second error; According to the second target input, controlling the follower robot to move into a minimum convex polygon formed by the plurality of leader robots; Among them, the state matrix of the follower robot, the input time lag of the follower robot, the input amount of the follower robot and the input matrix of the follower robot are all determined according to the second dynamic model.

7. The method according to claim 6, characterized in that Determining a second target input amount of the follower robot according to a second gain matrix of the follower robot, an input amount of the follower robot, an input time lag of the follower robot, and the second error comprises: determining a second error after a second preset time period according to the input time lag of the follower robot and the second error, wherein the second preset time period is determined based on the input time lag of the follower robot; According to the second gain matrix and the second error after the second preset time length, a time lag correction is performed on the input amount of the follower robot to obtain a second target input amount of the follower robot.

8. A robot control device, characterized in that: The device is used to control a robot system, wherein the robot system includes a plurality of leader robots and at least one follower robot, wherein the follower robot follows the movement of the plurality of leader robots, and the device includes: A first acquisition module is used to acquire formation information of multiple leader robots, pre-built first dynamic models of multiple leader robots, and pre-built second dynamic models of follower robots; the formation information is used to indicate the target position of each leader robot; a second acquisition module, for acquiring a first state observation value of each leader robot, wherein the first state observation value is obtained by estimating the motion state of the virtual leader based on a third dynamic model of a pre-built virtual leader and a preset adjacency matrix, wherein the virtual leader is used to guide the motion of the plurality of leader robots, and the preset adjacency matrix is ​​used to describe the communication connection relationship between the robots included in the robot system; A first control module, configured to control each of the leader robots to move to the target position according to the formation information, the first dynamics model, and a first state observation value of the leader robot; a third acquisition module, configured to acquire a second state observation value of each of the follower robots, wherein the second state observation value is obtained by estimating a motion state of the follower robot based on the first state observation value and the formation information; The second control module is used to control each follower robot to move into the minimum convex polygon formed by multiple leader robots according to the second state observation value of the follower robot and the second dynamic model.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the robot control method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the robot control method according to any one of claims 1 to 7 is implemented.

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