Robot control method, device, electronic device and storage medium

Abstract

Embodiments of the present application provide a robot control method, device, electronic device, and storage medium, relating to the field of robotics. The method includes: obtaining formation information of multiple leader robots, first dynamic models of multiple leader robots, and second dynamic models of follower robots; obtaining a first state observation value of each leader robot; for each leader robot, controlling the leader robot to move to a target position based on the formation information, the first dynamic model, and the first state observation value of the leader robot; obtaining a second state observation value of each follower robot; for each follower robot, controlling the follower robot to move within the minimum convex polygon formed by the multiple leader robots based on the follower robot's second state observation value and the second dynamic model. Embodiments of the present application can improve the formation accuracy of the multi-robot formation control process.

Description

Technical Field

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

[0002] To further enhance the functionality of robots, formation control technology can be used to enable multiple robots to coordinate their movements when performing complex tasks. Through precise position synchronization and time coordination, multiple robots can be formed into a formation to jointly overcome environmental challenges, improving the efficiency and safety of task execution. Multiple robots typically consist of a leader robot and follower robots. By controlling the leader robot in formation and then directing the follower robots to enter the convex hull area (i.e., the smallest convex polygon) formed by the leader robot, the follower robots can effectively move within the convex hull area formed by the leader robot, achieving formation maintenance and effective response of each robot to the leader robot. This enhances the flexibility of the formation and improves 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 dynamics and high real-time requirements. Summary of the Invention

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

[0005] To achieve the above objectives, a first aspect of an embodiment of the present application provides a robot control method for controlling a robot system, wherein the robot system includes multiple leader robots and at least one follower robot, wherein the follower robot follows the multiple leader robots in movement, and the method includes:

[0006] Acquire formation information of a plurality of leader robots, pre-built first dynamic models of a plurality of the leader robots, and pre-built second dynamic models of follower robots; the formation information is used to indicate a target position of each of the leader robots;

[0007] Obtaining a first state observation value of each leader robot, the first state observation value being obtained by estimating the motion state of the virtual leader by the leader robot based on a pre-built third dynamic model of the 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;

[0008] 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;

[0009] Obtaining a second state observation value of each follower robot, 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;

[0010] For each of the follower robots, the follower robot is controlled to move into a minimum convex polygon formed by multiple leader robots based on the second state observation value of the follower robot and the second dynamic model.

[0011] To achieve the above objectives, a second aspect of an embodiment of the present application provides a robot control device, wherein the device is used to control a robot system, wherein the robot system includes multiple leader robots and at least one follower robot, wherein the follower robot follows the multiple leader robots in movement, and the device includes:

[0012] a first acquisition module, configured to acquire formation information of a plurality of leader robots, pre-built first dynamic models of a plurality of the leader robots, and pre-built second dynamic models of follower robots; the formation information is used to indicate a target position of each of the leader robots;

[0013] a second acquisition module, configured to acquire a first state observation value of each leader robot, the first state observation value being obtained by estimating the motion state of the virtual leader by the leader robot based on a pre-built third dynamic model of the virtual leader and a preset adjacency matrix, the virtual leader being used to guide the motion of the plurality of leader robots, and the preset adjacency matrix being used to describe the communication connection relationship between the robots included in the robot system;

[0014] a first control module, configured to control each leader robot to move to the target position according to the formation information, the first dynamic model, and a first state observation value of the leader robot;

[0015] a third acquisition module, configured to acquire a second state observation value of each follower robot, 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;

[0016] 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.

[0017] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, the memory stores a computer program, and the processor implements the robot control method described in the first aspect when executing the computer program.

[0018] To achieve the above-mentioned purpose, the fourth aspect of an embodiment of the present application proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the robot control method described in the first aspect above.

[0019] The robot control method, device, electronic device, and storage medium proposed in this application obtain formation information of multiple leader robots, first dynamic models of the multiple leader robots, and second dynamic models of the follower robots, and obtain a first state observation value for each leader robot. Subsequently, for each leader robot, based on the formation information, the first dynamic model, and the leader robot's first state observation value, the leader robot is controlled to move to a target position indicated by the formation information. After each leader robot reaches the target position, the second state observation value of the follower robot is obtained. Furthermore, for each follower robot, based on the follower robot's second state observation value and the second dynamic model, the follower robot is controlled to move within the minimum convex polygon formed by the multiple leader robots. Through these steps, each robot in the robot system can estimate the state of a target (virtual leader or follower robot) internally, and then perform formation-inclusion control of the robot system based on the estimated state observation value. This improves the formation accuracy of multi-robot formation control when applied in scenarios with high dynamics and real-time requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is a flow chart of the robot control method provided by an embodiment of the present application;

[0021] Figure 2 Schematic diagram of the structure of the robot control device provided in an embodiment of the present application;

[0022] Figure 3 This is a schematic diagram of the hardware structure of the electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0024] It should be noted that although the device schematics illustrate functional module divisions and the flowcharts illustrate logical sequences, in certain circumstances, the steps shown or described may be performed in a sequence that differs from the module divisions in the device or the sequence in the flowcharts. The terms "first," "second," and so on, in the specification, claims, and drawings, are used to distinguish similar items and are not necessarily used to describe a specific sequence or precedence.

[0025] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0026] To solve the problems of the prior art, the embodiments of the present application provide a robot control method, device, electronic device and storage medium, which aim to improve the formation accuracy of the multi-robot formation control process.

[0027] The robot control method, device, electronic device and storage medium provided in the embodiments of the present application are specifically illustrated through the following embodiments. First, the robot control method in the embodiments of the present application is described.

[0028] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, techniques, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to achieve optimal results.

[0029] Fundamental AI technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interaction systems, and mechatronics. AI software technologies primarily encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0030] The robot control method provided in the embodiment of the present application relates to the field of robot technology. The robot control method provided in the embodiment of the present application can be applied to a terminal, can be applied to a server side, or can be software running in 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 be configured as a cloud server that provides 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 distribution 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.

[0031] The present 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, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like. The present 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, and the like that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments in which tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0032] Figure 1 This is a flowchart of the robot control method provided by the embodiment of the present application. Please refer to Figure 1 The robot control method provided in the embodiment of the present application can be applied to electronic devices. The robot control method is used to control a robot system, wherein the robot system includes multiple leader robots and at least one follower robot, and the follower robot follows the movement of the multiple leader robots. Figure 1 The method may include but is not limited to steps 101 to 105.

[0033] Step 101: Acquire formation information of a plurality of leader robots, pre-built first dynamic models of a plurality of the leader robots, and pre-built second dynamic models of follower robots; the formation information is used to indicate a target position of each of the leader robots;

[0034] Step 102: 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 pre-built third dynamic model of the virtual leader and a preset adjacency matrix. 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.

[0035] Step 103: 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;

[0036] Step 104: Obtain a second state observation value of each follower robot, 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.

[0037] Step 105 : For each follower robot, according to the second state observation value of the follower robot and the second dynamic model, control the follower robot to move into the minimum convex polygon formed by the plurality of leader robots.

[0038] The multiple robots included in the robot system can be quadruped robots, wheeled robots, or tracked robots, without limitation. During the control process, the formation information of multiple leader robots, the pre-built first dynamic models of multiple leader robots, and the pre-built second dynamic models of follower robots can be obtained. Specifically, it is assumed that the robot system can include n follower robots and m leader robots. Wherein, 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 of 1 to n, and the number of the leader robot can be any integer among n+1, n+2,..., n+m. The formation information is used to indicate the target position of each leader robot, and the formation information can include the formation offset of each leader robot relative to the virtual leader, that is, ,in, For the current moment The formation information at that time, Represents the current time The formation offset of each leader robot relative to the virtual leader, , for Any integer in this interval.

[0039] The first dynamics model is used to describe the relationship between the leader robot's input and its motion state, as well as the relationship between the leader robot's motion state and its output. The first dynamics model of each leader robot satisfies the following equation (1):

[0040] (1);

[0041] in, For the current moment, For the current moment The first The motion state of the leader robot, , Indicates the dimension A real column vector of ; for The moment The input of the leader robot, , Indicates the dimension A real column vector of , For the Input lag of the leader robot; For the current moment The first The output of the leader robot, , Indicates the dimension A real column vector of ; For the The state matrix of the leader robot reflects the The evolution relationship of the motion state (such as position, speed, and attitude angle) of each leader robot, , Indicates the number of rows , the number of columns is A real matrix of ; For the The input matrix of the leader robot reflects the path of the input quantity's effect on the motion state. , Indicates the number of rows , the number of columns is A real matrix of ; For the The output matrix of the leader robot, , Indicates the number of rows , the number of columns is A real matrix of .

[0042] The second dynamics 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 dynamics model of each follower robot satisfies the following formula (2):

[0043] (2);

[0044] in, For the current moment The first The motion state of the follower robot, for Any integer in , , Indicates the dimension A real column vector of ; for The moment The input of the follower robot, , Indicates the dimension A real column vector of , For the Input lag of each follower robot; It is the current moment The first The output of the follower robot, , Indicates the dimension A real column vector of ; For the The state matrix of the follower robot reflects the The evolution relationship of the motion state (such as position, velocity, and attitude angle) of each follower robot, , Indicates the number of rows , the number of columns is A real matrix of ; For the The input matrix of the follower robot reflects the path of the input's effect on the motion state. , Indicates the number of rows , the number of columns is A real matrix of ; For the The output matrix of the follower robot, , Indicates the number of rows , the number of columns is A real matrix of .

[0045] The virtual leader is used to guide the movement of multiple leader robots, which is not a physical robot. According to the motion state of the virtual leader and the formation information of 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 the first state observation value of 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 the preset adjacency matrix, thereby obtaining the first state observation value, that is, the estimated value of the virtual leader motion state 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 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):

[0046] (3);

[0047] in, Indicates the current time The movement state of the virtual leader at that time, , Indicates the dimension A real column vector of ; Indicates the current time The output of the virtual leader when ; The state matrix representing the virtual leader is used to describe the motion state of the virtual leader How the matrix changes over time, , Indicates the number of rows , the number of columns is A real matrix of ; Represents the output mapping matrix of the virtual leader, which is used to describe how to get from the motion state of the virtual leader The output is mapped The matrix, , Indicates the number of rows , the number of columns is A real matrix of .

[0048] and, . Status Matrix of Virtual Leaders Satisfies the following formula (4):

[0049] (4);

[0050] in, For the The first relative motion matrix of the leader robot is used to represent the The relative motion relationship between the leader robot and the virtual leader, Indicates the The control compensation term of the leader robot is used to describe the The amount of dynamic compensation applied by the leader robot to correct the effects of the disturbance.

[0051] So, for the 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 can be used to control the leader robot to move to the target position. Referring to the above steps, the position of each leader robot can be controlled, so that multiple leader robots form a formation indicated by the formation information.

[0052] Subsequently, the electronic device can obtain the second state observation value of each follower robot for subsequent control of the follower robot. 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 formation information. A follower robot can The second state observation value of the follower robot and the The second dynamic model of the follower robot controls the follower robot to move to the minimum convex polygon formed by the formation indicated by the formation information of multiple leader robots, thereby completing the formation-inclusive control process of the robot system, enabling the multiple robots included in the robot system to perform tasks together and improve the efficiency of task execution.

[0053] In steps 101 to 105, as illustrated in the embodiment of the present application, formation information of multiple leader robots, first dynamic models of multiple leader robots, and second dynamic models of follower robots are obtained, and a first state observation value of each leader robot is obtained. Subsequently, for each leader robot, the leader robot is controlled to move to a target position indicated by the formation information based on the formation information, the first dynamic model, and the first state observation value of the leader robot. After each leader robot moves to the target position, a second state observation value of the follower robot is obtained. Furthermore, for each follower robot, the follower robot is controlled to move within the minimum convex polygon formed by the multiple leader robots based on the follower robot's second state observation value and the second dynamic model. Through these steps, the state of the target (virtual leader or follower robot) can be estimated internally by each robot in the robot system, and formation-inclusion control of the robot system can be performed based on the estimated state observation value. This improves the formation accuracy of the multi-robot formation control process when applied in scenarios with high dynamics and high real-time requirements.

[0054] In some embodiments, the first state observation value is obtained according to the following process:

[0055] estimating a state matrix of the virtual leader according to the third dynamic model and the preset adjacency matrix to obtain a first state matrix observation value of the leader robot;

[0056] The motion state of the virtual leader is estimated according to the third dynamic model, the first state matrix observation value and the preset adjacency matrix to obtain the first state observation value.

[0057] To enable multiple leader robots to effectively track the desired motion trajectory, 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 a first state observation value. Specifically, based on the third dynamic model and the preset adjacency matrix, the state matrix of the virtual leader is estimated 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. The state matrix of the virtual leader can include the motion state of the virtual leader at multiple moments. The first state matrix observation value of the leader robot satisfies Equation (5):

[0058] (5);

[0059] in, For the The first state matrix observation value of the leader robot, is the first preset control parameter, The current moment in the preset adjacency matrix Time The adjacency matrix of the leader robots and the virtual leader, represents the state matrix of the virtual leader, The current moment in the preset adjacency matrix Time The leader robot and The adjacency matrix of the leader robots, For the The first state matrix observations of the leader robot.

[0060] According to the third dynamic model, the first state matrix observation value and the preset adjacency matrix, the motion state of the virtual leader is estimated to obtain the first state observation value. The first state observation value of the leader robot satisfies formula (6):

[0061] (6);

[0062] in, For the The first state observation value of the leader robot, is the second preset control parameter, The movement status of the virtual leader, For the The first state observation of the leader robot.

[0063] Through the above steps, each leader robot can observe and obtain 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 a formation indicated by the formation information.

[0064] In some embodiments, for each leader robot, 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:

[0065] For each leader robot, determining a trajectory deviation corresponding to the leader robot based on the leader robot's motion state, 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 between the leader robot and the virtual leader, the trajectory deviation is the deviation between the leader robot's motion state and the leader robot's target trajectory, and the leader robot's target trajectory is determined based on the first state observation value and the formation information;

[0066] 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;

[0067] controlling the leader robot to move to the target position according to a first target input amount of the leader robot;

[0068] The input amount of the leader robot and the input time lag of the leader robot are determined according to a first dynamic model of the leader robot.

[0069] For each leader robot, the leader robot can be controlled to move to the target position based on the formation information, the first dynamic model, and the first state observation of the leader robot. Specifically, in order to form the desired time-varying formation under any bounded initial conditions, that is, the formation indicated by the formation information, Equation (7) must be satisfied:

[0070] (7);

[0071] Then according to formula (4), we can get: .

[0072] like , then .

[0073] Based on this, , For the current moment Time The trajectory deviation corresponding to the leader robot.

[0074] However, since each robot cannot obtain the motion state of the leader robot in real time, the motion state of the leader robot can be estimated, that is, the trajectory deviation corresponding to the leader robot can be estimated. Specifically, the trajectory deviation corresponding to the leader robot can be estimated based on the motion state of the leader robot, formation information, first state observation value and first relative motion matrix. Among them, the trajectory deviation is the deviation between the motion state of the leader robot and the target trajectory of the leader robot. The target trajectory of the leader robot is determined based on the first state observation value and formation information. At the current moment The target trajectory of the leader robot In the actual control process, the The first state observation of the leader robot Approximately regarded as the movement state of a virtual leader Therefore, the estimated value of the trajectory deviation satisfies formula (8):

[0075] (8);

[0076] in, For the current moment Time The estimated value of the trajectory deviation of the leader robot.

[0077] Subsequently, a first target input for the leader robot can be determined based on the leader robot's first gain matrix, the estimated trajectory deviation of the leader robot (which can be roughly considered the leader robot's trajectory deviation), the leader robot's input, and the leader robot's input lag. The leader robot can then be controlled to move to the target position based on the first target input. Furthermore, because the first target input compensates for the input lag, the formation formed by multiple leader robots is more stable, improving the formation accuracy and response speed in complex environments.

[0078] In some embodiments, determining the first target input of the leader robot based on the first gain matrix of the leader robot, the trajectory deviation corresponding to the leader robot, the input of the leader robot, and the input lag of the leader robot includes:

[0079] determining a trajectory deviation after a first preset time period based on 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;

[0080] According to the first gain matrix and the trajectory deviation after the first preset time period, a time lag correction is performed on the input of the leader robot to obtain a first target input of the leader robot.

[0081] According to the first gain matrix of the leader robot, the trajectory deviation corresponding to the leader robot, the input amount of the leader robot and the input time lag of the leader robot, the first target input amount of the leader robot can be determined. After derivation, we can get formula (9):

[0082] (9);

[0083] in, , For the The first input after the leader robot is corrected, that is, The moment The input of each leader robot is the input after deducting the virtual leader motion state compensation term and the formation information compensation term. Then, through the partial differential transformation of formula (10), we can get and .

[0084] The following is formula (10):

[0085] (10);

[0086] in, is a spatiotemporal variable, which is the original time axis Redefinition of.

[0087] Thus, we can get formula (11):

[0088] (11);

[0089] in, is a natural constant.

[0090] After transforming formula (9), we can get By using the constant conversion equation, the future motion state can be predicted, so Equation (12) can be obtained:

[0091] (12);

[0092] At this point, it can be designed .in, is the first gain matrix, is the integral variable, which is the intermediate variable that replaces time in the integral term.

[0093] because is the Hurwitz matrix, then we can further deduce formula (13):

[0094] (13);

[0095] Therefore, we can get formula (14):

[0096] (14);

[0097] So, It can be deduced that ,because is the Hurwitz matrix, then In this way, multiple leader robots can form a formation indicated by the formation information under any bounded initial conditions.

[0098] Based on the above derivation process, we can know that for the current moment Time After taking the derivative of the estimated value of the trajectory deviation of the leader robot, the following formula (15) can be obtained:

[0099] (15);

[0100] in, , For the The second input after the leader robot is corrected, that is, The moment The input of the leader robot after deducting the first state observation value compensation term and the formation information compensation term. And, for the current moment Time The leader robot constructs the first intermediate variable, namely:

[0101] (16);

[0102] in, For the current moment Time The first intermediate variable of the leader robot construction.

[0103] At the same time, you can define , is the first deviation value, i.e. The deviation between the first state matrix observation of the leader robot and the state matrix of the virtual leader. And the definition , is the second deviation value, i.e. The deviation between the first state observation value of each leader robot and the motion state of the virtual leader. It can be understood that in order to ensure the control accuracy, , , so we can get Then, after deducing formula (15), we can get formula (17):

[0104] (17);

[0105] Then, for the current moment The first The first state observation of the leader robot Perform partial differential transformation and Performing partial differential transformation, we can get formula (18):

[0106] (18);

[0107] Then, we can calculate .in, .

[0108] For the current moment Time The second intermediate variable constructed by the leader robot. , we can get .

[0109] Based on this, according to the processing method of formula (13), formula (19) can be designed:

[0110] (19);

[0111] Therefore, the first target input of the leader robot should satisfy formula (20):

[0112] (20);

[0113] That is, electronic equipment Always on the The leader robot sends a The input of the leader robot, after the input delay Afterwards, act on A leader robot. For the The first target input of the leader robot, For the current moment At that time, Input lag of the leader robot The actual control input applied to the leader robot after compensation.

[0114] In this way, the first target input amount of the leader robot can be determined, so that the leader robot can be controlled to move to the target position according to the first target input amount.

[0115] In some embodiments, the second state observation value is obtained according to the following process:

[0116] estimating a state matrix of the virtual leader to obtain a second state matrix observation value of the follower robot;

[0117] estimating the motion state 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 to obtain the second state observation value of the follower robot;

[0118] 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.

[0119] Based on the first state observation value and 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 estimating the state matrix of the virtual leader, and is not the estimated value of the follower robot's own state matrix. The second state matrix observation value of the follower robot satisfies Equation (21):

[0120] (twenty one);

[0121] in, For the The second state matrix observation value of the follower robot, is the third preset control parameter, is the first in the preset adjacency matrix The follower robot and The adjacency matrix of the follower robots, If it is equal to 1, it means that the two robots are communicating, otherwise is equal to 0, For the The second state matrix observation value of the follower robot, It is the first Follower robots and The communication status of the leader robot.

[0122] 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):

[0123] (twenty two);

[0124] in, For the The second state observation value of the follower robot, is the fourth preset control parameter, For the The second state observation value of the follower robot, For the Through the above steps, the follower robots can obtain the second state observation value after estimating the motion state of the follower robots. 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 robots based on the second state observation value.

[0125] In some embodiments, controlling each follower robot to move to a minimum convex polygon formed by the plurality of leader robots based on the second state observation value of the follower robot and the second dynamic model includes:

[0126] 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;

[0127] 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 the plurality of leader robots;

[0128] 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;

[0129] According to the second target input, controlling the follower robot to move into a minimum convex polygon formed by the plurality of leader robots;

[0130] 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.

[0131] For each follower robot, based on 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 robot, , .

[0132] Since multiple leader robots have formed a formation indicated by the formation information, Equation (7) is satisfied. Then, we can let ,in, For the The target output of the leader robot. Then, we can get , is the set of target outputs of the leader robot, , .

[0133] Furthermore, it is assumed 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 of the follower robot is solvable). It is stable, Observable (indicating that the virtual leader edge is stable and bounded signals such as sine, cosine, and their combinations can be generated).

[0134] Assumptions:

[0135] , used to ensure that the second target input 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.

[0136] Then, the relative output information of the robot system can be defined to satisfy Equation (23):

[0137] (twenty three);

[0138] in, Represents the output matrix between multiple robots included in the robot system, It is The output of the follower robot, For the The output of the follower robot, For the The output of a leader robot.

[0139] At this time, according to the above formula, we can get formula (24):

[0140] (twenty four);

[0141] in, is a matrix including the output matrices between multiple robots, , is the first Kronecker product matrix, , represents the Kronecker product, is a matrix containing the output of each follower robot, .

[0142] 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 Equation (25):

[0143] (25);

[0144] in, is the third error.

[0145] In order for the follower robot to enter the minimum convex polygon formed by multiple leader robots, Equation (25) can be transformed into Equation (26):

[0146] (26);

[0147] in, is the second Kronecker product matrix, .

[0148] exist , the follower robot can enter the minimum convex polygon formed by multiple leader robots.

[0149] And, since the current moment The target trajectory of the leader robot , then there exists formula (27):

[0150] (27);

[0151] Therefore, we can design the output regulation equation with a unique solution as shown in Equation (28):

[0152] (28);

[0153] in, is the second relative motion matrix, used to represent the The relative motion relationship between the target of the follower robot and multiple leader robots, For the The control compensation term of the follower robot.

[0154] The first error of the follower robot can be determined based on the second state matrix observation value and the target state of the follower robot. 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):

[0155] (29);

[0156] in, 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, thereby gradually approaching the minimum convex polygon formed by multiple leader robots; The dimension is Matrix of Directed switching topology The The communication parameter matrix of the leader robot, , The preset communication matrix The communication status of the leader robot, is the Laplace matrix; Directed switching topology The The communication parameter matrix of the leader robot, , The preset communication matrix The communication status of the leader robot; represents the third Kronecker product matrix, , The dimension is 1 matrix. Directed switching topology graph It is used to describe the transition relationship between different states or nodes in the robot system. Assume that in the directed switching topology graph In the directed switching topology, there is a directed path between each leader robot and each follower robot. in and are all positive definite and non-singular virtual leader state matrices, and Existing and non-singular.

[0157] Then, define the first error ,in, is the set of the second state observation values of the follower robot , then, after taking the derivative of the first error, we can get formula (30):

[0158] (30);

[0159] in, , is the third deviation value, i.e. The deviation between the second state matrix observation of the follower robot and the state matrix of the virtual leader, , is a matrix containing the second state matrix observations of each follower robot, , is a matrix including the third deviation value corresponding to each follower robot, Indicates the extraction of diagonal elements in the matrix. Then we have formula (31):

[0160] (31);

[0161] at this time, is a positive definite matrix, The dimension is The matrix of According to formula (31), we can get Approaching 0, then the appropriate fourth preset control parameter can be determined To ensure Stablize.

[0162] According to the state matrix of the follower robot, the input time lag of the follower robot, the input amount 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 as ,in is the matrix including the second error of each follower robot, , is the matrix containing the motion state of each follower robot, , For the The second relative motion matrix of the follower robot.

[0163] At this time, Equation (26) can be rewritten as Equation (32):

[0164] (32);

[0165] Known, Approaching 0, if approaches 0, then Approaches 0. Therefore, the second target input should be Approaching 0.

[0166] make , To include The matrix of inputs of each follower robot at time , then we can get formula (33):

[0167] (33);

[0168] Then, we can get formula (34):

[0169] (34);

[0170] in, For the The first error of the follower robot.

[0171] 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.

[0172] Subsequently, the electronic device can determine a second target input for the follower robot based on the follower robot's second gain matrix, the follower robot's input, the follower robot's input lag, and the second error. Based on the second target input, each follower robot can be controlled to move within the minimum convex polygon formed by the multiple leader robots, thereby achieving containment control of the follower robots.

[0173] In some embodiments, determining the second target input amount of the follower robot based on the second gain matrix of the follower robot, the input amount of the follower robot, the input lag of the follower robot, and the second error includes:

[0174] 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;

[0175] According to the second gain matrix and the second error after the second preset time period, a time lag correction is performed on the input of the follower robot to obtain a second target input of the follower robot.

[0176] 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, the second target input amount of the follower robot can be determined. Specifically, in order for the follower robot to enter the minimum convex polygon formed by multiple leader robots, it is necessary to satisfy ,in, is the second gain matrix. By adjusting the second gain matrix , which can make is the Hurwitz matrix, and Approaching 0, approaches 0, so Approaching 0.

[0177] According to the input time lag and the second error of the follower robot, the second error after the second preset time length is determined. That is, according to the input time lag and the second error of the follower robot, it can be obtained ,in, For the The second target input of the follower robot, For Moment The second error of the follower robot.

[0178] The partial differential transformation is as follows (35):

[0179] (35);

[0180] Therefore, it can be determined After partial differential transformation, we can get .

[0181] Furthermore, we can get the following formula (36):

[0182] (36);

[0183] Thus, according to the second gain matrix and the second error after the second preset time, the input of the follower robot is subjected to time lag correction to obtain the second target input of the follower robot. The first The second target input of each follower robot satisfies formula (37):

[0184] (37);

[0185] In this way, the second target input amount of the follower robot can be determined, which facilitates the subsequent control of the follower robot so that the follower robot enters the minimum convex polygon formed by multiple leader robots.

[0186] The following is an example of the robot control method provided in the embodiments of the present application.

[0187] Assume that the robot system includes four follower robots (i.e., a first robot, a second robot, a third robot, and a fourth robot) and three leader robots (i.e., a fifth robot, a sixth robot, and a seventh robot), and there is a virtual leader to guide the movements of the three leader robots.

[0188] At this time, the switching signal of the robot system is set to satisfy formula (38):

[0189] (38);

[0190] in, For the current moment The switching signal when is an integer greater than or equal to 0, is the switching period of the switching topology network.

[0191] The input delay of the four follower robots ( 、 、 as well as ) is 0.2 seconds, and the input delay of the three leader robots ( 、 as well as ) for 0.25 seconds, the state matrix of the first robot (i.e. ) and the state matrix of the third robot (i.e. )for: , the input matrix of the first robot (i.e. ) and the input matrix of the third robot (i.e. )for: , the output matrix of the first robot (i.e. ) and the output matrix of the third robot (i.e. )for: The state matrix of the second robot (i.e. ) and the state matrix of the fourth robot (i.e. )for: , the input matrix of the second robot (i.e. ) and the input matrix of the fourth robot (i.e. )for: , the output matrix of the second robot (i.e. ) and the output matrix of the fourth robot (i.e. )for: The state matrix of the fifth robot (i.e. ), the state matrix of the sixth robot (i.e. ) and the state matrix of the seventh robot (i.e. ) are: , 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: The state matrix of the virtual leader (i.e. )for: , the output matrix of the virtual leader (i.e. )for: . And, set the first preset control parameter , the second preset control parameter , the first gain matrix (including 、 or )for: The third preset control parameter , the fourth preset control parameter , the second gain matrix (including 、 、 or )for: .

[0192] Therefore, we can see that the state signal generated by the virtual leader is a sinusoidal signal, which can be successfully tracked by the three leader robots. Moreover, the three leader robots form a triangle formation surrounded by four follower robots, which can achieve the control goal of formation-inclusive control.

[0193] The robot control method provided in the embodiments of the present application accurately addresses and overcomes the control challenges caused by time-delay heterogeneity by designing an observer within each robot and using the observer to observe the motion state. This ensures accurate information transmission and processing even in complex environments with varying input time delays. Furthermore, by converting the robot system into a first-order system, the design and implementation of the control strategy are simplified, making formation control more stable and efficient. Simultaneously, 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 the convex hull (i.e., the minimum convex polygon) of the formation formed by the leader robot. Furthermore, a feedback mechanism is designed to adjust the control strategy based on the real-time conditions of the robot system to adapt to environmental changes and system errors.

[0194] Figure 2 This is a schematic diagram of the structure of the robot control device provided in the embodiment of the present application. Figure 2The present application also provides a robot control device 200 that can implement the above-mentioned robot control method. The device 200 is used to control a robot system, wherein the robot system includes multiple leader robots and at least one follower robot, wherein the follower robot follows the movement of the multiple leader robots. The device includes:

[0195] A first acquisition module 201 is configured to acquire formation information of a plurality of leader robots, pre-built first dynamic models of a plurality of the leader robots, and pre-built second dynamic models of follower robots; the formation information is used to indicate a target position of each of the leader robots;

[0196] a second acquisition module 202 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 by the leader robot based on a pre-built third dynamic model of the 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;

[0197] A first control module 203 is configured to control 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;

[0198] a third acquisition module 204, configured to acquire a second state observation value of each follower robot, 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;

[0199] The second control module 205 is configured to control each follower robot to move into a minimum convex polygon formed by the plurality of leader robots according to the second state observation value of the follower robot and the second dynamic model.

[0200] In some embodiments, the first state observation value is obtained according to the following process:

[0201] estimating a state matrix of the virtual leader according to the third dynamic model and the preset adjacency matrix to obtain a first state matrix observation value of the leader robot;

[0202] The motion state of the virtual leader is estimated according to the third dynamic model, the first state matrix observation value and the preset adjacency matrix to obtain the first state observation value.

[0203] In some embodiments, the first control module 203 includes:

[0204] a first determination submodule for determining, for each leader robot, a trajectory deviation corresponding to the leader robot based on the leader robot's motion state, 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 between the leader robot and the virtual leader, the trajectory deviation is the deviation between the leader robot's motion state and the leader robot's target trajectory, and the leader robot's target trajectory is determined based on the first state observation value and the formation information;

[0205] a second determining submodule, configured to determine a first target input amount of the leader robot according to the first gain matrix of the leader robot, the trajectory deviation corresponding to the leader robot, the input amount of the leader robot, and the input time lag of the leader robot;

[0206] a first control submodule, configured to control the leader robot to move to the target position according to a first target input of the leader robot;

[0207] The input amount of the leader robot and the input time lag of the leader robot are determined according to a first dynamic model of the leader robot.

[0208] In some embodiments, the second determining submodule includes:

[0209] a first determining unit, configured to determine 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;

[0210] The first correction unit is used to perform time-lag correction on the input of the leader robot according to the first gain matrix and the trajectory deviation after the first preset time period to obtain a first target input of the leader robot.

[0211] In some embodiments, the second state observation value is obtained according to the following process:

[0212] estimating a state matrix of the virtual leader to obtain a second state matrix observation value of the follower robot;

[0213] estimating the motion state 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 to obtain the second state observation value of the follower robot;

[0214] 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.

[0215] In some embodiments, the second control module 205 includes:

[0216] a third determination submodule, configured to determine, for each follower robot, 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;

[0217] a fourth determination submodule, configured to determine a second error of the follower robot based on 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 the plurality of leader robots;

[0218] a fifth determining submodule, configured to determine 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;

[0219] a second control submodule, configured to control the follower robot to move into a minimum convex polygon formed by the plurality of leader robots according to the second target input;

[0220] 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.

[0221] In some embodiments, the fifth determining submodule includes:

[0222] a second determining unit, configured to determine a second error after a second preset time period according to an 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;

[0223] The second correction unit is used to perform time-lag correction on the input of the follower robot according to the second gain matrix and the second error after the second preset time length to obtain a second target input of the follower robot.

[0224] The specific implementation of the robot control device 200 can be found in the specific embodiment of the robot control method mentioned above, and will not be repeated here.

[0225] The present application also provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-mentioned robot control method. The electronic device can be any smart terminal including a tablet computer, an in-vehicle computer, or the like.

[0226] Figure 3 This is a hardware structure diagram of the electronic device provided in the embodiment of the present application. Figure 3 . Electronic equipment includes:

[0227] The processor 301 may be implemented as a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is configured to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

[0228] The memory 302 can be implemented in the form of 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 the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 302 and is called by the processor 301 to execute the robot control method of the embodiments of this application.

[0229] Input / output interface 303, used to implement information input and output;

[0230] Communication interface 304, used to implement communication interaction between this device and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WiFi, Bluetooth, etc.);

[0231] bus 305 , which transmits information between the various components of the device (e.g., processor 301 , memory 302 , input / output interface 303 , and communication interface 304 );

[0232] The processor 301 , the memory 302 , the input / output interface 303 and the communication interface 304 are connected to each other in communication within the device via the bus 305 .

[0233] An embodiment of the present application further provides 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.

[0234] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0235] The robot control method, apparatus, electronic device, and storage medium provided in embodiments of the present application obtain formation information of multiple leader robots, first dynamic models of the multiple leader robots, and second dynamic models of the follower robots, and obtain a first state observation value for each leader robot. Subsequently, for each leader robot, based on 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 a 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, based on the follower robot's second state observation value and the second dynamic model, the follower robot is controlled to move within a minimum convex polygon formed by the multiple leader robots. Through these steps, each robot in the robot system can estimate the state of a target (virtual leader or follower robot) internally, and then perform formation-inclusion control of the robot system based on the estimated state observation value. This improves the formation accuracy of multi-robot formation control when applied in scenarios requiring high dynamics and real-time performance.

[0236] The embodiments described in the embodiments of this application are intended to more clearly illustrate the technical solutions of the embodiments of this application and do not constitute a limitation on the technical solutions provided by the embodiments of this application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0237] Those skilled in the art will 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 shown in the figures, or a combination of certain steps, or different steps.

[0238] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0239] Those skilled in the art will appreciate that all or some of the steps in the methods, systems, and functional modules / units in the devices disclosed above may be implemented as software, firmware, hardware, or appropriate combinations thereof.

[0240] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0241] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least 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, c can be single or multiple.

[0242] 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 schematic. For example, the division of the above-mentioned units is only a logical function division. In actual implementation, there may be other division methods, such as 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0243] The units described above as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0244] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0245] 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, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes multiple instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: various media that can store programs, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0246] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but are not intended to limit the scope of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and essence of the present invention should be within the scope of the present invention.

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 moves following the plurality of leader robots, and the method includes: Acquire formation information of a plurality of leader robots, pre-built first dynamic models of a plurality of the leader robots, and pre-built second dynamic models of follower robots; the formation information is used to indicate a target position of each of the leader robots; Obtaining a first state observation value of each leader robot, the first state observation value being obtained by estimating the motion state of the virtual leader by the leader robot based on a pre-built third dynamic model of the 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 dynamic model, and the first state observation value of the leader robot; Obtaining a second state observation value of each follower robot, 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 multiple leader robots based on the second state observation value of the follower robot and the second dynamic model.

2. The method according to claim 1, characterized in that The first state observation value is obtained according to the following process: estimating a state matrix of the virtual leader according to the third dynamic model and the preset adjacency matrix 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 dynamic 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 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 leader robot, determining a trajectory deviation corresponding to the leader robot based on the leader robot's motion state, 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 between the leader robot and the virtual leader, the trajectory deviation is the deviation between the leader robot's motion state and the leader robot's target trajectory, and the leader robot's target trajectory is determined based on the first state observation value and the formation information; 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; controlling the leader robot to move to the target position according to a first target input amount of the leader robot; The input amount of the leader robot and the input time lag of the leader robot are determined according to a first dynamic model of the leader robot.

4. The method according to claim 3, characterized in that The determining of 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 includes: determining a trajectory deviation after a first preset time period based on 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 period, 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, wherein The second state observation value is obtained according to the following process: estimating a state matrix of the virtual leader to obtain a second state matrix observation value of the follower robot; estimating the motion state 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 to obtain the second state observation value of the follower robot; 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 The controlling, for each of the follower robots, to move to a minimum convex polygon formed by the plurality of leader robots based on the second state observation value of the follower robot and the second dynamic model 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 the plurality of 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; 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 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 includes: 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 period, a time lag correction is performed on the input of the follower robot to obtain a second target input 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 moves following the plurality of leader robots, and the device includes: a first acquisition module, configured to acquire formation information of a plurality of leader robots, pre-built first dynamic models of a plurality of the leader robots, and pre-built second dynamic models of follower robots; the formation information is used to indicate a target position of each of the leader robots; a second acquisition module, configured to acquire a first state observation value of each leader robot, the first state observation value being obtained by estimating the motion state of the virtual leader by the leader robot based on a pre-built third dynamic model of the virtual leader and a preset adjacency matrix, the virtual leader being used to guide the motion of the plurality of leader robots, and the preset adjacency matrix being used to describe the communication connection relationship between the robots included in the robot system; a first control module, configured to control each leader robot to move to the target position according to the formation information, the first dynamic 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 follower robot, 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; 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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