Robot formation control method and device, robot, and storage medium

Abstract

The application provides a robot formation control method and device, a robot and a storage medium. The method comprises the following steps: for any target robot in a robot formation, receiving first motion state information sent by a first neighbor robot at a (k+1)th moment, and determining an abnormal value of the first neighbor robot when it is determined that the first neighbor robot is abnormal according to the first motion state information; and finally, compensating the motion of the first neighbor robot according to the abnormal value of the first neighbor robot. That is, each robot in the robot formation can analyze whether the neighbor robot is abnormal according to the motion state information sent by the neighbor robot, and when it is determined that the neighbor robot is abnormal, the abnormal motion of the neighbor robot can be compensated in time according to the abnormal value, which can effectively reduce the influence of the abnormal robot on the robot formation, so that the robot formation can still accurately achieve the expected formation when the robot formation is abnormal, and the working reliability of the robot formation is improved.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a robot formation control method, apparatus, robot, and storage medium. Background Technology

[0002] Robot swarms can be understood as a multi-robot system that controls multiple robots through cooperative control methods, enabling tasks that cannot be performed by individual robots.

[0003] In practical applications, robots in a robot formation may be attacked from the outside or malfunction due to their own faults, causing abnormalities that can affect the normal operation of the robot formation. To address this technical problem, a detection-based control method has been proposed. Specifically, this method detects abnormal robots in the robot formation and removes them from the robot queue.

[0004] However, current detection-based control methods cannot effectively reduce the impact of abnormal robots on robot formations. Summary of the Invention

[0005] This application provides a robot formation control method, device, robot, and storage medium, which can effectively reduce the impact of abnormal robots on robot formation, so that even when there are abnormalities in the robot formation, the expected formation can still be accurately achieved, thereby improving the working reliability of the robot formation.

[0006] Firstly, this application provides a robot formation control method, including:

[0007] For any target robot in the robot formation, receive first motion state information sent by a first neighbor robot at time k+1, where the first neighbor robot is a neighbor robot of the target robot in the robot formation, and k is a positive integer;

[0008] Based on the first motion state information, when it is determined that the first neighbor robot is abnormal, the abnormal value of the first neighbor robot is determined;

[0009] Based on the abnormal values ​​of the first neighbor robot, motion compensation is performed on the first neighbor robot.

[0010] Secondly, a robot formation control device is provided, comprising:

[0011] The receiving unit is configured to receive, for any target robot in the robot formation, first motion state information sent by a first neighboring robot at time k+1, wherein the first neighboring robot is a neighboring robot of the target robot in the robot formation, and k is a positive integer;

[0012] The determining unit is used to determine the abnormal value of the first neighbor robot when the first neighbor robot is determined to be abnormal, based on the first motion state information.

[0013] The compensation unit is used to perform motion compensation on the first neighbor robot based on the abnormal values ​​of the first neighbor robot.

[0014] Thirdly, a robot is provided, including a processor and a memory. The memory is used to store a computer program, and the processor is used to invoke and run the computer program stored in the memory to perform the methods described in the first aspect or its implementations.

[0015] Fourthly, a chip is provided for implementing the methods of any one of the first to second aspects or their respective implementations. Specifically, the chip includes a processor for calling and running a computer program from a memory, causing a device on which the chip is installed to perform the methods of any one of the first aspects or their respective implementations.

[0016] Fifthly, a computer-readable storage medium is provided for storing a computer program that causes a computer to perform the methods of any of the first aspects or their implementations.

[0017] In a sixth aspect, a computer program product is provided, including computer program instructions that cause a computer to perform the methods in any of the first aspects or their implementations.

[0018] In a seventh aspect, a computer program is provided that, when run on a computer, causes the computer to perform the methods of any one of the first aspects or their implementations.

[0019] In summary, this application addresses any target robot in a robot formation by receiving first motion state information sent by a first neighbor robot at time k+1. Based on this first motion state information, when an anomaly is detected in the first neighbor robot, the application determines the anomaly value of the first neighbor robot and finally compensates for its motion based on this anomaly value. In other words, in this embodiment, each robot in the robot formation can communicate with its neighbor robots, sending its own motion state information to and receiving motion state information from neighbor robots. This allows each robot in the formation to analyze whether a neighbor robot is abnormal based on the motion state information sent by the neighbor robot. When an anomaly is detected, the application determines the anomaly value of that neighbor robot and compensates for its abnormal motion based on this value, thereby reducing the impact of abnormal robots on the robot formation. In other words, this application, through mutual anomaly detection and compensation among the robots in the formation, can offset or compensate for the impact of attacks or malfunctions without relying on global information or a global controller. This ensures that even when anomalies exist in the robot formation, the expected formation can still be accurately achieved, thus improving the overall control reliability of the robot formation. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of a robot formation according to an embodiment of this application;

[0022] Figure 2 for Figure 1 The topology diagram corresponding to the robot formation shown is shown below;

[0023] Figure 3 A flowchart illustrating a robot formation control method provided in an embodiment of this application;

[0024] Figure 4 A flowchart illustrating a robot formation control method provided in an embodiment of this application;

[0025] Figure 5 This is a schematic block diagram of a robot formation control device provided in an embodiment of this application;

[0026] Figure 6 This is a schematic block diagram of the robot provided in the embodiments of this application. Detailed Implementation

[0027] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0028] It should be understood that, in the embodiments of the present invention, "B corresponding to A" means that B is associated with A. In one implementation, B can be determined based on A. However, it should also be understood that determining B based on A does not mean determining B solely based on A; B can also be determined based on A and / or other information.

[0029] In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0030] Furthermore, to facilitate a clear description of the technical solutions in the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish identical or similar items with substantially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and the terms "first" and "second" are not necessarily different.

[0031] To facilitate understanding of the embodiments of this application, the relevant concepts involved in the embodiments of this application will be briefly introduced as follows:

[0032] Figure 1 This is a schematic diagram of a robot formation according to an embodiment of this application. The applications of robot formation mainly include formation generation, maintenance, transformation, path planning, and obstacle avoidance, all of which are based on graph theory.

[0033] Robot formations communicate with each other through a communication graph. The communication graph illustrates the information flow between the nodes. The goal of cooperative control is to design control protocols for each node to ensure that all nodes exhibit synchronized behavior in a specific sense. In a cooperative system, any control protocol must be distributed according to its defined graph topology; that is, each node's control protocol is only allowed to rely on information about that node and its neighbors in the graph. In multi-robot cooperative formations, the focus is on the behavior and interactions of dynamic systems interconnected through a communication network modeled as a graph with directed edges corresponding to the allowed information flows between systems. Each robot in the formation is modeled as a node in the graph. The fundamental question then becomes how the graph topology interacts with the nodes' local feedback control protocols to generate the overall behavior of the interconnected nodes.

[0034] Each robot in a robot formation is treated as a node in the topology graph. The topology graph corresponding to the robot formation can be represented as G = (V, E), where V = {x1, ..., xn} represents the set of N nodes, and E represents the set of edges. The element (xi, xj) in set E represents the edge from node xi to xj, which in the formation represents the flow of information from robot xi to robot xj. In one example... Figure 1 The diagram shown is a topology of the robot formation. Figure 2 As shown, the direction of the arrows indicates the direction of the information flow, for example... Figure 2 In the middle, robot 2 and robot 3 can communicate with each other.

[0035] In the topology graph corresponding to the robot formation, each node can be considered as an agent, and each agent follows the double integral dynamics shown in Equations (1) and (2):

[0036]

[0037]

[0038] Where, r i (t)∈R and v i (t)∈R represent the position and velocity of agent i at time t≥0, respectively, while u i (t)∈R represents the control input of agent i at time t.

[0039] In the embodiments of this application, the agent has continuous temporal dynamics, while the input is applied to discrete sampling times.

[0040] After discretization using the sampling period T, the double integral dynamics followed by each agent are shown in equations (3) and (4):

[0041]

[0042] v i (k+1)=v i (k)+Tu i (k), i=1,…,n (4)

[0043] Where, r i (k), v i (k) and u i (k) represents the absolute position, velocity, and control input of agent i at time t = kT, respectively. i (k+1) and v i (k+1) represent the absolute position and velocity of agent i at time t = (k+1)T, respectively.

[0044] Based on the topology diagram of the robot formation described above, this application embodiment realizes the control of the robot formation, so as to effectively reduce the impact of abnormal robots on the robot formation, so that even when there are abnormalities in the robot formation, the expected formation can still be accurately achieved, thereby improving the working reliability of the robot formation.

[0045] The technical solutions of the embodiments of this application will be described in detail below through some examples. The following embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0046] Figure 3 This is a flowchart illustrating a robot formation control method provided in an embodiment of this application, as shown below. Figure 3 As shown, the method in this application embodiment includes:

[0047] S301. For any target robot in the robot formation, receive the first motion state information sent by the first neighbor robot at time k+1.

[0048] Here, the first neighbor robot is a neighbor robot of the target robot in the robot formation, and k is a positive integer.

[0049] In some embodiments, the target robot is any one of the robots in the robot formation. That is, each robot in the robot formation executes the control method of the embodiments of this application, and the process of each robot executing the control method of the embodiments of this application is the same. Therefore, for ease of description, the following description uses one robot in the robot formation as an example. To distinguish it from other robots in the following description, this robot will be referred to as the target robot.

[0050] In some embodiments, the target robot is any normal robot in the robot formation. That is, in this embodiment, normal robots are used to compensate for the movement of abnormal robots to reduce the impact of abnormal robots on the robot formation. In one example, the target robot is any robot in the robot formation that does not exhibit abnormality at time k. For instance, the target robot sends motion status information to its neighboring robots at time k+1. The neighboring robots determine whether the target robot is abnormal or normal based on the motion status information sent by the target robot at time k+1 and send the determination result to the target robot. Thus, when the target robot determines that it is a normal robot based on the determination result, it executes the method of this embodiment.

[0051] In some embodiments, a robot in a robot formation includes a transceiver and a controller, wherein the transceiver is used to receive information streams sent by neighboring robots, such as motion state information sent by neighboring robots, and to send its own motion state information to neighboring robots. The controller is used to control the motion state of the robot according to the generated control information.

[0052] In some embodiments, the controller may further include a formation coordination controller and a compensation controller, wherein the formation coordination controller generates control input information to enable the robot to complete the expected motion state in the queue under the control of the control input information. The compensation controller controls the robot to compensate for the motion of abnormal neighboring robots, thereby reducing the impact of abnormal neighboring robots on the formation.

[0053] In this embodiment of the application, the neighboring robots of the target robot refer to robots in the robot formation that can transmit information streams to the target robot. For example Figure 2 In the topology shown, assuming the target robot is robot 3, the robots that can send information streams to robot 3 are robot 1, robot 2, robot 5, and robot 6. Therefore, robot 1, robot 2, robot 5, and robot 6 can be called the neighbor robots of the target robot.

[0054] In this embodiment, the target robot interacts with each neighboring robot in real time, receiving motion status information from each neighboring robot and determining whether each neighboring robot is abnormal based on this information. If a neighboring robot is determined to be abnormal, its abnormal value is identified, and its motion is compensated based on this value to reduce its impact on the robot formation. Therefore, in this embodiment, the target robot uses the same motion compensation measures for each neighboring robot. For ease of description, this example uses one neighboring robot from the target robot, for instance, the first neighboring robot.

[0055] The embodiments of this application do not limit the specific content included in the first motion state information.

[0056] In one example, the first motion state information includes the motion state information of the first neighboring robot at time k+1.

[0057] In one example, the first motion state information includes the motion state information of the first neighboring robot at time k.

[0058] In one example, the first motion state information includes the motion state information of N second neighbor robots of the first neighbor robot at time k. Specifically, the motion state information of the N second neighbor robots of the first neighbor robot at time k is sent to the first neighbor robot by the N second neighbor robots at time k.

[0059] In one example, the first motion state information includes the motion state information of the N second neighbor robots of the first neighbor robot at time k+1. Specifically, the motion state information of the N second neighbor robots of the first neighbor robot at time k+1 is sent to the first neighbor robot by the N second neighbor robots at time k+1.

[0060] In one example, the first motion state information includes the motion state information of the first neighbor robot at time k+1, and the motion state information of the N second neighbor robots of the first neighbor robot at time k.

[0061] In one example, the first motion state information includes the motion state information of the first neighbor robot at time k, and the motion state information of the N second neighbor robots of the first neighbor robot at time k.

[0062] In one example, the first motion state information includes the motion state information of the first neighbor robot at time k+1, and the motion state information of the N second neighbor robots of the first neighbor robot at time k+1.

[0063] Optionally, the first motion state information may also include other motion state-related information, which is not limited in this embodiment.

[0064] In some embodiments, if the first motion state information includes the motion state information of the first neighboring robot at time k+1, the embodiments of this application do not limit the specific content included in the motion state information of the first neighboring robot at time k+1. For example, it may include any information that can represent the motion state of the first neighboring robot at time k+1, such as the speed, position, acceleration, and control input information of the first neighboring robot at time k+1.

[0065] In one specific example, the motion state information of the first neighboring robot at time k+1 includes at least one of the first neighboring robot's velocity, position, and first control input information at time k+1. The first control input information can be understood as the control input information of the first neighboring robot at time k+1.

[0066] For example, if the motion state information of the first neighbor robot at time k+1 includes the speed, position, and first control input information of the first neighbor robot at time k+1, then the motion state information of the first neighbor robot at time k+1 can be represented as:

[0067]

[0068] Where i represents the identifier of the first neighboring robot in the robot formation, and Ψ1(k+1) represents the motion state information of the first neighboring robot at time k+1. v represents the position of the first robot in the robot formation at time k+1. i (k+1) represents the velocity of the first robot at time k+1. i (k+1) represents the first control input information of the first robot at time k+1.

[0069] In some embodiments, if the first motion state information includes the motion state information of N second neighbor robots of the first neighbor robot at time k, the embodiments of this application do not limit the specific content included in the motion state information of the N second neighbor robots at time k. For example, it may include any information that can represent the motion state of each second neighbor robot at time k, such as the speed, position, acceleration, and control input information of each of the N second neighbor robots at time k.

[0070] In one specific example, the motion state information of the N second neighbor robots at time k includes at least one of the velocity and position of each of the N second neighbor robots at time k.

[0071] For example, if the motion state information of N second neighbor robots at time k includes the velocity and position of each of the N second neighbor robots at time k, then the motion state information of the N second neighbor robots at time k can be represented as:

[0072]

[0073] Where j represents the identifier of the j-th neighbor robot of the first neighbor robot among the N neighbor robots, and Ψ2(k) represents the motion state information of the N second neighbor robots at time k. Let the velocity of the j-th neighboring robot of the first robot at time k be denoted as . Let represent the position of the j-th neighboring robot of the first robot in the robot formation at time k.

[0074] In some embodiments, if the first motion state information includes the motion state information of N second neighbor robots of the first neighbor robot at time k and the motion state information of the first neighbor robot at time k+1, and the motion state information of the first neighbor robot at time k+1 includes the speed, position, and first control input information of the first neighbor robot at time k+1, and the motion state information of the N second neighbor robots at time k includes the speed and position of each of the N second neighbor robots at time k, then the first motion state information can be expressed as:

[0075]

[0076] Among them, Ψ i (k+1) represents the first motion state information.

[0077] In some embodiments, the control input information of the first neighbor robot at time k+1 can be determined by the following formula (5):

[0078]

[0079] Among them, a ij Let represent the weight of the edge between the j-th second neighbor robot and the first neighbor robot in the topology graph. Specifically, if the j-th second neighbor robot and the first neighbor robot can communicate, then 'a' is determined. ij It equals a preset value, which is a positive number between 0 and 1. If the j-th second neighbor robot cannot communicate with the first neighbor robot, then a is determined. ij It equals 0.

[0080] Where α is a positive scalar. r i (k+1) represents the absolute position of the first neighboring robot at time k+1, Δ i This can be understood as the relative position required for the first neighboring robot in the robot formation. In the application embodiment, [the following will be used]. The position of the first neighbor robot in the robot formation at time k+1.

[0081] In some embodiments, the robots form a formation at the same speed. In this case, the goal of the above formula (5) is second-order consistency, that is, the robots in the robot formation satisfy the following formulas (6) and (7):

[0082]

[0083]

[0084] In this embodiment of the application, after the target robot in the robot formation receives the first motion state information sent by the first neighbor robot at time k+1, it executes the following step S302.

[0085] S302. Based on the first motion state information, when it is determined that the first neighboring robot is abnormal, determine the abnormal value of the first neighboring robot.

[0086] In this embodiment of the application, after the target robot receives the first motion state information sent by the first neighbor robot at time k+1, it determines whether the first neighbor robot is abnormal based on the first motion state information. If it is determined that the first neighbor robot is abnormal, then the abnormal value of the first neighbor robot is determined.

[0087] In some embodiments, the target robot can determine whether the first neighboring robot has experienced an anomaly at time k+1 based on the first motion state information.

[0088] For example, if the first motion state information includes the motion state information of the first neighboring robot at time k+1, the target robot can compare the motion state information of the first neighboring robot at time k+1 with the motion state information of the target robot at time k+1. For instance, it can compare whether the speed of the first neighboring robot at time k+1 is the same as the speed of the target robot at time k+1. If they are the same, it is determined that the first neighboring robot is normal at time k+1; if they are different, it is determined that the first neighboring robot is abnormal at time k+1.

[0089] In this embodiment, according to the above method, if it is determined that the first neighbor robot is abnormal at time k+1, then the abnormal value of the first neighbor robot at time k+1 is determined, and the movement of the first neighbor robot at time k+1 is compensated based on the abnormal value.

[0090] In some embodiments, S302 above includes the following steps:

[0091] S302-A: Based on the first motion state information, determine whether the first neighboring robot is abnormal at time k;

[0092] S302-B: When it is determined that the target robot is abnormal at time k, the abnormal value of the first neighboring robot at time k is determined.

[0093] In this embodiment, the target robot determines whether the first neighboring robot is abnormal at time k based on the first motion state information sent by the first neighboring robot at time k+1. If the target robot is determined to be abnormal at time k, the abnormal value of the first neighboring robot at time k is determined. That is, in this embodiment, the target robot compensates for the motion of the first neighboring robot at time k based on the first motion state information sent by the first neighboring robot at time k+1.

[0094] The following describes the specific process in S302-A above, which determines whether the first neighboring robot is abnormal at time k based on the first motion state information.

[0095] In this embodiment of the application, the methods for determining whether the first neighboring robot is abnormal at time k based on the first motion state information in S302-A above include at least the following:

[0096] Method 1: If the first motion state information sent by the first neighbor robot to the target robot at time k+1 includes the motion state information of N second neighbor robots of the first neighbor robot at time k, and the target robot is included among the N second neighbor robots, then the above S302-A includes the following steps S302-A-11 and S302-A-12:

[0097] S302-A-11. Obtain the motion state information of the target robot at time k from the motion state information of N second neighbor robots at time k;

[0098] S302-A-12. Compare the motion state information of the target robot at time k included in the first motion state information with the motion state information of the target robot at time k stored in the target robot, and determine whether the first neighbor robot is abnormal at time k.

[0099] In this first method, the first motion state information sent by the first neighboring robot to the target robot at time k+1 includes the motion state information of N second neighboring robots at time k, and the target robot is one of the N second neighboring robots. That is, the target robot is a neighboring robot of the first neighboring robot. The target robot sends its own motion state information at time k to the first neighboring robot at time k. At time k+1, the first neighboring robot sends the target robot's motion state information at time k as part of the motion state information of the N second neighboring robots at time k. Thus, the target robot can obtain its own motion state information at time k from the motion state information of the N second neighboring robots included in the first motion state information received at time k+1.

[0100] Next, the target robot compares the motion state information of the target robot at time k obtained from the first motion state information with its own saved motion state information of the target robot at time k to determine whether the first neighbor robot is abnormal at time k. For example, if the motion state information of the target robot at time k obtained from the first motion state information is exactly the same as the motion state information of the target robot at time k saved by the target robot, then the first neighbor robot is determined to be normal at time k. If the motion state information of the target robot at time k obtained from the first motion state information is not exactly the same as the motion state information of the target robot at time k saved by the target robot, then the first neighbor robot is determined to be abnormal at time k.

[0101] For example, suppose the motion state information of the target robot at time k included in the first motion state information is motion state information 1, and the motion state information of the target robot itself at time k is motion state information 2. This application embodiment does not limit the specific content included in motion state information 1 and motion state information 2. If the content included in motion state information 1 and motion state information 2 is not completely consistent, it indicates that the motion state information 1 stored by the first neighboring robot has been tampered with, indicating that the first neighboring robot can be attacked or has malfunctioned; therefore, the first neighboring robot is determined to be abnormal. If the content included in motion state information 1 and motion state information 2 is completely consistent, it indicates that the motion state information 1 stored by the first neighboring robot has not been tampered with, indicating that the first neighboring robot has not been attacked or has not malfunctioned; therefore, the first neighboring robot is determined to be normal.

[0102] In this embodiment, the specific content of the motion state information of the target robot at time k is not limited. For example, the motion state information of the target robot at time k may include at least one of the target robot's velocity, position, acceleration, and control input information at time k. Thus, the target robot can compare at least one of the target robot's velocity, position, acceleration, and control input information at time k included in the first motion state information with at least one of the target robot's velocity, position, acceleration, and control input information stored by the target robot itself at time k to determine whether the target robot is functioning normally at time k.

[0103] In some embodiments, if the motion state information of the target robot at time k includes the speed and position of the target robot at time k, then in S302-A-12 above, the motion state information of the target robot at time k included in the first motion state information is compared with the motion state information of the target robot at time k stored by the target robot to determine whether the first neighboring robot is abnormal at time k, including the following examples:

[0104] Example 1: If the speed and position of the target robot at time k included in the first motion state information are consistent with the speed and position of the target robot at time k stored by the target robot, it means that the first neighbor robot has not tampered with the speed and position information of the target robot at time k, and thus it is determined that the first neighbor robot is normal at time k.

[0105] Example 2: If the speed and position of the target robot at time k included in the first motion state information are inconsistent with at least one of the speed and position of the target robot at time k stored by the target robot, it indicates that the first neighbor robot has tampered with the speed and position information of the target robot at time k, and thus it is determined that the first neighbor robot is abnormal at time k.

[0106] For example, suppose the position and velocity of the target robot at time k, which are included in the first motion state information, are respectively... and The target robot itself, including its position and velocity at time k, is denoted as... and v j (k).

[0107] like and Then it is determined that the first neighbor robot is normal at time k.

[0108] like and / or If the first neighbor robot is abnormal at time k, then it is determined that the first neighbor robot is abnormal at time k.

[0109] In addition to using the first method mentioned above to determine whether the first neighbor robot is abnormal at time k, the target robot can also use the second method as follows to determine whether the first neighbor robot is abnormal at time k.

[0110] Method 2: If the target robot receives, in addition to receiving the first motion state information sent by the first neighbor robot at time k+1, also receives the second motion state information sent by the first neighbor robot at time k, then S302-A includes the following steps S302-A-21 and S302-A-22:

[0111] S302-A-21, Obtain the second motion state information sent by the first neighboring robot at time k;

[0112] S302-A-22. Based on the first motion state information and the second motion state information, determine whether the first neighboring robot is abnormal at time k.

[0113] In this second method, the target robot determines whether the first neighboring robot is abnormal at time k based on the second motion state information and the first motion state information sent by the first neighboring robot at time k. In other words, in this embodiment, the abnormality of the first neighboring robot is determined based on the motion state information sent by the first neighboring robot at two adjacent times.

[0114] In this embodiment of the application, there is no restriction on the specific method by which the first neighbor robot is determined to be abnormal at time k based on the first motion state information and the second motion state information in the above S302-A-22.

[0115] In some embodiments, the first motion state information and the second motion state information include speed and position, etc. Thus, the second motion state information sent at time k can be compared with the first motion state information sent at time k+1 to determine whether the first neighboring robot is abnormal at time k. For example, if the target robot determines that the relationship between the second motion state information sent by the first neighboring robot at time k and the first motion state information sent by the first neighboring robot at time k+1 meets preset requirements, such as the first and second motion state information including the same speed and the included position difference meeting a preset position difference, then the first neighboring robot is determined to be normal at time k. If the relationship between the second motion state information and the first motion state information does not meet the preset requirements, then the first neighboring robot is determined to be abnormal at time k.

[0116] This application does not impose any limitations on the above-mentioned preset requirements.

[0117] In one example, the preset formation of the robot formation is designed to maintain synchronized movement among the robots, meaning that all robots in the formation move at the same speed. Thus, the target robot can compare the second motion state information received at time k with the first motion state information received at time k+1. If the speeds included in the second and first motion state information match, the first neighboring robot is determined to be normal at time k. If the speeds included in the second and first motion state information do not match, the first neighboring robot is determined to be abnormal at time k.

[0118] In some embodiments, if the first motion state information includes the motion state information of N second neighbor robots of the first neighbor robot at time k, and the second motion state information includes the motion state information of the first neighbor robot at time k, then S302-A-22 above includes the following steps:

[0119] S302-A-221. Based on the motion state information of N second neighbor robots at time k and the motion state information of the first neighbor robot at time k, determine whether the first neighbor robot is abnormal at time k.

[0120] In this embodiment, if the first motion state information includes the motion state information of N second neighbor robots of the first neighbor robot at time k, and the second motion state information includes the motion state information of the first neighbor robot at time k, then the target robot obtains the motion state information of the N second neighbor robots at time k from the first motion state information, and obtains the motion state information of the first neighbor robot at time k from the second motion state information. Furthermore, based on the motion state information of the N second neighbor robots at time k and the motion state information of the first neighbor robot at time k, the target robot determines whether the first neighbor robot is abnormal at time k.

[0121] In this embodiment of the application, the specific method of determining whether the first neighbor robot is abnormal at time k based on the motion state information of N second neighbor robots at time k and the motion state information of the first neighbor robot at time k is not limited.

[0122] In one possible implementation, the motion state information of N second neighbor robots at time k is compared with the motion state information of the first neighbor robot at time k to determine whether the first neighbor robot is abnormal at time k. For example, the motion state information of the first neighbor robot at time k is compared with the motion state information of the N second neighbor robots at time k. If the motion state information of the first neighbor robot at time k and the motion state information of the N second neighbor robots at time k meet preset requirements, then the first neighbor robot is determined to be normal at time k. If the motion state information of the first neighbor robot at time k and the motion state information of the N second neighbor robots at time k do not meet preset requirements, then the first neighbor robot is determined to be abnormal at time k.

[0123] In another possible implementation, if the motion state information of the second neighbor robot at time k includes the velocity and position of the second neighbor robot at time k, and the motion state information of the first neighbor robot at time k includes the velocity, position, and second control input information of the first neighbor robot at time k, then the above S302-A-221 includes the following steps:

[0124] S302-A-221-1. Based on the speed and position of N second neighbor robots at time k, and the speed and position of the first neighbor robot at time k, determine the third control input information of the first neighbor robot at time k.

[0125] S302-A-221-1. If the third control input information is consistent with the second control input information, then the first neighbor robot is determined to be normal at time k.

[0126] S302-A-221-1. If the third control input information is inconsistent with the second control input information, then the first neighbor robot is determined to be abnormal at time k.

[0127] In this implementation, if the motion state information of the second neighbor robot at time k includes its speed and position at time k, and the motion state information of the first neighbor robot at time k includes its speed, position, and second control input information at time k, then firstly, based on the speeds and positions of the N second neighbor robots at time k, and the speed and position of the first neighbor robot at time k, the third control input information of the first neighbor robot at time k is determined. Next, the calculated third control input information is compared with the second control input information of the first neighbor robot at time k included in the second motion state information to determine whether the first neighbor robot follows a preset coding cooperation algorithm, thereby determining whether the first neighbor robot is abnormal at time k. Specifically, if the calculated third control input information is consistent with the second control input information included in the second motion state information, then the first neighbor robot is determined to be normal at time k. If the calculated third control input information is inconsistent with the second control input information included in the second motion state information, then the first neighbor robot is determined to be abnormal at time k.

[0128] This application embodiment does not limit the specific method of determining the third control input information of the first neighbor robot at time k based on the speed and position of N second neighbor robots at time k and the speed and position of the first neighbor robot at time k in S302-A-221-1.

[0129] In one possible implementation, if the speed and position of the first neighbor robot at time k are consistent with the speed and position of the N second neighbor robots at time k, then the third control input information of the first neighbor robot at time k is determined to be 0.

[0130] If the velocity and position of the first neighboring robot at time k are not completely consistent with the velocities and positions of the N second neighboring robots at time k, then the differences between the velocity of the first neighboring robot at time k and the velocities of the N second neighboring robots at time k, and the differences between the position of the first neighboring robot at time k and the velocity and position of the N second neighboring robots at time k, are determined. Next, based on the velocity and position differences, the third control input information for the first neighboring robot at time k is determined; for example, the sum of the velocity and position differences is used as the third control input information for the first neighboring robot at time k.

[0131] In another possible implementation, S302-A-221-1 above includes the following steps:

[0132] S302-A-221-11. For the j-th second neighbor robot among N second neighbor robots, determine the speed difference and position difference between the first neighbor robot and the j-th second neighbor robot at time k based on the speed and position of the first neighbor robot and the j-th second neighbor robot at time k respectively.

[0133] S302-A-221-12. Multiply the velocity difference by the first value and add it to the position difference to obtain the second sum.

[0134] S302-A-221-13. Multiply the second sum by the negative third value to obtain the control input information corresponding to the j-th second neighbor robot. If the j-th second neighbor robot can communicate with the first neighbor robot, the third value is a preset value greater than 0. If the j-th second neighbor robot cannot communicate with the first neighbor robot, the third value is 0.

[0135] S302-A-221-14. The sum of the control input information corresponding to each of the N second neighbor robots is determined as the third control input information.

[0136] In one example, the third control input information of the first neighbor robot at time k is determined according to the following formula (8):

[0137]

[0138] Among them, u i (k)3 represents the third control input information of the first neighbor robot at time k. This represents the position of the first neighboring robot at time k. Let v represent the position of the j-th second neighbor robot among N second neighbor robots at time k. i (k) represents the velocity of the first neighboring robot at time k, v j (k) represents the velocity of the j-th second neighbor robot among N second neighbor robots at time k, where α is a preset value, for example, it can be 1, a ij The third value represents the weight of the edge between the j-th second neighbor robot and the first neighbor robot in the topology graph. Specifically, if the j-th second neighbor robot and the first neighbor robot can communicate, the third value is set to a preset value, which is a positive number between 0 and 1. If the j-th second neighbor robot and the first neighbor robot cannot communicate, the third value is set to 0.

[0139] In this implementation, after determining the third control input information of the first neighbor robot at time k according to the above formula (8), the calculated third control input information is compared with the second control input information of the first neighbor robot at time k included in the second motion state information. Specifically, if the calculated third control input information is consistent with the second control input information included in the second motion state information, then the first neighbor robot is determined to be normal at time k. If the calculated third control input information is inconsistent with the second control input information included in the second motion state information, then the first neighbor robot is determined to be abnormal at time k.

[0140] In this embodiment of the application, when the target robot determines that the first neighbor robot is abnormal at time k according to the above method one or method two, it executes the above S302-B to determine the abnormal value of the first neighbor robot at time k.

[0141] The embodiments of this application do not limit the specific method for determining the outlier value of the first neighbor robot at time k.

[0142] In some embodiments, the default value is determined to be the abnormal value of the first neighboring robot at time k. That is, if it is determined that the first neighboring robot is abnormal at time k, then the abnormal value of the first neighboring robot at time k is determined to be the default value, and then the movement of the first neighboring robot at time k is compensated according to the default value.

[0143] In some embodiments, S302-B above includes the following step S302-B1:

[0144] S302-B1. Based on the first motion state information, determine the outlier value of the first neighboring robot at time k.

[0145] In this embodiment, the target robot can determine the abnormal value of the first neighbor robot at time k based on the first motion state information sent by the first neighbor robot at time k+1, thereby achieving accurate determination of the abnormal value of the first neighbor robot at time k.

[0146] This application embodiment does not limit the specific method of determining the abnormal value of the first neighbor robot at time k based on the first motion state information in S302-B1 above.

[0147] In one possible implementation, the target robot compares its first motion state information with its own motion state information at time k+1 to determine outliers of the first neighbor robot at time k. For example, if the first motion state information includes the velocity and position of the first neighbor robot at time k+1, then the velocity and position of the first neighbor robot at time k+1 are compared with the velocity and position of the target robot at time k+1 to determine outliers of the first neighbor robot at time k. For instance, the sum of the differences between the velocity and position distribution of the first neighbor robot at time k+1 and the velocity and position of the target robot at time k+1 is determined as the outlier of the first neighbor robot at time k.

[0148] In another possible implementation, S302-B1 above includes the following steps:

[0149] S302-B11: Obtain the second motion state information sent by the first neighboring robot at time k;

[0150] S302-B12. Based on the first motion state information and the second motion state information, determine the outlier value of the first neighbor robot at time k.

[0151] In this implementation, the target robot determines the outlier value of the first neighbor robot at time k based on the second motion state information sent by the first neighbor robot at time k and the first motion state information sent at time k+1.

[0152] This application embodiment does not limit the specific implementation method of determining the abnormal value of the first neighbor robot at time k based on the first motion state information and the second motion state information in the above S302-B12.

[0153] Example 1: If the first motion state information includes the velocity and position of the first neighboring robot at time k+1, and the second motion state information includes the velocity and position of the first neighboring robot at time k, then the target robot can predict the position of the first neighboring robot at time k+1 based on the robot's velocity at time k and the time difference between time k+1 and time k. Next, based on the difference between the robot's actual position and the predicted position at time k+1, and the difference between the robot's velocity at time k+1 and its velocity at time k, outliers of the first neighboring robot at time k are determined.

[0154] Example 2: The target robot determines the outlier of the first neighbor robot at time k according to the following steps S302-B121 to S302-B123:

[0155] S302-B121. Based on the first motion state information, determine the velocity deviation and position deviation of the first neighboring robot at time k.

[0156] In one possible implementation, the velocity and position of the first neighboring robot at time k+1 are obtained from the first motion state information, and the velocity, position, and control input information of the first neighboring robot at time k are obtained from the second motion state information. Then, the velocity and position of the first neighboring robot at time k+1 are calculated according to the above formulas (3) and (4), respectively. The difference between the calculated velocity of the first neighboring robot at time k+1 and the velocity of the first neighboring robot at time k+1 included in the first motion state information is determined as the velocity deviation of the first neighboring robot at time k. The difference between the calculated position of the first neighboring robot at time k+1 and the position of the first neighboring robot at time k+1 included in the first motion state information is determined as the position deviation of the first neighboring robot at time k.

[0157] In another possible implementation, if the first motion state information includes the velocities of N second neighbor robots at time k, and the target robot is among the N second neighbor robots, then the target robot obtains its velocity at time k from the first motion state information. Next, based on the difference between the target robot's stored velocity at time k and the velocity of the target robot included in the first motion state information at time k, the velocity deviation of the first neighbor robot at time k is determined. For example, the difference between the target robot's stored velocity at time k and the velocity of the target robot included in the first motion state information at time k is determined as the velocity deviation of the first neighbor robot at time k.

[0158] In another possible implementation, if the first motion state information includes the positions of N second neighbor robots at time k, and the target robot is among the N second neighbor robots, then the target robot obtains its position at time k from the first motion state information. Next, based on the difference between the target robot's saved position at time k and the position of the target robot included in the first motion state information at time k, the position deviation of the first neighbor robot at time k is determined. For example, the difference between the target robot's saved position at time k and the position of the target robot included in the first motion state information at time k is determined as the position deviation of the first neighbor robot at time k.

[0159] It should be noted that, in the embodiments of this application, the method of determining the velocity deviation and position deviation of the first neighbor robot at time k based on the first motion state information may include other methods besides those shown in the examples above, and the embodiments of this application do not limit this.

[0160] S302-B122. Based on the first motion state information and the second motion state information, determine the control input information deviation of the first neighbor robot at time k.

[0161] In one possible implementation, the first motion state information includes the control input information of the first neighboring robot at time k+1, and the second motion state information includes the control input information of the first neighboring robot at time k. If the robots in the robot formation move synchronously at time k and time k+1, that is, the speed and position of the first neighboring robot relative to the target robot remain unchanged at time k+1 and time k, then under normal circumstances, the control input information of the first neighboring robot at time k is consistent with the control input information at time k+1. Based on this, the target robot can determine the control input information deviation of the first neighboring robot at time k based on the difference between the first motion state information including the control input information of the first neighboring robot at time k+1 and the second motion state information including the control input information of the first neighboring robot at time k. For example, the difference between the first motion state information including the control input information of the first neighboring robot at time k+1 and the second motion state information including the control input information of the first neighboring robot at time k can be determined as the control input information deviation of the first neighboring robot at time k.

[0162] In another possible implementation, based on the speed and position of N second neighbor robots at time k, and the speed and position of the first neighbor robot at time k, the third control input information of the first neighbor robot at time k is determined. Based on the difference between the second control input information and the third control input information, the control input information deviation of the first neighbor robot at time k is determined.

[0163] In this implementation, the specific process of determining the third control input information of the first neighbor robot at time k based on the speed and position of the N second neighbor robots at time k and the speed and position of the first neighbor robot at time k can be referred to the relevant description in S302-A-221-1 above, and will not be repeated here.

[0164] The methods for determining the control input information deviation of the first neighbor robot at time k based on the difference between the second and third control input information include at least the following two:

[0165] Method 1: If the target neighbor robot compensates for the control input deviation of the first neighbor robot at time k, but the other neighbor robots of the first neighbor robot do not compensate for the control input deviation of the first neighbor robot at time k, then the difference between the second control input information and the third control input information is used to determine the control input information deviation of the first neighbor robot at time k.

[0166] Method 2: If all N second-neighbor robots of the first neighbor robot compensate for the control input deviation of the first neighbor robot at time k, then the compensation value for the target robot's control input deviation of the first neighbor robot at time k is one-Nth of the difference between the second and third control input information. In other words, the ratio of the difference between the second and third control input information to N is determined as the control input deviation of the first neighbor robot corresponding to the target robot at time k.

[0167] S302-B123. Determine the abnormal value of the first neighbor robot at time k based on at least one of the velocity deviation, position deviation, and control input information deviation of the first neighbor robot at time k.

[0168] Based on the steps S302-B121 and S302-B122 above, the velocity deviation, position deviation, and control input information deviation of the first neighbor robot at time k are determined, and then the abnormal value of the first neighbor robot at time k is determined based on at least one of the velocity deviation, position deviation, and control input information deviation of the first neighbor robot at time k.

[0169] In one example, the negative sum of the velocity deviation, position deviation, and control input information deviation of the first neighbor robot at time k is determined as the outlier value of the first neighbor robot at time k.

[0170] In another example, the first value is multiplied by the velocity deviation of the first neighboring robot at time k, and then added to the position deviation of the first neighboring robot at time k to obtain a first sum. The first sum is multiplied by a negative second value to obtain a first product. If the first neighboring robot and the target robot can communicate, the second value is a preset value greater than 0. If the first neighboring robot and the target robot cannot communicate, the second value is 0. The first product is added to the control input information deviation of the first neighboring robot at time k to obtain the abnormal value of the first neighboring robot at time k.

[0171] For example, the outlier of the first neighbor robot at time k is determined according to the following formula (9):

[0172]

[0173] in, This represents the outlier value of the first neighbor robot at time k. This represents the velocity deviation of the first neighboring robot at time k. This represents the position deviation of the first neighboring robot at time k. Let a represent the control input information deviation of the first neighbor robot at time k. ij α is the second value, and α is the first value.

[0174] After the target robot determines the outlier value of the first neighboring robot according to the above method, it executes the following S303.

[0175] S303. Perform motion compensation on the first neighbor robot based on the abnormal value of the first neighbor robot.

[0176] In this embodiment, each robot in the robot formation can communicate with its neighboring robots, sending its own motion state information to and receiving motion state information from neighboring robots. This allows each robot in the formation to analyze whether a neighboring robot is malfunctioning based on the motion state information sent by its neighbors. If a malfunction is detected, the malfunctioning value is determined, and the robot's motion is promptly compensated based on this value, thereby reducing the impact of malfunctioning robots on the robot formation. Furthermore, in this embodiment, the mutual anomaly detection and compensation among the robots in the formation improves the overall control reliability of the robot formation.

[0177] This application embodiment does not limit the specific method of performing motion compensation on the first neighbor robot based on the abnormal value of the first neighbor robot in the above-described S303.

[0178] In one possible implementation, the abnormal value of the first neighbor robot is determined as part of the control input information of the target robot at time k+1, so that the target robot compensates for the motion of the first neighbor robot based on the abnormal value of the first neighbor robot.

[0179] In another possible implementation, S303 above includes the following steps:

[0180] S303-A1, Determine the fourth control input information of the target robot at time k+1.

[0181] In one possible implementation, assume the target robot has M neighboring robots, and the target robot receives motion state information sent by each of these M neighboring robots at time k+1. If the motion state information sent by each of the M neighboring robots at time k+1 includes its velocity and position at time k+1, then the target robot can determine its fourth control input information at time k+1 based on its own velocity and position at time k+1, and the velocities and positions of each of the M neighboring robots at time k+1.

[0182] In one example, the fourth control input information of the target robot at time k+1 can be determined according to the following formula (10):

[0183]

[0184] Among them, u l4 (k+1) represents the fourth control input information of the target robot at time k+1. This represents the position of the target robot at time k+1. Let v represent the position of the m-th neighboring robot among M neighboring robots at time k+1. l (k+1) represents the velocity of the target robot at time k+1, v m (k+1) represents the velocity of the m-th neighbor robot among M neighbor robots at time k+1.

[0185] It should be noted that the above formula (10) is only an example. The target robot can also determine the fourth control input information of the target robot at time k+1 according to the modified formula (10) or other calculation methods. This application embodiment does not limit this.

[0186] S303-A2. Based on the abnormal value of the first neighboring robot and the fourth control input information, determine the target control input information of the target robot.

[0187] In this embodiment of the application, after the target robot determines the fourth control input information of the target robot at time k+1 according to the above steps, it determines the target control input information of the target robot according to the abnormal value of the first neighbor robot and the fourth control input information.

[0188] In one implementation, the sum of the outlier value of the first neighboring robot and the fourth control input information is determined as the target control input information of the target robot at time k+1.

[0189] In another implementation, to reduce the impact of motion compensation on robot formation, the target robot can compensate for the motion of its first neighbor robot at time k in multiple steps. That is, through multiple compensations, the target robot incorporates the outlier values ​​of the first neighbor robot into the fourth control input information, thus obtaining the target robot's target control input information.

[0190] In this method, the abnormal values ​​of the first neighbor robot are added to the fourth control input information through multiple compensations for the target neighbor robot. The specific method of obtaining the target control input information of the target robot is not limited.

[0191] Example 1: Divide the outlier values ​​of the first neighboring robot into multiple equal parts, and add each equal part to the fourth control input information in turn to obtain the target control input information of the target robot.

[0192] Example 2: Based on the preset compensation period Tc and the abnormal value of the first neighbor robot, determine the compensation value of each compensation in the first Tc-1 compensations and the compensation value of the Tcth compensation, where Tc is a positive integer greater than 1; based on the compensation value of each compensation in the first Tc-1 compensations and the compensation value of the Tcth compensation, compensate the abnormal value of the first neighbor robot into the fourth control input information in Tc times to obtain the target control input information of the target robot.

[0193] In one possible implementation of Example 2, within the compensation period Tc, the outlier values ​​of the first neighbor robot are divided into multiple equal parts, for example, into Tc equal parts. The time period from time k+1 to time k+Tc is also divided into Tc equal parts. Thus, starting from time k+1, outlier values ​​of the same equal parts are added to the fourth control input information at regular intervals to obtain the target control input information of the target robot.

[0194] In one possible implementation of Example 2, within the compensation period Tc, the abnormal value of the first neighboring robot is compensated Tc times and added to the fourth control input information, wherein the compensation value of each compensation in the first Tc-1 compensations is the same, and the compensation value of the Tc-th compensation is different from the compensation value of the first Tc-1 compensations.

[0195] For example, the negative of the ratio of 2 outliers to Tc-1 is determined as the compensation value for each of the first Tc-1 compensations. The outlier of the first neighboring robot is determined as the compensation value for the target robot in the Tc-th compensation.

[0196] For example, the compensation value for each compensation in the first Tc-1 compensations is determined according to the following formula (11):

[0197]

[0198] in, This represents the compensation value of the target robot to the first neighbor robot at time k+l, or the compensation value of each of the previous Tc-1 compensations, used to compensate for the input error of the first neighbor robot at time k.

[0199] For example, the compensation value for the Tcth compensation is determined according to the following formula (12):

[0200]

[0201] in, Indicates the target robot at k+T c The compensation value for the first neighbor robot at time k, or the compensation value at the Tcth compensation.

[0202] Based on the above steps, the target robot adds the outlier value of the first neighbor robot at time k to the fourth control input information of the target robot at time k+1, thus obtaining the target robot's target control input information. Next, steps S303-A3 are executed.

[0203] S303-A3: Move according to the target control input information to compensate for the motion of the first neighbor robot at time k.

[0204] The target robot moves under the control of the aforementioned determined target control input information. Since the target control input information is determined based on the abnormal value of the first neighbor robot at time k, when the target robot moves according to the target control input information, it can compensate for the abnormal movement of the first neighbor robot at time k, thereby reducing the impact of the first neighbor robot on the entire robot formation and improving the working reliability of the robot formation.

[0205] The robot formation control method provided in this application, for any target robot in the robot formation, receives first motion state information sent by a first neighbor robot at time k+1. Based on the first motion state information, when an anomaly is determined in the first neighbor robot, the method determines the anomaly value of the first neighbor robot. Finally, based on the anomaly value of the first neighbor robot, motion compensation is performed on the first neighbor robot. That is, in this application embodiment, each robot in the robot formation can communicate with its neighbor robots, sending its own motion state information to the neighbor robots and receiving motion state information from them. In this way, each robot in the robot formation can analyze whether a neighbor robot is abnormal based on the motion state information sent by the neighbor robot. When an anomaly is determined, the anomaly value of the neighbor robot is determined, and the abnormal motion of the neighbor robot is compensated in a timely manner based on the anomaly value, thereby reducing the impact of abnormal robots on the robot formation. In other words, this application, through mutual anomaly detection and compensation among the robots in the robot formation, can offset or compensate for the impact of attacks or malfunctions without relying on global information and a global controller. This allows the robot formation to accurately achieve the expected formation even when anomalies exist, thereby improving the control reliability of the entire robot formation.

[0206] Based on the above embodiments, the robot formation control method provided in this application will be further described below with reference to a specific embodiment.

[0207] Figure 4 This is a flowchart illustrating a robot formation control method provided in an embodiment of this application, as shown below. Figure 4 As shown, the method in this application embodiment includes:

[0208] S401. For any target robot in the robot formation, receive the first motion state information sent by the first neighbor robot at time k+1.

[0209] Here, the first neighbor robot is a neighbor robot of the target robot in the robot formation, and k is a positive integer.

[0210] In some embodiments, the target robot is any one of the robots in the robot formation.

[0211] In some embodiments, the target robot is a normal robot in the robot formation that does not exhibit any abnormalities.

[0212] The specific implementation process of S401 can be referred to the specific description of S301 above, and will not be repeated here.

[0213] S402. Based on the first motion state information, determine whether the first neighboring robot is abnormal.

[0214] If the first neighbor robot is determined to be abnormal, then the following steps S403 are executed.

[0215] If the first neighbor robot is determined to be functioning normally, then proceed with step S407 as follows.

[0216] The specific implementation process of S402 can be referred to the specific description of S302-A above, and will not be repeated here.

[0217] S403. Based on the first motion state information, determine the abnormal value of the first neighboring robot.

[0218] The specific implementation process of S403 can be referred to the specific description of S302-B1 above, and will not be repeated here.

[0219] S404. Determine whether the abnormal value of the first neighbor robot is less than or equal to a preset threshold.

[0220] If the abnormal value of the first neighbor robot is less than or equal to the preset threshold, then execute S405 as follows to compensate for the movement of the first neighbor robot.

[0221] If the abnormal value of the first neighbor robot is greater than the preset threshold, then execute S506 as follows to remove the first neighbor robot from the robot formation.

[0222] In other words, in this embodiment of the application, if the outlier value of the first neighbor robot is small, the first neighbor robot is compensated; if the outlier value of the first neighbor robot is large, the first neighbor robot is directly removed from the robot formation.

[0223] S405. Based on the abnormal values ​​of the first neighbor robot, perform motion compensation on the first neighbor robot.

[0224] The specific implementation process of S405 can be referred to the specific description of S303 above, and will not be repeated here.

[0225] S406. Remove the first neighbor robot from the robot formation.

[0226] S407. No compensation will be provided to the first neighbor robot.

[0227] In this embodiment, when an anomaly is determined in the first neighboring robot, its anomaly value is determined. If the anomaly value is greater than a preset threshold, the first neighboring robot is not compensated, but is directly removed from the robot formation. If the anomaly value of the first neighboring robot is less than or equal to the preset threshold, motion compensation is performed on the first neighboring robot based on the anomaly value, thereby improving the control reliability of the robot formation.

[0228] It should be understood that Figures 3 to 4 This is merely an example of what is being done and should not be construed as limiting the scope of this application.

[0229] The preferred embodiments of this application have been described in detail above with reference to the accompanying drawings. However, this application is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this application, various simple modifications can be made to the technical solutions of this application, and these simple modifications all fall within the protection scope of this application. For example, the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, this application will not describe the various possible combinations separately. Furthermore, various different embodiments of this application can also be arbitrarily combined, as long as they do not violate the spirit of this application, they should also be considered as the content disclosed in this application.

[0230] The above text combined Figures 3 to 4 The method embodiments of this application are described in detail below, in conjunction with... Figures 5 to 6 The following describes in detail the device embodiments of this application.

[0231] Figure 5 This is a schematic block diagram of a robot formation control device according to an embodiment of this application. The device 10 can be a target robot or a part of a target robot.

[0232] like Figure 5 As shown, the robot formation control device 10 may include:

[0233] The receiving unit 11 is configured to receive, for any target robot in the robot formation, first motion state information sent by a first neighbor robot at time k+1, wherein the first neighbor robot is a neighbor robot of the target robot in the robot formation, and k is a positive integer;

[0234] The determining unit 12 is used to determine the abnormal value of the first neighbor robot when the first neighbor robot is determined to be abnormal, based on the first motion state information.

[0235] The compensation unit 13 is used to perform motion compensation on the first neighbor robot based on the abnormal value of the first neighbor robot.

[0236] In some embodiments, the first motion state information includes at least one of the motion state information of N second neighbor robots of the first neighbor robot at time k and the motion state information of the first neighbor robot at time k+1, where N is a positive integer.

[0237] In some embodiments, the motion state information of the first neighboring robot at time k+1 includes at least one of the first neighboring robot's velocity, position, and first control input information at time k+1; or...

[0238] The motion state information of the N second neighbor robots at time k includes at least one of the velocity and position of each of the N second neighbor robots at time k.

[0239] In some embodiments, the determining unit 12 is specifically configured to determine whether the first neighboring robot is abnormal at time k based on the first motion state information; and when it is determined that the target robot is abnormal at time k, to determine the abnormal value of the first neighboring robot at time k.

[0240] In some embodiments, if the first motion state information includes the motion state information of N second neighbor robots of the first neighbor robot at time k, and the target robot is included among the N second neighbor robots, then the determining unit 12 is specifically used to obtain the motion state information of the target robot at time k from the motion state information of the N second neighbor robots at time k; compare the motion state information of the target robot at time k included in the first motion state information with the motion state information of the target robot at time k stored by the target robot, and determine whether the first neighbor robot is abnormal at time k.

[0241] In some embodiments, if the motion state information of the target robot at time k includes the speed and position of the target robot at time k, then the determining unit 12 is specifically configured to determine that the first neighbor robot is normal at time k if the speed and position of the target robot included in the first motion state information at time k are consistent with the speed and position of the target robot saved by the target robot at time k; and to determine that the first neighbor robot is abnormal at time k if the speed and position of the target robot included in the first motion state information at time k are inconsistent with at least one of the speed and position of the target robot saved by the target robot at time k.

[0242] In some embodiments, the determining unit 12 is specifically configured to acquire the second motion state information sent by the first neighbor robot at time k; and determine whether the first neighbor robot is abnormal at time k based on the first motion state information and the second motion state information.

[0243] In some embodiments, if the first motion state information includes the motion state information of the N second neighbor robots at time k, and the second motion state information includes the motion state information of the first neighbor robot at time k, then the determining unit 12 is specifically used to determine whether the first neighbor robot is abnormal at time k based on the motion state information of the N second neighbor robots at time k and the motion state information of the first neighbor robot at time k.

[0244] In some embodiments, if the motion state information of the second neighbor robot at time k includes the speed and position of the second neighbor robot at time k, and the motion state information of the first neighbor robot at time k includes the speed, position, and second control input information of the first neighbor robot at time k, then the determining unit 12 is specifically configured to determine the third control input information of the first neighbor robot at time k based on the speed and position of the N second neighbor robots at time k and the speed and position of the first neighbor robot at time k; if the third control input information is consistent with the second control input information, then the first neighbor robot is determined to be normal at time k; if the third control input information is inconsistent with the second control input information, then the first neighbor robot is determined to be abnormal at time k.

[0245] In some embodiments, the determining unit 12 is specifically configured to determine the abnormal value of the first neighbor robot at time k based on the first motion state information.

[0246] In some embodiments, the determining unit 12 is specifically used to obtain the second motion state information sent by the first neighbor robot at time k; and to determine the abnormal value of the first neighbor robot at time k based on the first motion state information and the second motion state information.

[0247] In some embodiments, the determining unit 12 is specifically configured to: determine the velocity deviation and position deviation of the first neighbor robot at time k based on the first motion state information; determine the control input information deviation of the first neighbor robot at time k based on the first motion state information and the second motion state information; and determine the abnormal value of the first neighbor robot at time k based on at least one of the velocity deviation, position deviation and control input information deviation of the first neighbor robot at time k.

[0248] In some embodiments, if the first motion state information includes the speeds of the N second neighbor robots at time k, and the target robot is included among the N second neighbor robots, then the determining unit 12 is specifically configured to obtain the speed of the target robot at time k from the first motion state information; and determine the speed deviation of the first neighbor robot at time k based on the difference between the speed of the target robot at time k stored by the target robot and the speed of the target robot at time k included in the first motion state information.

[0249] In some embodiments, if the first motion state information includes the positions of the N second neighbor robots at time k, and the target robot is included among the N second neighbor robots, then the determining unit 12 is specifically used to obtain the position of the target robot at time k from the first motion state information; and determine the position deviation of the first neighbor robot at time k based on the difference between the position of the target robot at time k saved by the target robot and the position of the target robot at time k included in the first motion state information.

[0250] In some embodiments, if the second motion state information includes the speed, position, and second control input information of the first neighbor robot at time k, and the first motion state information includes the speed and position of the N second neighbor robots at time k, then the determining unit 12 is specifically configured to determine the third control input information of the first neighbor robot at time k based on the speed and position of the N second neighbor robots at time k and the speed and position of the first neighbor robot at time k; and determine the control input information deviation of the first neighbor robot at time k based on the difference between the second control input information and the third control input information.

[0251] In some embodiments, the determining unit 12 is specifically used to determine the ratio of the difference between the second control input information and the third control input information to N as the control input information deviation of the first neighbor robot corresponding to the target robot at time k.

[0252] In some embodiments, the determining unit 12 is specifically configured to multiply a first value by the speed deviation of the first neighboring robot at time k, and then add it to the position deviation of the first neighboring robot at time k to obtain a first sum; multiply the first sum by a negative second value to obtain a first product; if the first neighboring robot and the target robot can communicate, the second value is a preset value greater than 0; if the first neighboring robot and the target robot cannot communicate, the second value is 0; and add the first product to the control input information deviation of the first neighboring robot at time k to obtain the abnormal value of the first neighboring robot at time k.

[0253] In some embodiments, the determining unit 12 is specifically configured to, for the j-th second neighbor robot among the N second neighbor robots, determine the speed difference and position difference between the first neighbor robot and the j-th second neighbor robot at time k based on the speed and position of the first neighbor robot and the j-th second neighbor robot at time k, respectively; multiply the speed difference by a first value and add it to the position difference to obtain a second sum; multiply the second sum by a negative third value to obtain the control input information corresponding to the j-th second neighbor robot; if the j-th second neighbor robot can communicate with the first neighbor robot, the third value is a preset value greater than 0; if the j-th second neighbor robot cannot communicate with the first neighbor robot, the third value is 0; and determine the sum of the control input information corresponding to each of the N second neighbor robots as the third control input information.

[0254] In some embodiments, the compensation unit 13 is specifically used to determine the fourth control input information of the target robot at time k+1; determine the target control input information of the target robot based on the abnormal value of the first neighbor robot and the fourth control input information; and perform movement based on the target control input information to compensate the movement of the first neighbor robot at time k.

[0255] In some embodiments, the compensation unit 13 is specifically used to add the abnormal value of the first neighbor robot to the fourth control input information through multiple compensations to obtain the target control input information of the target robot.

[0256] In some embodiments, the compensation unit 13 is specifically used to determine the compensation value of each compensation in the first T-1 compensations and the compensation value of the Tth compensation based on a preset compensation period T and the abnormal value of the first neighbor robot, where T is a positive integer greater than 1; and to compensate the abnormal value of the first neighbor robot into the fourth control input information in T parts based on the compensation value of each compensation in the first T-1 compensations and the compensation value of the Tth compensation, thereby obtaining the target control input information of the target robot.

[0257] In some embodiments, the compensation unit 13 is specifically used to determine the negative number of the ratio of twice the abnormal value to T-1 as the compensation value for each of the first T-1 compensations.

[0258] In some embodiments, the compensation unit 13 is specifically used to perform motion compensation on the first neighbor robot if the abnormal value of the first neighbor robot is less than or equal to a preset threshold; and to remove the first neighbor robot from the robot formation if the abnormal value of the first neighbor robot is greater than the preset threshold.

[0259] In some embodiments, the target robot is any one of the normal robots in the robot formation.

[0260] It should be understood that the device embodiments and method embodiments can correspond to each other, and similar descriptions can be referred to the method embodiments. To avoid repetition, further details will not be provided here. Specifically, Figure 5 The apparatus shown can perform the embodiments of the above-described method, and the foregoing and other operations and / or functions of each module in the apparatus are for implementing the embodiments of the above-described method, which will not be described in detail here for the sake of brevity.

[0261] The apparatus of this application embodiment has been described above from the perspective of functional modules in conjunction with the accompanying drawings. It should be understood that this functional module can be implemented in hardware, in software instructions, or in a combination of hardware and software modules. Specifically, the steps of the method embodiments in this application can be completed by integrated logic circuits in the processor's hardware and / or by software instructions. The steps of the method disclosed in this application embodiment can be directly embodied as being executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. Optionally, the software module can reside in a mature storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps in the above method embodiments.

[0262] Figure 6This is a schematic block diagram of a robot provided in an embodiment of this application, which is used to perform the above-described method embodiments.

[0263] like Figure 6 As shown, the robot 30 may include:

[0264] The system includes a memory 31 and a processor 32. The memory 31 stores a computer program 33 and transfers the program code 33 to the processor 32. In other words, the processor 32 can retrieve and run the computer program 33 from the memory 31 to implement the methods described in the embodiments of this application.

[0265] For example, the processor 32 can be used to execute the above method steps according to the instructions in the computer program 33.

[0266] In some embodiments of this application, the processor 32 may include, but is not limited to:

[0267] General-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0268] In some embodiments of this application, the memory 31 includes, but is not limited to:

[0269] Volatile memory and / or non-volatile memory. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), and Direct Rambus RAM (DR RAM).

[0270] In some embodiments of this application, the computer program 33 may be divided into one or more modules, which are stored in the memory 31 and executed by the processor 32 to complete the page recording method provided in this application. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program 33 in the robot.

[0271] like Figure 6 As shown, the robot 30 may also include:

[0272] Transceiver 34, which can be connected to processor 32 or memory 31.

[0273] The processor 32 can control the transceiver 34 to communicate with other devices; specifically, it can send information or data to other devices or receive information or data sent by other devices. The transceiver 34 may include a transmitter and a receiver. The transceiver 34 may further include antennas, and the number of antennas may be one or more.

[0274] It should be understood that the various components in the robot 30 are connected through a bus system, which includes a data bus, a power bus, a control bus, and a status signal bus.

[0275] According to one aspect of this application, a computer storage medium is provided that stores a computer program thereon, which, when executed by a computer, enables the computer to perform the methods of the above-described method embodiments.

[0276] This application also provides a computer program product containing instructions that, when executed by a computer, cause the computer to perform the methods described in the above method embodiments.

[0277] According to another aspect of this application, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A robot's processor reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the robot to perform the method described in the above-described method embodiments.

[0278] In other words, when implemented using software, it can be implemented wholly or partially in the form of a computer program product. This computer program product includes one or more computer instructions. When these computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., digital video disc (DVD)), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0279] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0280] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0281] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. For example, the functional modules in the various embodiments of this application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0282] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A robot formation control method, characterized in that, include: For any target robot in the robot formation, receive first motion state information sent by a first neighbor robot at time k+1, where the first neighbor robot is a neighbor robot of the target robot in the robot formation, and k is a positive integer; Based on the first motion state information, determine whether the first neighbor robot is abnormal at time k; When it is determined that the first neighbor robot is abnormal at time k, the abnormal value of the first neighbor robot at time k is determined; Based on the abnormal values ​​of the first neighbor robot, motion compensation is performed on the first neighbor robot; Wherein, the first motion state information includes the motion state information of N second neighbor robots of the first neighbor robot at time k, where N is a positive integer, and the target robot is included among the N second neighbor robots, then determining whether the first neighbor robot is abnormal at time k based on the first motion state information includes: The motion state information of the target robot at time k is obtained from the motion state information of the N second neighbor robots at time k, and the motion state information of the target robot at time k included in the first motion state information is compared with the motion state information of the target robot at time k saved by the target robot to determine whether the first neighbor robot is abnormal at time k.

2. The method according to claim 1, characterized in that, If the motion state information of the target robot at time k includes the speed and position of the target robot at time k, then comparing the motion state information of the target robot at time k included in the first motion state information with the motion state information of the target robot at time k stored by the target robot to determine whether the first neighboring robot is abnormal at time k includes: If the speed and position of the target robot at time k included in the first motion state information are consistent with the speed and position of the target robot at time k stored in the target robot, then it is determined that the first neighbor robot is normal at time k. If the speed and position of the target robot at time k included in the first motion state information are inconsistent with at least one of the speed and position of the target robot at time k stored by the target robot, then the first neighbor robot is determined to be abnormal at time k.

3. The method according to claim 1, characterized in that, The step of determining whether the first neighbor robot is abnormal at time k based on the first motion state information further includes: Obtain the second motion state information sent by the first neighboring robot at time k; Based on the first motion state information and the second motion state information, determine whether the first neighbor robot is abnormal at time k.

4. The method according to claim 3, characterized in that, If the first motion state information includes the motion state information of the N second neighbor robots at time k, and the second motion state information includes the motion state information of the first neighbor robot at time k, then determining whether the first neighbor robot is abnormal at time k based on the first motion state information and the second motion state information includes: Based on the motion state information of the N second neighbor robots at time k and the motion state information of the first neighbor robot at time k, determine whether the first neighbor robot is abnormal at time k.

5. The method according to claim 4, characterized in that, If the motion state information of the second neighbor robot at time k includes the speed and position of the second neighbor robot at time k, and the motion state information of the first neighbor robot at time k includes the speed, position, and second control input information of the first neighbor robot at time k, then determining whether the first neighbor robot is abnormal at time k based on the motion state information of the N second neighbor robots at time k and the motion state information of the first neighbor robot at time k includes: Based on the speed and position of the N second neighbor robots at time k, and the speed and position of the first neighbor robot at time k, determine the third control input information of the first neighbor robot at time k; If the third control input information is consistent with the second control input information, then it is determined that the first neighbor robot is normal at time k. If the third control input information is inconsistent with the second control input information, then the first neighbor robot is determined to be abnormal at time k.

6. The method according to claim 1, characterized in that, The determination of the outlier value of the first neighbor robot at time k includes: Based on the first motion state information, the abnormal value of the first neighboring robot at time k is determined.

7. The method according to claim 6, characterized in that, The step of determining the outlier value of the first neighbor robot at time k based on the first motion state information includes: Obtain the second motion state information sent by the first neighboring robot at time k; Based on the first motion state information and the second motion state information, the outlier value of the first neighbor robot at time k is determined.

8. The method according to claim 7, characterized in that, The step of determining the outlier value of the first neighbor robot at time k based on the first motion state information and the second motion state information includes: Based on the first motion state information, determine the velocity deviation and position deviation of the first neighboring robot at time k; Based on the first motion state information and the second motion state information, the control input information deviation of the first neighbor robot at time k is determined; The abnormal value of the first neighboring robot at time k is determined based on at least one of the velocity deviation, position deviation, and control input information deviation of the first neighboring robot at time k.

9. The method according to claim 8, characterized in that, If the first motion state information includes the speed and position of N second neighbor robots of the first neighbor robot at time k, and the target robot is included among the N second neighbor robots, then determining the speed deviation and position deviation of the first neighbor robot at time k based on the first motion state information includes: The velocity and position of the target robot at time k are obtained from the first motion state information; Based on the difference between the target robot's velocity at time k stored in the target robot's data and the target robot's velocity at time k included in the first motion state information, the velocity deviation of the first neighboring robot at time k is determined. Based on the difference between the target robot's position at time k, which is stored in the target robot, and the target robot's position at time k, which is included in the first motion state information, the position deviation of the first neighboring robot at time k is determined.

10. The method according to claim 8, characterized in that, If the second motion state information includes the velocity, position, and second control input information of the first neighbor robot at time k, and the first motion state information includes the velocity and position of N second neighbor robots of the first neighbor robot at time k, then determining the control input information deviation of the first neighbor robot at time k based on the first motion state information and the second motion state information includes: Based on the speed and position of the N second neighbor robots at time k, and the speed and position of the first neighbor robot at time k, determine the third control input information of the first neighbor robot at time k; Based on the difference between the second control input information and the third control input information, the control input information deviation of the first neighbor robot at time k is determined.

11. The method according to claim 10, characterized in that, The step of determining the control input information deviation of the first neighbor robot at time k based on the difference between the second control input information and the third control input information includes: The ratio of the difference between the second control input information and the third control input information to N is determined as the control input information deviation of the first neighboring robot corresponding to the target robot at time k.

12. The method according to claim 8, characterized in that, The step of determining the outlier value of the first neighbor robot at time k based on at least one of the velocity deviation, position deviation, and control input information deviation of the first neighbor robot at time k includes: Multiply the first value by the velocity deviation of the first neighboring robot at time k, and then add it to the position deviation of the first neighboring robot at time k to obtain the first sum. Multiply the first sum by the negative second value to obtain the first product. If the first neighboring robot and the target robot can communicate, the second value is a preset value greater than 0. If the first neighboring robot and the target robot cannot communicate, the second value is 0. The first product is added to the deviation of the control input information of the first neighboring robot at time k to obtain the abnormal value of the first neighboring robot at time k.

13. The method according to claim 5 or 10, characterized in that, The step of determining the third control input information of the first neighbor robot at time k based on the speed and position of the N second neighbor robots at time k, and the speed and position of the first neighbor robot at time k, includes: For the j-th second neighbor robot among the N second neighbor robots, the speed difference and position difference between the first neighbor robot and the j-th second neighbor robot at time k are determined based on the speed and position of the first neighbor robot and the j-th second neighbor robot at time k, respectively. Multiply the velocity difference by the first value and then add it to the position difference to obtain the second sum. Multiply the second sum by the negative third value to obtain the control input information corresponding to the j-th second neighbor robot. If the j-th second neighbor robot can communicate with the first neighbor robot, the third value is a preset value greater than 0. If the j-th second neighbor robot cannot communicate with the first neighbor robot, the third value is 0. The sum of the control input information corresponding to each of the N second neighbor robots is determined as the third control input information.

14. The method according to any one of claims 1-12, characterized in that, The step of performing motion compensation on the first neighbor robot based on the abnormal values ​​of the first neighbor robot includes: Determine the fourth control input information of the target robot at time k+1; Based on the abnormal value of the first neighboring robot and the fourth control input information, the target control input information of the target robot is determined; The robot moves according to the target control input information to compensate for the movement of the first neighboring robot at time k.

15. The method according to claim 14, characterized in that, The step of determining the target control input information of the target robot based on the abnormal value of the first neighboring robot and the fourth control input information includes: Through multiple compensations, the abnormal values ​​of the first neighboring robot are added to the fourth control input information to obtain the target control input information of the target robot.

16. The method according to claim 15, characterized in that, The step of adding the outlier values ​​of the first neighboring robot to the fourth control input information through multiple compensations to obtain the target control input information of the target robot includes: Based on the preset compensation period T and the abnormal value of the first neighbor robot, determine the compensation value of each compensation in the first T-1 compensations, as well as the compensation value of the Tth compensation, where T is a positive integer greater than 1. Based on the compensation value of each compensation in the previous T-1 compensations and the compensation value of the Tth compensation, the abnormal value of the first neighbor robot is compensated into the fourth control input information in T parts to obtain the target control input information of the target robot.

17. A robot formation control device, characterized in that, include: The receiving unit is configured to receive, for any target robot in the robot formation, first motion state information sent by a first neighboring robot at time k+1, wherein the first neighboring robot is a neighboring robot of the target robot in the robot formation, and k is a positive integer; The determining unit is configured to determine whether the first neighboring robot is abnormal at time k based on the first motion state information, and to determine the abnormal value of the first neighboring robot when it is determined that the first neighboring robot is abnormal at time k. The compensation unit is used to perform motion compensation on the first neighbor robot based on the abnormal values ​​of the first neighbor robot. Wherein, the first motion state information includes the motion state information of N second neighbor robots of the first neighbor robot at time k, where N is a positive integer, and the target robot is included among the N second neighbor robots, then the determining unit is specifically used to obtain the motion state information of the target robot at time k from the motion state information of the N second neighbor robots at time k, and compare the motion state information of the target robot at time k included in the first motion state information with the motion state information of the target robot at time k stored by the target robot, to determine whether the first neighbor robot is abnormal at time k.

18. A robot, characterized in that, Includes transceiver, processor, and memory; The transceiver is used to receive motion status information sent by the neighboring robot; The memory is used to store computer programs; The processor is configured to execute the computer program to implement the method as described in any one of claims 1 to 16.

19. A computer-readable storage medium, characterized in that, Used to store computer programs that cause a computer to perform the method as described in any one of claims 1 to 16.

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