Multi-robot formation path determination method, robot control method and device

Abstract

The embodiment of the invention discloses a multi-robot formation path determination method, a robot control method and a robot control device, which are used for improving the efficiency of path planning. The multi-robot formation path determination method comprises the steps that the initial position and the initial form of a formation are acquired, the target position and the target form of the formation are acquired, and the form indicates the positions of a plurality of robots in the formation under a formation coordinate system; based on a preset sample generation algorithm, generating at least one sample position of the formation and a form on the sample position according to the initial position, the target position and the target form; on the basis of the forms of the formation on all the positions, generating a path fragment between two positions meeting a first preset condition in all the positions, so as to generate a path tree formed by a plurality of path fragments; and finding out a target path from the initial position to the target position, which meets a second preset condition, from the path tree based on a preset path planning algorithm.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of robots, and in particular to a multi-robot formation path determination method, a robot control method and a device. Background Art

[0002] With the continuous development of robot technology, people's production and living standards are also constantly improving. Among them, multi-robot collaborative systems can complete more complex tasks than single robots, such as search and rescue operations, intelligent logistics, precision operations, etc. The existing way to realize multi-robot collaborative systems is to use multi-robot formation control technology to organize multiple robots into a specific formation, complete the overall task along the designed path, and ensure the orderliness of the formation and the coordination of robot movements to avoid mutual interference and collision.

[0003] However, in the existing solutions, manual experience is needed to plan the path of the multi-robot formation, and the manual planning efficiency is low and cannot meet the task requirements. Summary of the invention

[0004] The embodiments of the present application provide a multi-robot formation path determination method, a robot control method and a device for improving the efficiency of path planning.

[0005] A first aspect of an embodiment of the present application provides a multi-robot formation path determination method, which is applied to a control system, and the method includes:

[0006] Acquire an initial position and an initial form of the formation, and acquire a target position and a target form of the formation, wherein the form indicates positions of multiple robots in the formation in a formation coordinate system;

[0007] Based on a preset sample generation algorithm, generating at least one sample position of the formation and a form at the sample position according to the initial position, the target position and the target form;

[0008] Based on the form of the formation at all positions, generating a path segment between two positions that meet a first preset condition among all positions, so as to generate a path tree consisting of a plurality of the path segments;

[0009] Based on a preset path planning algorithm, a target path from the initial position to the target position that meets a second preset condition is found from the path tree.

[0010] Optionally, the generating at least one sample position of the formation and the form at the sample position according to the initial position, the target position and the target form based on a preset sample generating algorithm includes:

[0011] Taking the initial position as a reference position;

[0012] Randomly generating a sample position of the formation that does not overlap with an obstacle at a preset step length from the reference position, and a straight line segment between the sample position and the reference position does not pass through an obstacle;

[0013] The sample position is used as a new reference position, and the first designation step is returned to execute until a distance between a sample position and the target position is less than the preset step length, and then the generation of sample positions is stopped, wherein the first designation step is to randomly generate a sample position of the formation that does not overlap with an obstacle at a preset step length from the reference position, and a straight line segment between the sample position and the reference position does not pass through an obstacle;

[0014] Randomly generate a form of the formation in which the positions of the robots in the formation do not overlap with obstacles at overlapping sample positions, and determine that the form of the formation at non-overlapping sample positions is the target form, wherein the overlapping sample positions are sample positions in which the positions of the robots in the formation overlap with obstacles when the formation is in the target form, and the non-overlapping sample positions are sample positions in which the positions of the robots in the formation do not overlap with obstacles when the formation is in the target form;

[0015] or,

[0016] Based on a fast search random tree algorithm or a fast search random tree star algorithm, generating the sample position according to the initial position and the target position;

[0017] The form of the formation in which the positions of the robots in the formation do not overlap with obstacles is randomly generated at the sample positions.

[0018] Optionally, the generating a path segment between two positions that meet a first preset condition among all positions based on the form of the formation at all positions includes:

[0019] Selecting two positions from all positions of the formation as detection positions;

[0020] Selecting a first robot position that has not been detected in the formation at the first detection position, and selecting a second robot position that has not been detected in the formation at the second detection position;

[0021] Determining whether there is a collision-free trajectory between the first robot position and the second robot position;

[0022] If so, generating a collision-free robot trajectory between the first robot position and the second robot position;

[0023] Determining whether the number of robot trajectories is consistent with the number of robots in the formation;

[0024] If they are consistent, a path segment is generated between the first detection position and the second detection position, and the second specified step is returned to execute until all positions of the formation have been selected, wherein the second specified step is to select two positions from all positions of the formation as detection positions;

[0025] If they are inconsistent, returning to execute the third designation step, the third designation step is to select a first robot position that has not been detected in the formation at the first detection position, and to select a second robot position that has not been detected in the formation at the second detection position;

[0026] If it does not exist, then select any undetected robot position in the formation at the first detection position as the new first robot position, and return to execute the fourth specified step until all robot positions at the first detection position fail to generate a collision-free robot trajectory with the second robot position, then return to execute the second specified step, wherein the fourth specified step is to determine whether there is a collision-free trajectory between the first robot position and the second robot position.

[0027] Optionally, the finding a target path from the initial position to the target position that satisfies a second preset condition from the path tree based on a preset path planning algorithm includes:

[0028] Based on a preset path planning algorithm, a target path from the initial position to the target position with the shortest time or the minimum energy consumption is found from the path tree.

[0029] A second aspect of an embodiment of the present application provides a robot control method, which is applied to each robot in a control system or a multi-robot formation, and the method includes:

[0030] obtaining an expected robot position of the robot according to a form of the formation on a target path of the formation, the form indicating positions of a plurality of robots in the formation in a formation coordinate system;

[0031] Based on a preset instruction optimization algorithm, and on the premise of satisfying the robot motion function and the preset minimum spacing distance between the robots, a target robot control instruction is obtained that makes the actual robot position closest to the expected robot position, so that the robot moves based on the target robot control instruction, wherein the independent variables of the robot motion function include the current robot position and the current robot control instruction, and the dependent variable includes the robot position at the next moment, and the robot motion function is obtained by a preset formation motion equation.

[0032] Optionally, after obtaining the target robot control instruction that makes the actual robot position closest to the expected robot position based on the preset instruction optimization algorithm under the premise of satisfying the robot motion function and the preset minimum spacing distance between the robots, the method further includes:

[0033] Obtaining correction instructions for the robot based on preset robot behavior rules;

[0034] The target robot control instruction is adjusted using the correction instruction.

[0035] A third aspect of an embodiment of the present application provides a multi-robot formation path determination device, comprising:

[0036] An acquisition unit, used to acquire an initial position and an initial form of the formation, and acquire a target position and a target form of the formation, wherein the form indicates the positions of multiple robots in the formation in a formation coordinate system;

[0037] a generating unit, configured to generate at least one sample position of the formation and a form at the sample position according to the initial position, the target position and the target form based on a preset sample generating algorithm;

[0038] The generating unit is further configured to generate a path segment between two positions that meet a first preset condition among all positions based on the form of the formation at all positions, so as to generate a path tree consisting of a plurality of the path segments;

[0039] An analysis unit is used to find a target path from the initial position to the target position that meets a second preset condition from the path tree based on a preset path planning algorithm.

[0040] A fourth aspect of the present application provides a robot control device, including:

[0041] a processing unit, configured to obtain a desired robot position of the robot according to a form of the formation on a target path of the formation, the form indicating positions of a plurality of robots in the formation in a formation coordinate system;

[0042] A control unit is used to obtain a target robot control instruction that makes the actual robot position closest to the expected robot position based on a preset instruction optimization algorithm, on the premise of satisfying the robot motion function and the preset minimum spacing distance between the robots, so that the robot moves based on the target robot control instruction, wherein the independent variables of the robot motion function include the current robot position and the current robot control instruction, and the dependent variable includes the robot position at the next moment, and the robot motion function is obtained by a preset formation motion equation.

[0043] A fifth aspect of an embodiment of the present application provides a multi-robot formation path determination device, comprising:

[0044] CPU, memory and input / output interface;

[0045] The memory is a short-term storage memory or a persistent storage memory;

[0046] The central processing unit is configured to communicate with the memory and execute instructions in the memory to perform the aforementioned method.

[0047] A sixth aspect of the embodiments of the present application provides a robot control device, including:

[0048] CPU, memory and input / output interface;

[0049] The memory is a short-term storage memory or a persistent storage memory;

[0050] The central processing unit is configured to communicate with the memory and execute instructions in the memory to perform the aforementioned method.

[0051] A seventh aspect of an embodiment of the present application provides a computer-readable storage medium, comprising instructions, which, when executed on a computer, enable the computer to execute the aforementioned method.

[0052] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0053] First, the initial position and initial form of the formation are obtained, and the target position and target form of the formation are obtained. Then, based on the preset sample generation algorithm, at least one sample position of the formation and the form at the sample position are generated according to the initial position, the target position and the target form. Then, based on the form of the formation at all positions, path segments are generated between two positions that meet the first preset condition in all positions to generate a path tree composed of multiple path segments. Finally, based on the preset path planning algorithm, the target path from the initial position to the target position that meets the second preset condition is found from the path tree. The target path that does not collide with obstacles is automatically planned by the position and form of multiple robots, without manual planning, with a high degree of automation and high planning efficiency, which can meet the needs of the task. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 A schematic diagram of an embodiment of a multi-robot formation path determination method disclosed in the present application;

[0055] Figure 2 A schematic diagram of another embodiment of a multi-robot formation path determination method disclosed in the present application;

[0056] Figure 3 A schematic diagram of the sample position sampling of the formation disclosed in this application;

[0057] Figure 4 A schematic diagram of robot trajectory generation for robots in a formation disclosed in the present application;

[0058] Figure 5 A schematic diagram for generating a path tree disclosed in this application;

[0059] Figure 6 A schematic diagram of an embodiment of a robot control method disclosed in the present application;

[0060] Figure 7 A schematic diagram of another embodiment of a robot control method disclosed in the present application;

[0061] Figure 8 A schematic diagram of an embodiment of a multi-robot formation path determination device disclosed in the present application;

[0062] Fig. 9 This is a schematic diagram of another embodiment of a multi-robot formation path determination device disclosed in the present application;

[0063] Fig.10 A schematic diagram of an embodiment of a robot control device disclosed in the present application;

[0064] Fig.11 This is a schematic diagram of another embodiment of a robot control device disclosed in this application. DETAILED DESCRIPTION

[0065] The present application is further described in detail below in conjunction with the accompanying drawings.

[0066] The embodiments of the present application provide a multi-robot formation path determination method, a robot control method and a device for improving the efficiency of path planning.

[0067] Robot technology has been widely used in various industries such as medicine and logistics. Among them, multi-robot formation control technology is used to realize the function of multi-robot formation. However, in the existing scheme, manual planning of the path of the multi-robot formation is required, and the efficiency of manual planning is low and cannot meet the task requirements. In order to solve the above problems, the present application provides a multi-robot formation path determination method, a robot control method and related devices, which automatically plan the target path that does not collide with obstacles through the position and form of multiple robots, without manual planning, with a high degree of automation and high planning efficiency, strong robustness and adaptability, and can automatically avoid obstacles and automatically adjust the form of multi-robot formations, and can efficiently carry out various tasks.

[0068] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0069] The terms "first", "second", "third", "fourth", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0070] The following is a description of a multi-robot formation path determination method of the present application. Figure 1 , an embodiment of a multi-robot formation path determination method of the present application is applied to a control system, and the method includes:

[0071] 101. Obtaining the initial position and initial shape of the formation, and obtaining the target position and target shape of the formation;

[0072] Get the initial position and initial form of the formation, and get the target position and target form of the formation, where the form indicates the position of multiple robots in the formation in the formation coordinate system. The initial position of the formation is the initial position in the inertial coordinate system, the initial form is the form of the formation at the initial position, the target position is the final position to be reached in the inertial coordinate system, and the target form is the form of the formation at the target position. For example, if the center point of the formation is the origin of the formation coordinate system (0, 0), and there is a robot one unit above the origin, then the robot position is (0, 1), instead of being determined by the inertial coordinate system, in which case the initial position of the formation in the inertial coordinate system can be (10, 20). The inertial coordinate system and the formation coordinate system can be converted to each other.

[0073] 102. Generate at least one sample position and a shape at the sample position of the formation according to the initial position, the target position, and the target shape based on a preset sample generation algorithm;

[0074] Based on the preset sample generation algorithm, at least one sample position of the formation and the form at the sample position are generated according to the initial position, the target position and the target form. Since there are only two positions of the formation, the conditions for generating the target path are insufficient, so at least one sample position of the formation is generated at a non-collision point, and the forms corresponding to these sample positions are generated.

[0075] 103. Based on the forms of the formation at all positions, generate a path segment between two positions that meet a first preset condition among all positions to generate a path tree consisting of a plurality of path segments;

[0076] Based on the form of the formation at all positions, a path segment is generated between two positions that meet the first preset condition among all positions to generate a path tree consisting of multiple path segments. Among them, all positions include the initial position, sample position and target position mentioned above, and the first preset condition is that there should be no obstacles between the two positions. Simply put, among all positions of the formation, any two positions are used to try to generate path segments. If the first preset condition is met, it is generated, and if the first preset condition is not met, it is not generated. After trying all any two positions, path segments can be obtained, and the path segments are connected to form a path tree.

[0077] 104. Find a target path from the initial position to the target position that meets a second preset condition from the path tree based on a preset path planning algorithm.

[0078] Based on the preset path planning algorithm, a target path from the initial position to the target position that meets the second preset condition is found from the path tree. Among them, the second preset condition has many situations, such as the shortest time of the formation, the minimum energy consumption of all robots in the formation, etc., which are not limited here. The path planning algorithm can be selected according to actual needs, or it can be designed by itself, which is not limited here.

[0079] In the embodiment of the present application, the initial position and initial form of the formation are first obtained, and the target position and target form of the formation are obtained, and then based on the preset sample generation algorithm, at least one sample position of the formation and the form at the sample position are generated according to the initial position, target position and target form, and then based on the form of the formation at all positions, path segments are generated between two positions that meet the first preset condition in all positions to generate a path tree composed of multiple path segments, and finally based on the preset path planning algorithm, the target path from the initial position to the target position that meets the second preset condition is found from the path tree. The target path that does not collide with obstacles is automatically planned by the position and form of multiple robots, without manual planning, with a high degree of automation and high planning efficiency, which can meet the needs of the task.

[0080] It is understandable that this embodiment can use a big data model based on Markov decision process and deep reinforcement learning algorithm such as PPO to train the relevant data of the formation to obtain the method steps of this embodiment, or it can be designed by itself. The specific details are not limited here. The following describes the relevant method steps for determining the target path. In addition, this embodiment is executed by a control system, that is, a device or platform with a processor that can determine the target path of the formation. Please refer to Figure 2 Another embodiment of a multi-robot formation path determination method of the present application is applied to a control system, and the method includes:

[0081] 201. Obtaining the initial position and initial shape of the formation, and obtaining the target position and target shape of the formation;

[0082] Get the initial position and initial form of the formation, and get the target position and target form of the formation, where the form indicates the position of multiple robots in the formation in the formation coordinate system. The initial position of the formation is the initial position in the inertial coordinate system, the initial form is the form of the formation at the initial position, the target position is the final position to be reached in the inertial coordinate system, and the target form is the form of the formation at the target position, or the desired ideal relative position of each robot in the formation. For example, if the center point of the formation is the origin of the formation coordinate system (0, 0), then there is a robot one unit above the origin, and the robot position is (0, 1), instead of being determined by the inertial coordinate system. At this time, the initial position of the formation in the inertial coordinate system can be (10, 20). The inertial coordinate system and the formation coordinate system can be converted into each other.

[0083] 202. Generate at least one sample position and a form at the sample position of the formation according to the initial position, the target position and the target form based on a preset sample generation algorithm;

[0084] Based on a preset sample generation algorithm, at least one sample position of the formation and the form at the sample position are generated according to the initial position, the target position and the target form. Among them, the sample generation algorithm can be a fast search random tree algorithm, etc., which is not specifically limited here. Since there are only two positions of the formation, the conditions for generating the target path are insufficient, so at least one sample position of the formation is generated at a collision-free position, and the forms corresponding to these sample positions are generated, and each sample position corresponds to the possible position of the formation at a certain moment. Two implementation methods are described below.

[0085] In one embodiment, the initial position is first used as the reference position. Then, a sample position of the formation that does not overlap with an obstacle is randomly generated at a preset step length from the reference position, and the straight line segment between the sample position and the reference position does not pass through an obstacle. The sample position is then used as a new reference position, and the first specified step is returned to execute until a distance between a sample position and the target position is less than the preset step length, and the generation of sample positions is stopped, wherein the first specified step is to randomly generate a sample position of the formation that does not overlap with an obstacle at a preset step length from the reference position, and the straight line segment between the sample position and the reference position does not pass through an obstacle. Finally, the form of the formation in which the position of the robot in the formation does not overlap with an obstacle is randomly generated at the overlapping sample position, and the form of the formation at the non-overlapping sample position is determined to be the target form, wherein the overlapping sample position is a sample position in which the position of the robot in the formation overlaps with an obstacle when the formation is in the target form, and the non-overlapping sample position is a sample position in which the position of the robot in the formation does not overlap with an obstacle when the formation is in the target form. The preset step size can be set according to actual needs. There is no specific limit here. For details, please refer to Figure 3 , the center of each box represents the position of a formation, the cross-shaped box is the robot position, the center of the leftmost box is the initial position, the initial shape is X(t), the center of the upper right corner box is the target position, and the corresponding target shape is S. Taking the initial position as the reference position, multiple sample positions are continuously generated based on the preset step size. Figure 3All positions except the initial position and the target position are sample positions (some sample positions are selected as representatives). The sample positions must not overlap with obstacles, and the straight line between the sample position and the reference position does not pass through obstacles. Among them, the center of the second box from the left, the center of the top box, and the center of the box in the lower right corner are non-overlapping sample positions, because they can be in the target shape S, and the centers of the two boxes in the gap between obstacles are overlapping sample positions. If they are in the target shape S, the robot position will overlap with the obstacle, so they randomly generate shapes that do not overlap with obstacles, which are Xi (two diagonal rows) and Xj (one diagonal row).

[0086] In another embodiment, a sample position can be first generated based on the initial position and the target position based on a fast search random tree algorithm or a fast search random tree star algorithm. Finally, a formation shape in which the position of the robots in the formation does not overlap with obstacles is randomly generated at the sample position. Specifically, a position that does not overlap with obstacles is first randomly set, the sample position is connected to the initial position, and the sample position is set at a preset step length on the connection line. Then, the next sample position is re-determined based on the sample position until it is connected to the target position, which will not be repeated here. The shape is randomly generated and does not overlap with obstacles, and the specifics are not limited here.

[0087] 203. Select any two positions from all positions of the formation as detection positions;

[0088] Two positions are randomly selected from all positions of the formation as detection positions. All positions include initial positions, sample positions and target positions. Specifically, any two positions that are not connected by a path and have not been processed by steps 204 to 206 are determined as detection positions.

[0089] 204. Select a first robot position that has not been detected in the formation at the first detection position, and select a second robot position that has not been detected in the formation at the second detection position;

[0090] A first robot position which has not been detected is selected from the formation at the first detection position, and a second robot position which has not been detected is selected from the formation at the second detection position, wherein the position which has not been detected means the position which has not been processed by step 205 .

[0091] 205. Determine whether there is a collision-free trajectory between the first robot position and the second robot position. If not, execute step 206. If yes, execute step 207.

[0092] Specifically, it is determined whether the first robot position and the second robot position can be connected to form a robot trajectory, and whether the robot trajectory cannot overlap with an obstacle.

[0093] 206. Select any robot position that has not been detected in the formation at the first detection position as a new first robot position, and return to step 205, until all robot positions at the first detection position fail to generate a collision-free robot trajectory with the second robot position, and then return to step 203;

[0094] If there is no collision-free track between the first robot position and the second robot position, then specifically, a robot position is selected from the formation at the first detection position as a new first robot position, and the robot position cannot be a position that has been used to attempt to connect to the second robot position to form a track, and then it is re-determined whether there is a collision-free track between the two positions. If there is, step 207 is executed, and if not, a new first robot position is selected. If all robot positions at the first detection position cannot be connected to the second robot position to form a collision-free track, it means that the first detection position cannot be connected to the second detection position to form a path segment, and the process returns to step 203 to reselect the detection position. It can be understood that it is also possible to fix the first robot position and exhaustively enumerate the robot positions at the second detection position to try to connect to the first robot position, which will not be described in detail here.

[0095] 207. Generate a collision-free robot trajectory between the first robot position and the second robot position;

[0096] If there is a collision-free trajectory between the first robot position and the second robot position, a collision-free robot trajectory is generated between the first robot position and the second robot position.

[0097] 208. Determine whether the number of robot trajectories is consistent with the number of robots in the formation. If not, return to step 204. If yes, execute step 209.

[0098] In short, step 209 can only be executed if the robot trajectories of all robot positions in the formation at the first detection position correspond one-to-one with those of all robot positions in the formation at the second detection position. If not, it means that no path segment can be generated between the two detection positions.

[0099] For ease of understanding, steps 203 to 208 are described below with examples. Figure 4, select any two positions, that is, determine the sample positions with the shapes Xi and Xj as the detection positions, regard the left one as the first detection position, and the right one as the second detection position, randomly generate the first robot position and the second robot position, for example, randomly determine the top robot position in the formation of the first detection position as the first robot position, for example, randomly determine the bottom robot position in the formation of the second detection position as the second robot position, and there is a collision-free trajectory between these two positions, then connect these two robot positions to generate a robot trajectory. Since there is only one robot trajectory and there are five robots, it is necessary to continue to try to generate robot trajectories, randomly determine the bottom robot position in the formation of the first detection position as the first robot position, for example, randomly determine the leftmost robot position in the formation of the second detection position as the second robot position, and so on to try to generate robot trajectories until five robot trajectories are generated.

[0100] 209. Generate a path segment between the first detection position and the second detection position, and return to step 203 until all positions of the formation have been selected to generate a path tree consisting of multiple path segments;

[0101] If the number of robot trajectories is the same as the number of robots in the formation, a path segment is generated between the first detection position and the second detection position, and the detection positions are reselected to try to generate path segments until all positions in the formation have been selected, so as to generate a path tree consisting of multiple path segments. Figure 5 ,Multiple path segments constitute a path tree with the initial position as the starting point and the target position as the end point.

[0102] 210. Find a target path from the initial position to the target position that meets a second preset condition from the path tree based on a preset path planning algorithm.

[0103] Based on a preset path planning algorithm, a target path from the initial position to the target position that meets the second preset condition is found from the path tree. Specifically, the path planning algorithm can be a fast search random tree algorithm, a fast search random tree star algorithm, an A star algorithm or a CHOMP algorithm, etc., which are not specifically limited here. The second preset condition can be the shortest time, or the minimum energy consumption, etc., which are not specifically limited here.

[0104] In addition, during the actual operation of the formation, it can also detect obstacles and other surrounding environment information in real time and dynamically adjust the formation's target path. The formation can be split into multiple sub-formations and controlled separately, and the formation constraints can be abandoned to control multi-robot distributed conflict-free control.

[0105] In this embodiment, the initial position and initial form of the formation are first obtained, and the target position and target form of the formation are obtained, and then based on the preset sample generation algorithm, at least one sample position of the formation and the form at the sample position are generated according to the initial position, the target position and the target form, and then based on the form of the formation at all positions, path segments are generated between two positions that meet the first preset condition in all positions to generate a path tree composed of multiple path segments, and finally based on the preset path planning algorithm, the target path from the initial position to the target position that meets the second preset condition is found from the path tree. The target path that does not collide with obstacles is automatically planned by the position and form of multiple robots, without manual planning, with a high degree of automation and high planning efficiency, which can meet the needs of the task.

[0106] A method for determining a multi-robot formation in an embodiment of the present application is described above. A method for controlling a robot in an embodiment of the present application is described below. The execution device of the robot control method can be a control system or a robot in a multi-robot formation. If centralized control is used, the control system is used. The control system directly calculates the target robot control instructions for each robot and sends them to the corresponding robot to drive the robot to move. If distributed control is used, the robots will each receive target path related data to calculate the target robot control instructions to perform their respective tasks. Please refer to Figure 6 , a robot control method in an embodiment of the present application is applied to each robot in a control system or a multi-robot formation, and the method includes:

[0107] 601. Obtaining an expected robot position of the robot according to the shape of the formation on the target path of the formation;

[0108] The expected robot position of the robot is obtained according to the shape of the formation on the target path of the formation, wherein the shape indicates the positions of multiple robots in the formation in the formation coordinate system. The target path is obtained by the control system in the above embodiment. If it is centralized control, the control system directly uses the target path. If it is distributed control, the control system needs to send the shape of the formation on the target path to each robot, and each robot obtains its own expected robot position, which is the position of the robot at each moment on the target path.

[0109] 602. Based on a preset instruction optimization algorithm, and on the premise of satisfying the robot motion function and the preset minimum spacing distance between the robots, a target robot control instruction is obtained that makes the actual robot position closest to the expected robot position, so that the robot moves based on the target robot control instruction.

[0110] Based on the preset instruction optimization algorithm, under the premise of satisfying the robot motion function and the preset minimum spacing distance between the robots, the target robot control instruction that makes the actual robot position closest to the expected robot position is obtained, so that the robot moves based on the target robot control instruction. Among them, the independent variables of the robot motion function include the current robot position and the current robot control instruction, the dependent variable includes the robot position at the next moment, and the robot motion function is obtained by the preset formation motion equation. The instruction optimization algorithm can be an ADMM algorithm, or a variant of the ADMM algorithm, or a distributed gradient descent, etc., which is not limited here. The preset minimum spacing distance between robots ensures that there will be no collision between robots, and the actual robot position is as close as possible to the expected robot position, which can make the actual motion trajectory of the formation close to the target path.

[0111] In the embodiment of the present application, the expected robot position of the robot is first obtained according to the formation shape on the target path of the formation, and then based on the preset instruction optimization algorithm, the target robot control instruction that makes the actual robot position closest to the expected robot position is obtained under the premise of satisfying the robot motion function and the preset minimum spacing distance between the robots, so that the robot moves based on the target robot control instruction. In the existing scheme, the formation shape is single and cannot be changed, and the robot behavior rules will cause the formation to be abnormal. This embodiment can be appropriately adjusted based on the target path of the formation, and can be constrained for each robot, which improves robustness and can ensure the consistency of the overall formation and no conflict between robots.

[0112] See also Figure 7 Another embodiment of a robot control method in an embodiment of the present application is applied to each robot in a control system or a multi-robot formation, and the method includes:

[0113] 701. Obtaining an expected robot position of the robot according to the shape of the formation on the target path of the formation;

[0114] The expected robot position of the robot is obtained according to the shape of the formation on the target path of the formation, wherein the shape indicates the positions of multiple robots in the formation in the formation coordinate system. The target path is obtained by the control system in the above embodiment. If it is centralized control, the control system directly uses the target path. If it is distributed control, the control system needs to send the shape of the formation on the target path to each robot, and each robot obtains its own expected robot position, which is the position of the robot at each moment on the target path.

[0115] 702. Based on a preset instruction optimization algorithm, and under the premise of satisfying the robot motion function and the preset minimum spacing distance between the robots, a target robot control instruction is obtained that makes the actual robot position closest to the expected robot position, so that the robot moves based on the target robot control instruction;

[0116] Based on the preset instruction optimization algorithm, under the premise of satisfying the robot motion function and the preset minimum spacing distance between the robots, the target robot control instruction that makes the actual robot position closest to the expected robot position is obtained, so that the robot moves based on the target robot control instruction. Among them, the independent variables of the robot motion function include the current robot position and the current robot control instruction, the dependent variable includes the robot position at the next moment, and the robot motion function is obtained by the preset formation motion equation.

[0117] Specifically, the formation motion equation is used to describe the motion trajectory of the formation, which is X(t+1)=R(t)*T(t)*X(t), where X(t+1) is the shape matrix at time t+1, R(t) is the rotation matrix at time t, T(t) is the translation matrix at time t, and X(t) is the shape matrix at time t. According to R(t) and T(t), combined with the shape matrix at the current moment, the shape matrix at the next moment can be obtained.

[0118] Among them, X(t) describes the formation shape at time t, and each row of the shape matrix corresponds to the coordinates of a robot. X(t) is a matrix with N rows and 2 columns, where N is the number of robots in the formation. Each row of the matrix corresponds to a robot and contains the x and y coordinate values ​​of the robot in the formation reference coordinate system. For example, assuming that a formation consists of 3 robots, at a certain time t, X(t) can be expressed as:

[0119]

[0120] Among them, the first line [x1(t), y1(t)] gives the relative position coordinates of the first robot at time t, the second line [x2(t), y2(t)] gives the coordinates of the second robot, and the third line [x3(t), y3(t)] gives the coordinates of the third robot.

[0121] T(t) describes the translational motion at time t, such as forward, backward, left, right, etc. The matrix representation is:

[0122]

[0123] Ti(t) is the 3x3 displacement matrix of the ith robot, in the form:

[0124]

[0125] Among them, T(t) is a large 3Nx3N diagonal matrix, and the diagonal is the displacement matrix Ti(t) of each robot. Ti(t) contains only two components, txi(t) and tyi(t), which represent the individual displacements of the i-th robot in the x and y directions.

[0126] R(t) describes the rotation of the formation, and its matrix form is:

[0127]

[0128] Ri(t) is the 3x3 rotation matrix of the ith robot, in the form:

[0129]

[0130] Among them, θi(t) represents the rotation angle of the ith robot at time t. R(t) is a large 3Nx3N diagonal matrix. The diagonal is the rotation moment Ri(t) of each robot, and each Ri(t) is a standard 2D rotation matrix. By setting θi(t), the ith robot can be controlled to rotate around itself. All non-diagonal elements are 0, indicating that the rotations of different robots are independent of each other.

[0131] The above formula can be substituted into the calculation to obtain the robot position of each robot at time t+1:

[0132] xi(t+1)=(xi(t)+txi(t))*cos(θi(t))-(yi(t)+tyi(t))*sin(θi(t));

[0133] yi(t+1)=(xi(t)+txi(t))*sin(θi(t))+(yi(t)+tyi(t))*cos(θi(t));

[0134] The above is an embodiment of the robot motion function, which is not limited here. The robot motion function accurately describes the new coordinates of each robot after rotation θi(t) and displacement (txi(t), tyi(t)). By controlling θi(t), txi(t) and tyi(t) at each moment, the motion trajectory and morphological changes of the robot formation can be fully determined.

[0135] In addition, the matrix can be replaced by a quaternion, a double matrix, etc., which is not limited here.

[0136] In order to avoid abnormal behavior or collision in the formation, it is necessary to set constraints and make the actual robot position as close to the expected robot position as possible. The constraint formula for the actual robot position to be as close to the expected robot position as possible is:

[0137] min||xi(t+1)-xi,desired(t+1)||; xi,desired(t+1) is the desired robot position, xi(t+1) is the actual robot position;

[0138] Robot motion function constraints:

[0139] xi(t+1)=fi(xi(t),ui(t)); fi is the robot motion function, where ui(t) is the control instruction, i.e., the rotation amount and / or translation amount;

[0140] Distance constraints between robots:

[0141] ||xi(t+1)-xj(t+1)||≥dmin, dmin is the minimum distance between robots;

[0142] Combined with the above formula, the target robot control instructions are obtained based on the instruction optimization algorithm.

[0143] 703. Obtaining correction instructions for the robot based on preset robot behavior rules;

[0144] The correction instructions of the robot are obtained based on the preset robot behavior rules. There are multiple preset robot behavior rules, which are not limited here. In one embodiment, the robot behavior rules may be obstacle avoidance behavior rules. When a single robot is too close to an obstacle in the environment, an obstacle avoidance correction vector, i.e., a correction instruction, is calculated according to the position of the obstacle and its own motion state. In another embodiment, the robot behavior rules may be formation maintenance rules, which obtain the positions of other formation members within the sensing range to autonomously adjust the movement and maintain the relative distance and angle with other members.

[0145] 704. Use the correction instruction to adjust the control instruction of the target robot.

[0146] The target robot control instructions are adjusted using the correction instructions. The correction instructions are added to the target robot control instructions to adjust the robot's motion.

[0147] In this embodiment, the target path based on the formation can be appropriately adjusted, and each robot can be constrained individually, which improves robustness and ensures the consistency of the overall formation and the absence of conflicts between robots. In this embodiment, the multi-robot formation is regarded as a system, and a mathematical model is established to describe it, which can accurately reflect the situation of the formation. In addition, drawing on the idea of ​​biological swarm intelligence, each robot is given a set of simple behavior rules to allow the robot to actively avoid obstacles and avoid collisions, which greatly improves the adaptability of the system. The high autonomy, robustness and flexibility of the formation behavior can meet the requirements of multiple tasks.

[0148] The following is a detailed description of a multi-robot formation path determination device in an embodiment of the present application. Figure 8 Another embodiment of a multi-robot formation path determination device according to an embodiment of the present application includes:

[0149] An acquisition unit 801 is used to acquire an initial position and an initial form of a formation, and acquire a target position and a target form of the formation, wherein the form indicates positions of multiple robots in the formation in a formation coordinate system;

[0150] A generating unit 802 is configured to generate at least one sample position of the formation and a form at the sample position according to the initial position, the target position and the target form based on a preset sample generating algorithm;

[0151] The generating unit 802 is further configured to generate a path segment between two positions that meet a first preset condition among all positions based on the form of the formation at all positions, so as to generate a path tree consisting of a plurality of the path segments;

[0152] The analyzing unit 803 is configured to find a target path from the initial position to the target position that meets a second preset condition from the path tree based on a preset path planning algorithm.

[0153] In the embodiment of the present application, the acquisition unit 801 first acquires the initial position and initial form of the formation, and acquires the target position and target form of the formation. The generation unit 802 then generates at least one sample position and the form at the sample position of the formation based on the initial position, target position and target form based on the preset sample generation algorithm. Then, based on the form of the formation at all positions, a path segment is generated between two positions that meet the first preset condition in all positions to generate a path tree composed of multiple path segments. Finally, the analysis unit 803 finds the target path from the initial position to the target position that meets the second preset condition from the path tree based on the preset path planning algorithm. The target path that does not collide with obstacles is automatically planned by the position and form of multiple robots, without manual planning, with a high degree of automation and high planning efficiency, which can meet the needs of the task.

[0154] Another embodiment of a multi-robot formation path determination device according to an embodiment of the present application includes:

[0155] An acquisition unit, used to acquire an initial position and an initial form of the formation, and acquire a target position and a target form of the formation, wherein the form indicates the positions of multiple robots in the formation in a formation coordinate system;

[0156] a generating unit, configured to generate at least one sample position of the formation and a form at the sample position according to the initial position, the target position and the target form based on a preset sample generating algorithm;

[0157] The generating unit is further configured to generate a path segment between two positions that meet a first preset condition among all positions based on the form of the formation at all positions, so as to generate a path tree consisting of a plurality of the path segments;

[0158] An analysis unit is used to find a target path from the initial position to the target position that meets a second preset condition from the path tree based on a preset path planning algorithm.

[0159] Generate a unit, specifically for:

[0160] Taking the initial position as a reference position;

[0161] Randomly generating a sample position of the formation that does not overlap with an obstacle at a preset step length from the reference position, and a straight line segment between the sample position and the reference position does not pass through an obstacle;

[0162] The sample position is used as a new reference position, and the first designation step is returned to execute until a distance between a sample position and the target position is less than the preset step length, and then the generation of sample positions is stopped, wherein the first designation step is to randomly generate a sample position of the formation that does not overlap with an obstacle at a preset step length from the reference position, and a straight line segment between the sample position and the reference position does not pass through an obstacle;

[0163] Randomly generate a form of the formation in which the positions of the robots in the formation do not overlap with obstacles at overlapping sample positions, and determine that the form of the formation at non-overlapping sample positions is the target form, wherein the overlapping sample positions are sample positions in which the positions of the robots in the formation overlap with obstacles when the formation is in the target form, and the non-overlapping sample positions are sample positions in which the positions of the robots in the formation do not overlap with obstacles when the formation is in the target form;

[0164] or,

[0165] Based on a fast search random tree algorithm or a fast search random tree star algorithm, generating the sample position according to the initial position and the target position;

[0166] The form of the formation in which the positions of the robots in the formation do not overlap with obstacles is randomly generated at the sample positions.

[0167] Generates units, also used to:

[0168] Selecting two positions from all positions of the formation as detection positions;

[0169] Selecting a first robot position that has not been detected in the formation at the first detection position, and selecting a second robot position that has not been detected in the formation at the second detection position;

[0170] Determining whether there is a collision-free trajectory between the first robot position and the second robot position;

[0171] If so, generating a collision-free robot trajectory between the first robot position and the second robot position;

[0172] Determining whether the number of robot trajectories is consistent with the number of robots in the formation;

[0173] If they are consistent, a path segment is generated between the first detection position and the second detection position, and the second specified step is returned to execute until all positions of the formation have been selected, wherein the second specified step is to select two positions from all positions of the formation as detection positions;

[0174] If they are inconsistent, returning to execute the third designation step, the third designation step is to select a first robot position that has not been detected in the formation at the first detection position, and to select a second robot position that has not been detected in the formation at the second detection position;

[0175] If it does not exist, then select any undetected robot position in the formation at the first detection position as the new first robot position, and return to execute the fourth specified step until all robot positions at the first detection position fail to generate a collision-free robot trajectory with the second robot position, then return to execute the second specified step, wherein the fourth specified step is to determine whether there is a collision-free trajectory between the first robot position and the second robot position.

[0176] The analysis unit is specifically used for:

[0177] Based on a preset path planning algorithm, a target path from the initial position to the target position with the shortest time or the minimum energy consumption is found from the path tree.

[0178] The functions and processes performed by each unit in the multi-robot formation path determination device of this embodiment are similar to those of the aforementioned Figures 1 to 5 The functions and processes performed by the multi-robot formation path determination device are similar and will not be repeated here.

[0179] Fig. 9 It is a structural schematic diagram of a multi-robot formation path determination device provided in an embodiment of the present application. The multi-robot formation path determination device 900 may include one or more central processing units (CPU) 901 and a memory 905, and the memory 905 stores one or more application programs or data.

[0180] The memory 905 may be a volatile storage or a persistent storage. The program stored in the memory 905 may include one or more modules, each of which may include a series of instruction operations in the multi-robot formation path determination device 900. Furthermore, the central processor 901 may be configured to communicate with the memory 905, and execute a series of instruction operations in the memory 905 on the multi-robot formation path determination device 900.

[0181] The multi-robot formation path determination device 900 may also include one or more power supplies 902, one or more wired or wireless network interfaces 903, one or more input and output interfaces 504, and / or one or more operating systems, such as Windows ServerTM, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, etc.

[0182] The CPU 901 can execute the aforementioned Figures 1 to 5 The operations performed by the multi-robot formation path determination device in the illustrated embodiment will not be described in detail here.

[0183] The robot control device of the present application is described below. Fig.10 , an embodiment of a robot control device of the present application includes:

[0184] A processing unit 1001 is used to obtain an expected robot position of the robot according to a form of the formation on a target path of the formation, wherein the form indicates positions of multiple robots in the formation in a formation coordinate system;

[0185] The control unit 1002 is used to obtain a target robot control instruction that makes the actual robot position closest to the expected robot position based on a preset instruction optimization algorithm, on the premise of satisfying the robot motion function and the preset minimum spacing distance between the robots, so that the robot moves based on the target robot control instruction, wherein the independent variables of the robot motion function include the current robot position and the current robot control instruction, and the dependent variable includes the robot position at the next moment, and the robot motion function is obtained by a preset formation motion equation.

[0186] In the embodiment of the present application, the processing unit 001 first obtains the expected robot position of the robot according to the formation shape on the target path of the formation, and the control unit 1002 then obtains the target robot control instruction that makes the actual robot position closest to the expected robot position based on the preset instruction optimization algorithm, under the premise of satisfying the robot motion function and the preset minimum spacing distance between the robots, so that the robot moves based on the target robot control instruction. In the existing scheme, the formation shape is single and cannot be changed, and the robot behavior rules will cause the formation to be abnormal. This embodiment can be appropriately adjusted based on the target path of the formation, and can be constrained for each robot, which improves robustness and can ensure the consistency of the overall formation and no conflict between robots.

[0187] Another embodiment of a robot control device of the present application includes:

[0188] a processing unit, configured to obtain a desired robot position of the robot according to a form of the formation on a target path of the formation, the form indicating positions of a plurality of robots in the formation in a formation coordinate system;

[0189] A control unit is used to obtain a target robot control instruction that makes the actual robot position closest to the expected robot position based on a preset instruction optimization algorithm, on the premise of satisfying the robot motion function and the preset minimum spacing distance between the robots, so that the robot moves based on the target robot control instruction, wherein the independent variables of the robot motion function include the current robot position and the current robot control instruction, and the dependent variable includes the robot position at the next moment, and the robot motion function is obtained by a preset formation motion equation.

[0190] The robot control device also includes a regulating unit, which is specifically used to:

[0191] Obtaining correction instructions for the robot based on preset robot behavior rules;

[0192] The target robot control instruction is adjusted using the correction instruction.

[0193] The functions and processes performed by each unit in the robot control device of this embodiment are similar to those described above. Figure 6 to Figure 7 The functions and processes performed by the robot control device are similar and will not be repeated here.

[0194] Fig.11It is a schematic diagram of the structure of a robot control device provided in an embodiment of the present application. The robot control device 1100 may include one or more central processing units (CPU) 1101 and a memory 1105. The memory 1105 stores one or more application programs or data.

[0195] The memory 1105 may be a volatile storage or a persistent storage. The program stored in the memory 1105 may include one or more modules, each of which may include a series of instruction operations in the robot control device 1100. Furthermore, the central processing unit 1101 may be configured to communicate with the memory 1105 and execute a series of instruction operations in the memory 1105 on the robot control device 1100.

[0196] The robot control device 1100 may also include one or more power supplies 1102, one or more wired or wireless network interfaces 1103, one or more input and output interfaces 1104, and / or one or more operating systems, such as Windows ServerTM, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, etc.

[0197] The CPU 1101 can execute the aforementioned Figure 6 to Figure 7 The operations performed by the robot control device in the illustrated embodiment will not be described in detail here.

[0198] An embodiment of the present application also provides a computer-readable storage medium, including instructions, which, when executed on a computer, enable the computer to execute the method in the aforementioned embodiment.

[0199] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0200] It should be noted that, although the steps in the flowcharts involved in the embodiments are drawn in sequence according to the instructions of the arrows, unless otherwise clearly stated in this document, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0201] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0202] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0203] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0204] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, read-only memory), random access memory (RAM, random access memory), disk or optical disk and other media that can store program code.

Claims

1. A multi-robot formation path determination method, characterized in that: Applied to a control system, the method comprises: Acquire an initial position and an initial form of the formation, and acquire a target position and a target form of the formation, wherein the form indicates positions of multiple robots in the formation in a formation coordinate system; Based on a preset sample generation algorithm, generating at least one sample position of the formation and a form at the sample position according to the initial position, the target position and the target form; Based on the form of the formation at all positions, generating a path segment between two positions that meet a first preset condition among all positions, so as to generate a path tree consisting of a plurality of the path segments; Based on a preset path planning algorithm, a target path from the initial position to the target position that meets a second preset condition is found from the path tree.

2. The multi-robot formation path determination method according to claim 1, characterized in that: The method of generating at least one sample position of the formation and the form at the sample position based on the preset sample generation algorithm according to the initial position, the target position and the target form includes: Taking the initial position as a reference position; Randomly generating a sample position of the formation that does not overlap with an obstacle at a preset step length from the reference position, and a straight line segment between the sample position and the reference position does not pass through an obstacle; The sample position is used as a new reference position, and the first designation step is returned to execute until a distance between a sample position and the target position is less than the preset step length, and then the generation of sample positions is stopped, wherein the first designation step is to randomly generate a sample position of the formation that does not overlap with an obstacle at a preset step length from the reference position, and a straight line segment between the sample position and the reference position does not pass through an obstacle; Randomly generate a form of the formation in which the positions of the robots in the formation do not overlap with obstacles at overlapping sample positions, and determine that the form of the formation at non-overlapping sample positions is the target form, wherein the overlapping sample positions are sample positions in which the positions of the robots in the formation overlap with obstacles when the formation is in the target form, and the non-overlapping sample positions are sample positions in which the positions of the robots in the formation do not overlap with obstacles when the formation is in the target form; or, Based on a fast search random tree algorithm or a fast search random tree star algorithm, generating the sample position according to the initial position and the target position; The form of the formation in which the positions of the robots in the formation do not overlap with obstacles is randomly generated at the sample positions.

3. The multi-robot formation path determination method according to claim 1, characterized in that: The generating of a path segment between two positions that meet a first preset condition among all positions based on the form of the formation at all positions includes: Selecting two positions from all positions of the formation as detection positions; Selecting a first robot position that has not been detected in the formation at the first detection position, and selecting a second robot position that has not been detected in the formation at the second detection position; Determining whether there is a collision-free trajectory between the first robot position and the second robot position; If so, generating a collision-free robot trajectory between the first robot position and the second robot position; Determining whether the number of robot trajectories is consistent with the number of robots in the formation; If they are consistent, a path segment is generated between the first detection position and the second detection position, and the second specified step is returned to execute until all positions of the formation have been selected, wherein the second specified step is to select two positions from all positions of the formation as detection positions; If they are inconsistent, returning to execute the third designation step, the third designation step is to select a first robot position that has not been detected in the formation at the first detection position, and to select a second robot position that has not been detected in the formation at the second detection position; If it does not exist, then select any undetected robot position in the formation at the first detection position as the new first robot position, and return to execute the fourth specified step until all robot positions at the first detection position fail to generate a collision-free robot trajectory with the second robot position, then return to execute the second specified step, wherein the fourth specified step is to determine whether there is a collision-free trajectory between the first robot position and the second robot position.

4. The multi-robot formation path determination method according to claim 1, characterized in that: The step of finding a target path from the initial position to the target position that satisfies a second preset condition from the path tree based on a preset path planning algorithm includes: Based on a preset path planning algorithm, a target path from the initial position to the target position with the shortest time or the minimum energy consumption is found from the path tree.

5. A robot control method, characterized in that: Applied to each robot in a control system or a multi-robot formation, the method comprises: obtaining an expected robot position of the robot according to a form of the formation on a target path of the formation, the form indicating positions of a plurality of robots in the formation in a formation coordinate system; Based on a preset instruction optimization algorithm, and on the premise of satisfying the robot motion function and the preset minimum spacing distance between the robots, a target robot control instruction is obtained that makes the actual robot position closest to the expected robot position, so that the robot moves based on the target robot control instruction, wherein the independent variables of the robot motion function include the current robot position and the current robot control instruction, and the dependent variable includes the robot position at the next moment, and the robot motion function is obtained by a preset formation motion equation.

6. The robot control method according to claim 5, characterized in that: After obtaining the target robot control instruction that makes the actual robot position closest to the expected robot position based on the preset instruction optimization algorithm and under the premise of satisfying the robot motion function and the preset minimum spacing distance between the robots, the method further includes: Obtaining correction instructions for the robot based on preset robot behavior rules; The target robot control instruction is adjusted using the correction instruction.

7. A multi-robot formation path determination device, characterized in that: include: An acquisition unit, used to acquire an initial position and an initial form of the formation, and acquire a target position and a target form of the formation, wherein the form indicates the positions of multiple robots in the formation in a formation coordinate system; a generating unit, configured to generate at least one sample position of the formation and a form at the sample position according to the initial position, the target position and the target form based on a preset sample generating algorithm; The generating unit is further configured to generate a path segment between two positions that meet a first preset condition among all positions based on the form of the formation at all positions, so as to generate a path tree consisting of a plurality of the path segments; An analysis unit is used to find a target path from the initial position to the target position that meets a second preset condition from the path tree based on a preset path planning algorithm.

8. A robot control device, characterized in that: include: a processing unit, configured to obtain a desired robot position of the robot according to a form of the formation on a target path of the formation, the form indicating positions of a plurality of robots in the formation in a formation coordinate system; A control unit is used to obtain a target robot control instruction that makes the actual robot position closest to the expected robot position based on a preset instruction optimization algorithm, on the premise of satisfying the robot motion function and the preset minimum spacing distance between the robots, so that the robot moves based on the target robot control instruction, wherein the independent variables of the robot motion function include the current robot position and the current robot control instruction, and the dependent variable includes the robot position at the next moment, and the robot motion function is obtained by a preset formation motion equation.

9. A multi-robot formation path determination device, characterized in that: include: CPU, memory and input / output interface; The memory is a short-term storage memory or a persistent storage memory; The central processing unit is configured to communicate with the memory and execute instructions in the memory to perform the method according to any one of claims 1 to 4.

10. A robot control device, characterized in that: include: CPU, memory and input / output interface; The memory is a short-term storage memory or a persistent storage memory; The central processing unit is configured to communicate with the memory and execute instructions in the memory to perform the method according to any one of claims 5 to 6.

11. A computer-readable storage medium, characterized in that: The method comprises instructions, which, when executed on a computer, cause the computer to execute the method according to any one of claims 1 to 6.

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