Autonomous rapid aggregation behavior emergence control method for swarm robots

Abstract

The invention discloses a swarm robot autonomous rapid gathering behavior emergence control method, which comprises the following steps: step 1, controlling robots to move until the robots encounter interaction information states of other robots; 2, judging whether set conditions are met or not, if yes, moving to the target area, if not, judging whether the position of the target area is known or not, if yes, returning to the step 1, and if not, performing formation with other robots; 3, carrying out the movement based on the formation condition, and updating the information state and the interaction information state in real time in the movement process; 4, judging whether set conditions are met or not, if yes, separating from the formation and moving to the target area, if not, judging whether the position of the target area is known or not, if yes, separating from the formation and returning to the step 1, and if not, returning to the step 3; wherein decision control logic of the robot is constructed by using the behavior tree. According to the method, the whole group can be quickly gathered to a specific target area, and the success rate and efficiency of gathering behaviors are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of cluster control, and particularly relates to a method for emergent control of autonomous and rapid aggregation behavior of swarm robots. Background Art

[0002] The aggregation behavior of swarm robots refers to a collective formed by multiple robots working or acting together cooperatively. Through autonomous decision-making, the robots approach each other, enabling communication between the robots within the swarm in a direct or relay form, which is the basis for the subsequent actions of the swarm.

[0003] Aggregation behavior is one of the most basic group behaviors in which swarm intelligence emergence can be observed in nature. Essentially, it is a problem of exploring unknown space based on local perception. Each agent within the swarm only relies on limited local information to explore the unknown space and avoid obstacles, and finally makes the swarm gather in a small area, enabling communication between any two agents. The complete aggregation movement of the swarm always depends on the limited and local worldviews of individuals.

[0004] The traditional spontaneous aggregation process of swarm robots is divided into two cases. One is to gather at a specific area (specified spot), and the other is to gather at an unspecified area (unspecified spot). The traditional method is to adopt a random movement method, and when the agent randomly moves to the gathering area, it stops. However, this method of using random movement has a low success rate and efficiency of the aggregation behavior in practical applications. Summary of the Invention

[0005] To solve some or all of the technical problems existing in the above-mentioned prior art, the present invention provides a method for emergent control of autonomous and rapid aggregation behavior of swarm robots.

[0006] The technical solution of the present invention is as follows:

[0007] A method for emergent control of autonomous and rapid aggregation behavior of swarm robots is provided. The method is used for controlling a single robot in the swarm robots and includes the following steps:

[0008] Step 1, control the robot to move in a preset random movement mode and update its own information state in real time until it encounters other robots, interact with the other robots for information state, and record the IDs and quantities of the robots whose positions of known target areas are known;

[0009] Step 2: Determine whether the set conditions are met. If so, move towards the known target area. If not, further determine whether the position of the target area is known. If so, return to Step 1 to continue the random movement. If not, form a formation with other robots, obtain the team role, and establish and update the formation information status.

[0010] Step 3: Move based on the team role and formation situation, and in the process of movement, update the own information status in real time, interact with the information status of other robots in the formation in real time, update the formation information status in real time, and interact with the information status of other robots not in the formation encountered.

[0011] Step 4: Determine whether the set conditions are met. If so, break away from the formation and move towards the known target area. If not, further determine whether the position of the target area is known. If so, break away from the formation and return to Step 1 to continue the random movement. If not, return to Step 3 to continue the movement.

[0012] Among them, based on the above steps, a behavior tree is used to construct the decision control logic of the robot.

[0013] In some optional implementation manners, the preset random movement manner includes:

[0014] At each moment, use the coordinate position calculation formula to determine the coordinate position of the robot at the next moment, and control the robot to move to the determined coordinate position.

[0015] The coordinate position calculation formula is:

[0016]

[0017] Where, (x t ,y t ) represents the coordinate position of the robot in the global coordinate system at time t, (x t-1 ,y t-1 ) represents the coordinate position of the robot in the global coordinate system at time t-1, m rand and n rand represent random numbers within [-40, 40] that follow a normal distribution, v x represents the absolute velocity in the x direction based on the robot's body coordinate system, v y represents the absolute velocity in the y direction based on the robot's body coordinate system, and dt represents the time interval between time t and time t-1.

[0018] In some alternative embodiments, the self - information status of the robot includes: its own ID, the distance to adjacent robots, its absolute position in the activity space, its absolute angle, the absolute position of the target area, its running time, the relative distance to the target area, the position of adjacent robots relative to itself, the angle of adjacent robots relative to itself, and the trajectory it has traveled.

[0019] In some alternative embodiments, the formation - information status includes: the IDs of the robots currently within its communication and perception range, the IDs of the robots that have encountered all known target - area positions among the robots currently within its communication and perception range, the team - role flag bit in the formation, the IDs of other robots detected within the same formation, the absolute velocity vectors of the other detected robots obtained through communication, and the absolute positions of the other detected robots obtained through communication.

[0020] In some alternative embodiments, the set conditions are as follows:

[0021] The running time of the robot itself is greater than a preset time threshold, and the number of robots with recorded known target - area positions is greater than a preset quantity threshold.

[0022] In some alternative embodiments, the team roles include: leader and follower.

[0023] In some alternative embodiments, the movement based on the team role and formation situation includes:

[0024] If the team role is the leader, at each moment, use the coordinate - position calculation formula to determine the coordinate position of the robot at the next moment, and control the robot to move to the determined coordinate position;

[0025] If the team role is the follower, at each moment, move following the leader in the formation based on the cohesion rule;

[0026] The coordinate - position calculation formula is as follows:

[0027]

[0028] Where, (x t , y t ) represents the coordinate position of the robot in the global coordinate system at time t, (x t-1 , y t-1 ) represents the coordinate position of the robot in the global coordinate system at time t - 1, m rand and n rand represent random numbers within [- 40, 40] that follow a normal distribution, v x represents the absolute velocity in the x - direction based on the robot's body coordinate system, vy represents the absolute velocity in the y - direction based on the robot body coordinate system, and dt represents the time interval between time t and time t - 1.

[0029] The main advantages of the technical solution of the present invention are as follows:

[0030] The method for emergent control of autonomous and rapid aggregation behavior of swarm robots in the present invention constructs the decision - making control logic of robots by using a behavior tree, enabling each robot in the swarm to perform motion control in a preset manner according to the set control logic, so that the entire swarm can quickly aggregate towards a specific target area, improving the success rate and efficiency of the aggregation behavior; moreover, in the process of motion control, by combining the autonomous motion and formation motion of robots, the success rate and efficiency of the aggregation behavior can be further improved; in addition, by using a behavior tree to construct the decision - making control logic of robots, the modularization of the decision - making control logic can be realized, and the interpretability of the decision - making control logic can be achieved, which is convenient for subsequent adjustment and expansion. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The drawings described herein are used to provide a further understanding of the embodiments of the present invention and constitute a part of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0032] Figure 1 is a flowchart of a method for emergent control of autonomous and rapid aggregation behavior of swarm robots provided by an embodiment of the present invention;

[0033] Figure 2 is a schematic diagram of an aggregation scenario of swarm robots provided by an embodiment of the present invention;

[0034] Figure 3 is a schematic diagram of a sub - tree of a RandomWalk behavior tree provided by an embodiment of the present invention;

[0035] Figure 4 is a schematic diagram of a sub - tree of an ORCA behavior tree provided by an embodiment of the present invention;

[0036] Figure 5 is a schematic diagram of a sub - tree of a GroupRandomWalk behavior tree provided by an embodiment of the present invention;

[0037] Figure 6 is a schematic diagram of a sub - tree of a MoveTarget behavior tree provided by an embodiment of the present invention;

[0038] Figure 7 is a schematic diagram of a behavior tree of a decision - making control strategy of a robot provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0039] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0040] The technical solutions provided by the embodiments of the present invention will be described in detail below with reference to the drawings.

[0041] See Figure 1 , the embodiments of the present invention provide a method for controlling the emergence of autonomous rapid aggregation behavior of swarm robots. This method is used to control a single robot in the swarm robots and includes the following steps:

[0042] Step 1: Control the robot to move in a preset random motion mode and update its own information state in real time until it encounters other robots, interact with other robots to exchange information states, and record the IDs and quantities of the robots whose known target area positions are known.

[0043] Step 2: Judge whether the set conditions are met. If so, move towards the known target area. If not, further judge whether the position of the target area is known. If so, return to Step 1 to continue the random motion. If not, form a formation with other robots, obtain the team role, and establish and update the formation information state.

[0044] Step 3: Move based on the team role and formation situation, and in the process of movement, update its own information state in real time, interact with other robots in the formation to exchange information states in real time, update the formation information state in real time, and interact with other robots not in the formation encountered.

[0045] Step 4: Judge whether the set conditions are met. If so, break away from the formation and move towards the known target area. If not, further judge whether the position of the target area is known. If so, break away from the formation and return to Step 1 to continue the random motion. If not, return to Step 3 to continue the movement.

[0046] In the embodiments of the present invention, based on the above-set steps, a behavior tree is used to construct the decision control logic of the robot, and the aggregation behavior of each robot in the swarm is controlled based on the constructed behavior tree, so as to make the swarm achieve the emergence control effect.

[0047] Refer to Figure 2 , Figure 2A schematic diagram of the aggregation scenario of a swarm of robots provided by an embodiment of the present invention. In the embodiment of the present invention, each robot in the swarm cannot perceive the positions of other robots at the initial stage. When the robots enter each other's perception range, the robots can communicate with each other and obtain the information states of the robots within the communication range.

[0048] Furthermore, in the embodiment of the present invention, the preset random motion mode includes:

[0049] At each moment, use the coordinate position calculation formula to determine the coordinate position of the robot at the next moment, and control the robot to move to the determined coordinate position;

[0050] The coordinate position calculation formula is:

[0051]

[0052] where, (x t , y t ) represents the coordinate position of the robot in the global coordinate system at time t, (x t-1 , y t-1 ) represents the coordinate position of the robot in the global coordinate system at time t - 1, m rand and n rand represent random numbers within [-40, 40] that follow a normal distribution, v x represents the absolute velocity in the x direction based on the robot's body coordinate system, v y represents the absolute velocity in the y direction based on the robot's body coordinate system, and dt represents the time interval between time t and time t - 1.

[0053] The aggregation behavior of a swarm is a very typical behavior of the swarm, which is the research basis for other emergent behaviors of the swarm. The main goal of the aggregation behavior of the swarm is to enable each agent in the swarm to quickly and efficiently gather together, so that the agents can maintain communication connections directly or indirectly, which is the distributed working basis for a multi-agent swarm. However, due to the strong interaction between the agents in the swarm, it is usually difficult to construct a unified mathematical model. Therefore, the emergence of the swarm's behavior is a complex system problem. For this reason, in the embodiment of the present invention, a behavior tree is introduced, and the decision control logic of the robot is constructed using the behavior tree, which can realize the modularization of the decision control logic and improve the interpretability of the decision control logic. By controlling the aggregation behavior of each robot in the swarm based on the constructed behavior tree, the control effect of swarm aggregation emergence can be achieved.

[0054] The Behavior Tree (BT) is a method for implementing the switching of different tasks in autonomous agents. In form, it is a directed rooted tree, and the task switching is achieved by the internal nodes of the tree, enabling the desired behaviors to be described using modular leaf nodes.

[0055] Referring to Table 1, the node types of the behavior tree include internal nodes and leaf nodes. The internal nodes are also called control nodes, including: selector nodes, sequence nodes, parallel nodes, and decorator nodes. Different control nodes have corresponding ways of returning status. Among them, in Table 1, M represents a preset threshold, and N represents all the child nodes under the parallel node. The leaf nodes are also called execution nodes, including: action nodes and condition nodes. The condition nodes record the execution conditions, and the action nodes record the execution actions. Different execution nodes have corresponding ways of returning status.

[0056] When the behavior tree is executed, it starts from the root node and passes to the control nodes. The control nodes execute the child nodes sequentially from left to right. The child nodes return a status to the upper-level control node according to the execution status of the current action. The status can be, for example, Success, Failure, or Processing. The control node executes the next node to be executed according to the status it returns. The behavior tree is executed at a fixed frequency during the execution process.

[0057] Table 1 (Node Types of the Behavior Tree)

[0058]

[0059]

[0060] Furthermore, in order to be able to use the behavior tree to construct the decision-making control logic of the robot corresponding to the above steps 1 - 4, in the embodiments of the present invention, basic action nodes as shown in Table 2 and condition nodes as shown in Table 3 are defined.

[0061] Table 2 Basic Action Nodes of the Behavior Tree

[0062]

[0063] Table 3 Condition Nodes of the Behavior Tree

[0064]

[0065]

[0066] It should be noted that the above-mentioned agents represent robots in the group.

[0067] Further, in order to facilitate the construction of a behavior tree for describing the decision control logic of a robot, in the embodiments of the present invention, based on the above-defined basic action nodes and condition nodes, a plurality of behavior tree subtrees as shown in Table 4 are also defined.

[0068] Table 4 Behavior Tree Subtrees

[0069]

[0070] Reference Figures 3 - 6 , Figure 3 is a schematic diagram of a RandomWalk behavior tree subtree provided by an embodiment of the present invention. Figure 4 is a schematic diagram of an ORCA behavior tree subtree provided by an embodiment of the present invention. Figure 5 is a schematic diagram of a GroupRandomWalk behavior tree subtree provided by an embodiment of the present invention. Figure 6 is a schematic diagram of a MoveTarget behavior tree subtree provided by an embodiment of the present invention. Using the behavior tree subtrees shown in the above attachments Figures 3 - 6 can implement the functions to be achieved by different behavior tree subtrees described in Table 4 above.

[0071] It should be noted that the above-mentioned agent represents a robot in a group.

[0072] It should be noted that in the attachments Figure 3 "PROB" represents "Probability", that is, the meaning of probability, which is used to define the execution probability of a certain branch or behavior. When the behavior tree reaches a node with a PROB parameter, it is determined whether to continue executing along this branch according to the probability value of this node. Among them, the specific probability value is set according to actual needs.

[0073] It should be noted that in the attachments Figures 3 - 7 MF represents MoveForward, MB represents MoveBackward, TL represents RotateLeft, and TR represents RotateRight.

[0074] Further, in the embodiments of the present invention, the self-information state of the robot includes: its own ID, the distance from adjacent robots, its absolute position in the activity space, its absolute angle, the absolute position of the target area, its running time, the relative distance from the target area, the position of adjacent robots relative to itself, the angle of adjacent robots relative to itself, and the trajectory it has traveled.

[0075] The formation information status includes: the IDs of the robots currently within its own communication and perception range, the IDs of the robots that have encountered all known target area positions among the robots currently within its own communication and perception range, the team role flag bits in the formation, the IDs of other robots detected within the same formation, the absolute velocity vectors of other detected robots obtained through communication, and the absolute positions of other detected robots obtained through communication.

[0076] Furthermore, based on the self - information status and formation information status defined above, the self - information status space shown in Table 5 and the formation information status space shown in Table 6 are defined.

[0077] Table 5 Self - information status space

[0078] Self-Information Status Description Self-ID Self ID Number Dist to Neighbor Distance to Adjacent Agent Self-Position(Abs) Absolute Position of Self in the Activity Space Self-Angular(Abs) Absolute Angle of Self Goal-Position(Abs) Absolute Position of the Target Area Running-Time Running Time of Self Dist to Goal Relative Distance to the Target Area Neighbor-Position(Rel) Position of Adjacent Agent Relative to Self Neighbor-Angular(Rel) Angle of Adjacent Agent Relative to Self Self-Track(Abs) Record of the Trajectory Traveled by Self

[0079] Table 6 Formation information status space

[0080]

[0081]

[0082] Furthermore, in the embodiment of the present invention, the set conditions in Step 2 and Step 4 are:

[0083] The running time of the robot itself is greater than a preset time threshold, and the number of robots with recorded known target area positions is greater than a preset number threshold.

[0084] In the embodiment of the present invention, the preset time threshold and the preset number threshold are specifically set according to the actual situation.

[0085] Furthermore, in the embodiment of the present invention, based on the above - set team roles including leaders and followers, in Step 3 above, moving based on the team role and formation situation further includes:

[0086] If the team role is a leader, at each moment, use the coordinate position calculation formula to determine the coordinate position of the robot at the next moment, and control the robot to move to the determined coordinate position;

[0087] If the team role is a follower, at each moment, move following the leader in the formation based on the cohesion rule;

[0088] The coordinate position calculation formula is:

[0089]

[0090] where, (x t ,y t) represents the coordinate position of the robot in the global coordinate system at time t, (x t-1 , y t-1 ) represents the coordinate position of the robot in the global coordinate system at time t-1, m rand and n rand represent random numbers within [-40, 40] that follow a normal distribution, v x represents the absolute velocity in the x direction based on the robot's body coordinate system, v y represents the absolute velocity in the y direction based on the robot's body coordinate system, and dt represents the time interval between time t and time t-1.

[0091] Among them, when moving following the leader in the formation based on the cohesion rule, the follower calculates the centroid of the group and generates a velocity pointing to the centroid, and performs the following movement based on the generated velocity.

[0092] Furthermore, in the embodiments of the present invention, based on each step defined by the above control method and each definition for the behavior tree, a decision control strategy behavior tree of the robot as shown in Figure 7 is constructed to be used for the control of the aggregation behavior of the robot.

[0093] The emergent control method for the autonomous and rapid aggregation behavior of the swarm robots provided by the embodiments of the present invention constructs the decision control logic of the robot by using the behavior tree, enabling each robot in the swarm to perform motion control in a preset manner according to the set control logic, which can enable the entire swarm to rapidly aggregate towards a specific target area, improving the success rate and efficiency of the aggregation behavior; and, in the process of motion control, by adopting the combination of the autonomous motion of the robot and the formation motion, the success rate and efficiency of the aggregation behavior can be further improved; in addition, by using the behavior tree to construct the decision control logic of the robot, the modularization of the decision control logic can be realized, and the interpretability of the decision control logic can be achieved, which is convenient for subsequent adjustment and expansion.

[0094] It should be noted that in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. In addition, in this article, "front", "rear", "left", "right", "upper", and "lower" are all referenced with respect to the placement state shown in the drawings.

[0095] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than limiting it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for controlling the emergence of autonomous rapid aggregation behavior of swarm robots, characterized in that: The method is used for controlling a single robot in a group of robots, and comprises the following steps: Step 1: Control the robot to move in a preset random motion mode and update its own information status in real time until it encounters other robots, exchanges information status with other robots, and records the ID and number of robots with known target area locations; Step 2, determine whether the set conditions are met. If so, move to the known target area. If not, further determine whether the location of the target area is known. If so, return to step 1 to continue random movement. If not, form a team with other robots, obtain the team role, and establish and update the formation information status; Step 3: Move based on the team role and formation situation, and update its own information status in real time during the movement, interact with other robots in the formation in real time, update the formation information status in real time, and interact with other robots not in the formation encountered; Step 4, determine whether the set conditions are met, if so, leave the formation and move to the known target area, if not, further determine whether the location of the target area is known, if so, leave the formation and return to step 1 to continue random movement, if not, return to step 3 to continue movement; Based on the above steps, a behavior tree is used to build the robot's decision control logic.

2. The method for controlling the emergence of autonomous rapid aggregation behavior of swarm robots according to claim 1, characterized in that: The preset random motion mode includes: At each moment, the coordinate position calculation formula is used to determine the coordinate position of the robot at the next moment, and the robot is controlled to move to the determined coordinate position; The coordinate position calculation formula is: Among them, (x t ,y t ) represents the coordinate position of the robot in the global coordinate system at time t, (x t-1 ,y t-1 ) represents the coordinate position of the robot in the global coordinate system at time t-1, m rand and n rand represents a random number within [-40,40] that follows a normal distribution, v x Indicates the absolute speed in the x direction based on the robot body coordinate system, v y It represents the absolute speed in the y direction based on the robot body coordinate system, and dt represents the time interval between time t and time t-1.

3. The method for controlling the emergence of autonomous rapid aggregation behavior of swarm robots according to claim 1, characterized in that: The robot's own information status includes: its own ID, distance from adjacent robots, its absolute position in the activity space, its absolute angle, absolute position of the target area, its running time, relative distance from the target area, position of adjacent robots relative to itself, angle of adjacent robots relative to itself, and the trajectory it has traveled.

4. The method for controlling the emergence of autonomous rapid aggregation behavior of swarm robots according to claim 3, characterized in that: The formation information status includes: the ID of the robot currently within its own communication perception range, the IDs of all robots in the known target area encountered by the robot currently within its own communication perception range, the team role flag in the formation, the IDs of other robots detected in the same formation, the absolute velocity vectors of other robots detected through communication, and the absolute positions of other robots detected through communication.

5. The method for controlling the emergence of autonomous rapid aggregation behavior of swarm robots according to claim 4, characterized in that: The setting conditions are: The robot's own running time is greater than a preset time threshold, and the number of robots with known target area positions recorded is greater than a preset number threshold.

6. The method for controlling the emergence of autonomous rapid aggregation behavior of swarm robots according to any one of claims 1 to 5, characterized in that: Team roles include: leader and follower.

7. The method for controlling the emergence of autonomous rapid aggregation behavior of swarm robots according to claim 6, characterized in that: The movement based on team roles and formations includes: If the team role is the leader, the coordinate position calculation formula is used at each moment to determine the coordinate position of the robot at the next moment, and the robot is controlled to move to the determined coordinate position; If the team role is a follower, it follows the leader in the formation to move based on the cohesion rule at each moment; The coordinate position calculation formula is: Among them, (x t ,y t ) represents the coordinate position of the robot in the global coordinate system at time t, (x t-1 ,y t-1 ) represents the coordinate position of the robot in the global coordinate system at time t-1, m rand and n rand represents a random number within [-40,40] that follows a normal distribution, v x Indicates the absolute speed in the x direction based on the robot body coordinate system, v y It represents the absolute speed in the y direction based on the robot body coordinate system, and dt represents the time interval between time t and time t-1.

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