Multi-robot path searching method for optical communication scene

Abstract

The invention provides a multi-robot path searching method for an optical communication scene, and belongs to the field of robot path planning, and the method comprises the steps: obtaining an optical communication scene road network containing optical communication sites and non-optical communication sites, and obtaining a simplified road network and road network nodes through the fusion of the non-optical communication sites; on the basis of neglecting conflicts among robot paths, forming an initial path scheme through a shortest path search algorithm, judging whether space-time conflicts exist or not, generating corresponding space-time constraints according to a space-time conflict judgment result, and recalculating to form a new path scheme through the shortest path search algorithm with the space-time constraints; and repeatedly executing the shortest path search algorithm with the space-time constraint until an optimal path scheme is obtained. The problems that in the prior art, due to the fact that continuous real-time wireless communication is relied on, effective conflict detection and avoidance cannot be carried out in an optical communication discontinuous communication scene, the robot collision risk is high, and the planning efficiency is low are solved.

Description

Technical Field

[0001] This invention belongs to the field of robot path planning, and in particular relates to a multi-robot path search method for optical communication scenarios. Background Technology

[0002] Multi-robot pathfinding is the core and most challenging aspect of multi-robot scheduling systems. It requires finding a collision-free path for each robot, ensuring that all robot paths do not conflict in time or space, and optimizing overall efficiency. In current multi-robot scheduling system applications, WiFi-based scheduling is very common. In WiFi scenarios, scheduling systems can obtain real-time robot position and status information, dynamically detect and resolve conflicts, and dynamically search for new paths. However, in scenarios with high security and confidentiality requirements, such as defense and military industries, wireless communication methods like WiFi, 4G, and 5G are prohibited in production workshops. Robots can only interact with the scheduling system through short-range optical communication devices at designated communication stations. Therefore, the scheduling system cannot dynamically obtain the real-time status of each robot, and unpredictable collisions are highly likely to occur in areas where communication is lost. Path planning becomes highly complex and difficult; conventional multi-robot pathfinding algorithms suitable for WiFi scenarios are not applicable to this situation. Summary of the Invention

[0003] To address the aforementioned shortcomings in existing technologies, this invention provides a multi-robot path search method for optical communication scenarios. This method solves the problem that existing technologies, which rely on continuous real-time wireless communication, cannot effectively detect and avoid collisions in intermittent optical communication scenarios, resulting in high robot collision risks and low planning efficiency.

[0004] To achieve the above objectives, the technical solution adopted by this invention is: a multi-robot path search method for optical communication scenarios, comprising the following steps: S1. Obtain the optical communication scenario road network that includes optical communication sites and non-optical communication sites, and simplify the optical communication scenario road network by merging non-optical communication sites to obtain a simplified road network and road network nodes; S2. Based on the simplified road network, road network nodes, and the task objectives of multiple robots, and ignoring conflicts between robot paths, the initial shortest path for each robot to complete the task objective is calculated using the shortest path search algorithm, thus forming the initial path scheme. S3. Determine whether there are spatiotemporal conflicts between the paths of all robots in the initial path scheme, and generate corresponding spatiotemporal constraints for the robots based on the spatiotemporal conflict determination results. S4. Based on the spatiotemporal constraints, simplified road network, road network nodes, and multi-robot task objectives, the shortest path with spatiotemporal constraints is recalculated using the shortest path search algorithm to complete the task objectives of the robots and form a new path scheme. S5. Take the new path scheme as the current path scheme, evaluate it through the cost function, and return to S3 for iterative processing until a path scheme in which there is no spatiotemporal conflict between the paths of all robots is obtained, and this path scheme is taken as the final conflict-free path scheme.

[0005] Further: The specific steps of S1 include: S101. Obtain the road network of the optical communication scenario and mark the optical communication sites and non-optical communication sites within it; S102. Based on the optical communication scenario road network, traverse all non-optical communication sites. For any non-optical communication site that has not been merged, find all other non-optical communication sites connected to it to obtain the non-optical communication connected area. S103. Merge all non-optical communication sites in the non-optical communication connected area to obtain a new non-optical communication site; S104. Based on the new non-optical communication sites and optical communication sites, a simplified road network and its corresponding road network nodes are obtained according to the road network connection of the optical communication scenario.

[0006] The further beneficial effects mentioned above are: by merging interconnected non-optical communication sites into a new non-optical communication site, the complexity of the road network in optical communication scenarios can be reduced, the computational load of path search can be reduced, and at the same time, it can prevent two or more robots from entering the same non-optical communication connectivity area at the same time, which would lead to unknown conflicts and resource competition.

[0007] Further: The specific steps of S2 include: S201. Based on the initial multi-robot task objective, determine the path start information and path end information of each robot, and map them to the corresponding road network nodes in the simplified road network to obtain the start node and end node of each robot. S202. Based on the starting and ending nodes of each robot, ignoring conflicts between robot paths, the shortest path search algorithm is used to independently traverse each robot on the simplified road network nodes to obtain the initial shortest path for each robot to complete the task objective. S203. Integrate the initial shortest paths for each robot to complete the task objective to form an initial path scheme.

[0008] The further beneficial effects mentioned above are: by independently planning the initial shortest path for each robot, the initial complexity of multi-robot collaborative path search is reduced, while providing high-quality initial data for subsequent conflict detection and iterative optimization of path schemes, thereby improving the convergence speed and computational efficiency of the overall path search process.

[0009] Furthermore, the specific steps of S3 include: S301. Based on the initial shortest paths of all robots in the initial path scheme, determine whether there is a spatiotemporal conflict between the initial shortest paths of any two different robots. If so, mark the two different robots as a conflicting robot group and proceed to S302. Otherwise, it is a path scheme without spatiotemporal conflict. The types of spatiotemporal conflicts include vertex conflicts and edge conflicts; The condition for determining vertex conflict is: at the same time step, two robots occupy the same road network node position; The condition for determining edge conflict is: after one time step, the two robots exchanged the positions of their respective road network nodes. S302. Based on the type of spatiotemporal conflict, generate spatiotemporal constraints corresponding to the type of spatiotemporal conflict for one of the conflicting robot groups. The spatiotemporal conflict type is vertex conflict, and the spatiotemporal constraint is: the robot is prohibited from occupying the node at the time step in which the spatiotemporal conflict occurs; The spatiotemporal conflict type is an edge conflict, and the spatiotemporal constraint is: prohibiting the robot from performing a movement that causes an edge conflict at the corresponding time step in which the edge conflict occurs.

[0010] The further beneficial effects mentioned above are as follows: By defining the types of spatiotemporal conflicts and generating targeted constraints, the present invention can accurately identify potential collision conflicts between robots and thereby limit the subsequent path search space, thus guiding the path search optimization direction and improving the convergence speed of solving spatiotemporal conflict-free paths in a multi-robot wireless communication environment.

[0011] Furthermore, the specific steps of S4 include: S401. Based on the spatiotemporal constraints, determine the target robot corresponding to the spatiotemporal constraints; S402. Based on the target robot, determine the target robot's task objective, path start information, and path end information, and map them to the corresponding road network nodes in the simplified road network to obtain the target robot's start node and end node. S403. Take the starting node of the target robot as the starting point of the path search, perform node expansion search through the shortest path search algorithm, and check whether the spatiotemporal constraints are violated according to the time step. If so, abandon the node expansion search direction and start the node expansion search again. Otherwise, include the feasible node expansion search direction and proceed to S404. S404. Based on the node expansion search direction and road network nodes, form a node expansion search path, and stop the shortest path search algorithm when the node expansion search path first reaches the target robot's termination node, thus obtaining the constrained shortest path for the robot to complete the task objective with spatiotemporal constraints. S405. Integrate the shortest constrained paths for all robots with spatiotemporal constraints to complete the task objective into the initial path scheme to obtain a new path scheme.

[0012] The further beneficial effects mentioned above are: by incorporating spatiotemporal constraints into the robot path search process, a balance is achieved between spatiotemporal constraint compliance and path search efficiency. While ensuring that the generated path meets the spatiotemporal constraints, invalid nodes are discarded to expand the search direction, thereby reducing computational costs.

[0013] Furthermore, the expression for the cost function in S5 is as follows:

[0014] in, For path scheme The cost function, For path scheme Total length, To generate a path scheme The number of constraints added.

[0015] The beneficial effects of this invention are: 1. This solution innovatively introduces a map preprocessing stage, which reasonably simplifies the map, thus achieving better search efficiency. In addition, because it integrates sites in non-optical communication connectivity areas, it avoids the risk of collisions and conflicts caused by multiple robots entering non-optical communication connectivity areas at the same time and losing communication simultaneously.

[0016] 2. This scheme employs a breadth-first search algorithm, combined with a cost function based on the sum of the path distances of each robot plus the total number of constraints. This approach balances search success rate and efficiency, minimizing path conflicts while maintaining a small total path distance. Compared to the commonly used combination of depth-first search and the path distance sum cost function, the scheme presented in this invention offers significant advantages in both success rate and search efficiency. Attached Figure Description

[0017] Figure 1 A flowchart illustrating a multi-robot path search method for optical communication scenarios; Figure 2 This is a schematic diagram of the preprocessing process for the road network in an optical communication scenario; Figure 3 This is a schematic diagram of spatiotemporal conflicts in multi-robot paths. Detailed Implementation

[0018] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0019] Example 1 like Figure 1 The diagram shown is a flowchart of a multi-robot path search method for optical communication scenarios. This invention provides a multi-robot path search method for optical communication scenarios, comprising the following steps: S1. Obtain the optical communication scenario road network that includes optical communication sites and non-optical communication sites, and simplify the optical communication scenario road network by merging non-optical communication sites to obtain a simplified road network and road network nodes; S2. Based on the simplified road network, road network nodes, and the task objectives of multiple robots, and ignoring conflicts between robot paths, the initial shortest path for each robot to complete the task objective is calculated using the shortest path search algorithm, thus forming the initial path scheme. S3. Determine whether there are spatiotemporal conflicts between the paths of all robots in the initial path scheme, and generate corresponding spatiotemporal constraints for the robots based on the spatiotemporal conflict determination results. S4. Based on the spatiotemporal constraints, simplified road network, road network nodes, and multi-robot task objectives, the shortest path with spatiotemporal constraints is recalculated using the shortest path search algorithm to complete the task objectives of the robots and form a new path scheme. S5. Repeat S3 and S4 until a feasible path scheme with no spatiotemporal conflict is obtained between all robot paths. Then, evaluate the feasible path scheme based on the preset cost function to obtain the optimal path scheme.

[0020] In high-security optical communication scenarios, where wireless communication such as WiFi and 4G / 5G is prohibited, existing technologies can only interact with robots at discrete optical communication sites. This prevents the real-time, continuous acquisition of the position status of all robots, causing conventional multi-robot path planning algorithms to fail and resulting in unknown collisions when robots enter communication blind spots. This invention proposes a multi-robot path search method for optical communication scenarios. Through a non-optical communication site fusion step, interconnected non-optical communication sites are aggregated into a single node, preventing multiple robots from losing connection when entering the same non-optical communication site from a map modeling perspective. Unknown collisions are identified, simplifying the path search space and improving path search efficiency. Subsequently, on the simplified road network, an initial shortest path is planned for each robot, and an iterative optimization loop based on spatiotemporal constraints is entered. This loop dynamically generates corresponding spatiotemporal constraints by detecting spatiotemporal conflicts between each robot's paths, namely vertex conflicts and edge conflicts, and replans paths for affected robots based on these constraints. The merits of the path schemes are judged using the sum of the total path length and the number of spatiotemporal constraints as the cost function. This allows for the exploration of different combinations of spatiotemporal constraint branches, prioritizing the expansion of better solutions until a set of completely conflict-free path schemes is found.

[0021] In one embodiment of the present invention, because there are non-optical communication stations in the optical communication scenario road network that cannot communicate with the scheduling system, the non-optical communication connectivity area formed by multiple non-optical communication stations is completely unknown to the scheduling system. In order to prevent two or more robots from entering the same non-optical communication connectivity area at the same time step, resulting in unknown conflicts and resource competition, the present invention preprocesses the optical communication scenario road network: S1, obtain the optical communication scenario road network containing optical communication stations and non-optical communication stations, and simplify the optical communication scenario road network by merging non-optical communication stations to obtain a simplified road network and road network nodes; like Figure 2 The diagram illustrates the preprocessing of the road network in an optical communication scenario. In a specific embodiment of the present invention, step S1 includes: S101. Obtain the optical communication scene road network and mark the optical communication stations and non-optical communication stations within it, i.e. Figure 2 Nodes A, E, F, and J are optical communication stations, while nodes C, B, D, L, G, I, K, and H are non-optical communication stations. S102. Based on the optical communication scenario road network, traverse all non-optical communication sites. For any non-optical communication site that has not been merged, find all other non-optical communication sites connected to it to obtain the non-optical communication connected area. S103. Merge all non-optical communication sites in the non-optical communication connected area to obtain a new non-optical communication site; S104. Based on the new non-optical communication sites and optical communication sites, a simplified road network and its corresponding road network nodes are obtained according to the road network connection of the optical communication scenario.

[0022] exist Figure 2 In this network, nodes C, B, D, and L form a non-optical communication connected area, nodes I and K form another non-optical communication connected area, and nodes G and H, although not connected to other non-optical communication stations, can still be considered as a non-optical communication connected area. After merging all the non-optical communication stations in the non-optical communication connected areas, new non-optical communication stations CBDL and IK are obtained, finally forming a simplified road network and its corresponding road network nodes. The simplified road network enables subsequent path planning for the robot to focus more efficiently on the coordination of optical communication stations, improving the overall scheduling efficiency and reliability.

[0023] In one embodiment of the present invention, in a multi-robot optical communication scenario road network, simultaneously finding the path for each robot to complete its task objective without collisions requires enormous computational resources and time costs, and is extremely complex. Therefore, the present invention prioritizes finding the shortest path for each robot to complete its task objective without considering collisions between robots. Specifically, through S2: based on a simplified road network, road network nodes, and the task objectives of the multiple robots, ignoring conflicts between robot paths, an initial shortest path for each robot to complete its task objective is calculated using a shortest path search algorithm, forming an initial path scheme. The specific steps of S2 include: S201. Based on the initial multi-robot task objective, determine the path start information and path end information of each robot, and map them to the corresponding road network nodes in the simplified road network to obtain the start node and end node of each robot. S202. Based on the starting and ending nodes of each robot, ignoring conflicts between robot paths, the shortest path search algorithm is used to independently traverse each robot on the simplified road network nodes to obtain the initial shortest path for each robot to complete the task objective. S203. Integrate the initial shortest paths for each robot to complete the task objective to form an initial path scheme.

[0024] In one embodiment of the present invention, in a multi-robot optical communication scenario road network, real-time communication is not possible to preemptively avoid potential collision risks between the paths of multiple robots. Based on the shortest path obtained in S2 for each robot to complete its task objective, the present invention generates spatiotemporal constraints for robots with collision risks by clarifying and simulating the occurrence of collision risks, and adjusts their paths. The present invention designs S3, which determines whether there are spatiotemporal conflicts between the paths of all robots in the initial path scheme, and generates corresponding spatiotemporal constraints for the robots based on the spatiotemporal conflict determination results. The specific steps of S3 include: S301. Based on the initial shortest paths of all robots in the initial path scheme, determine whether there is a spatiotemporal conflict between the initial shortest paths of any two different robots. If so, mark the two different robots as a conflicting robot group and proceed to S302. Otherwise, it is a path scheme without spatiotemporal conflict. The types of spatiotemporal conflicts include vertex conflicts and edge conflicts; The condition for determining vertex conflict is: at the same time step, two robots occupy the same road network node position, such as... Figure 3 As shown, this is a schematic diagram of spatiotemporal conflict in multi-robot paths. The numbers 1, 2, 3, and 4 on the right side represent time steps. Path 1 and Path 2 have vertex conflicts. At time step 2, they both occupy node B. The condition for determining edge conflict is: after one time step, the two robots have exchanged the positions of their respective road network nodes. Figure 3 In the process, there is an edge conflict between path 3 and path 4. From time step 2 to time step 3, the two robots represented by path 3 and path 4 exchanged their road network nodes, which means that a head-on collision occurred. S302. Based on the type of spatiotemporal conflict, generate a spatiotemporal constraint corresponding to the type of spatiotemporal conflict for one of the conflicting robot groups; the spatiotemporal constraint can take the form of time step, road network node, etc., representing that the robot cannot appear at the road network node at that time step. The spatiotemporal constraint for a vertex conflict type is: the robot is prohibited from occupying the node at the time step in which the spatiotemporal conflict occurs; The spatiotemporal conflict type is edge conflict, and the spatiotemporal constraint is: prohibiting the robot from executing a movement that causes an edge conflict at the corresponding time step. After generating spatiotemporal constraints for the robot, the robot's path is adjusted in both time and space to prevent multiple robots from colliding at the same road network node in the same time step, as well as collisions due to relative paths.

[0025] In one embodiment of the present invention, after adding constraints to the robot, it can be ensured that the original multi-robot path scheme will no longer collide, thereby recalculating the multi-robot path scheme to obtain a new path scheme. S4: Based on the spatiotemporal constraints, simplified road network, road network nodes, and multi-robot task objectives, the shortest path with spatiotemporal constraints for the robots to complete the task objectives is recalculated using a shortest path search algorithm, and a new path scheme is formed; the specific steps of S4 include: S401. Based on the spatiotemporal constraints, determine the target robot corresponding to the spatiotemporal constraints; by determining the target robot, the computational resource consumption when planning new path schemes can be reduced, and computational efficiency can be improved. S402. Based on the target robot, determine the target robot's task objective, path start information, and path end information, and map them to the corresponding road network nodes in the simplified road network to obtain the target robot's start node and end node. S403. Using the starting node of the target robot as the starting point of the path search, the node expansion search is performed using the shortest path search algorithm. The time step is used to check whether the spatiotemporal constraints are violated. If so, the node expansion search direction is abandoned and the node expansion search is performed again. Otherwise, the feasible node expansion search direction is included and the process proceeds to S404. This invention adds spatiotemporal constraints to the original shortest path search algorithm, thereby eliminating invalid node expansion search schemes and improving the search efficiency of finding feasible paths under constraints. S404. Based on the node expansion search direction and road network nodes, form a node expansion search path, and stop the shortest path search algorithm when the node expansion search path first reaches the target robot's termination node, thus obtaining the constrained shortest path for the robot to complete the task objective with spatiotemporal constraints. S405. Integrate the shortest constrained paths for all robots with spatiotemporal constraints to complete the task objective into the initial path scheme to obtain a new path scheme.

[0026] In one embodiment of the present invention, S4 yields a new path scheme. However, in the new path scheme, multiple robot paths may enter nodes not occupied by the old path scheme at different time steps, leading to the recurrence of spatiotemporal conflicts. Therefore, the new path scheme undergoes another spatiotemporal conflict judgment, i.e., S5, repeating S3 and S4 until a feasible path scheme without spatiotemporal conflicts between all robot paths is obtained. The feasible path schemes are then evaluated based on a preset cost function, and the path scheme with the minimum cost function is selected to obtain the optimal path scheme. The preset cost function is the sum of the distances of all robot paths plus the total number of constraints. The expression of the cost function in S5 is as follows:

[0027] in, For path scheme The cost function, For path scheme Total length, To generate a path scheme The number of constraints added.

[0028] The beneficial effects of this invention are as follows: This invention addresses the challenge of collaborative path scheduling for multiple robots in optical communication scenarios with high security requirements. By preprocessing through the fusion of non-optical communication sites, connected communication blind spots are merged into virtual nodes, significantly simplifying the topological complexity of path search and avoiding collision risks caused by multiple robots simultaneously entering unmonitored areas, thus ensuring system security. Furthermore, by defining spatiotemporal conflict types and generating corresponding constraints, this invention transforms the obstacle avoidance problem in real-time communication into a pre-planned spatiotemporal constraint satisfaction problem. Through shortest path search with spatiotemporal constraints, combined with iterative calculations using a cost function equal to the sum of the total path length and the number of spatiotemporal constraints, it can efficiently and reliably search for globally conflict-free path solutions with excellent overall efficiency. This invention is applicable to road networks in optical communication scenarios with high security requirements, possessing strong engineering practicality and deployment convenience, providing secure, reliable, and efficient technical support for multi-robot path scheduling in confidential industries.

Claims

1. A multi-robot path search method for optical communication scenarios, characterized in that, Includes the following steps: S1. Obtain the optical communication scenario road network that includes optical communication sites and non-optical communication sites, and simplify the optical communication scenario road network by merging non-optical communication sites to obtain a simplified road network and road network nodes; S2. Based on the simplified road network, road network nodes, and the task objectives of multiple robots, and ignoring conflicts between robot paths, the initial shortest path for each robot to complete the task objective is calculated using the shortest path search algorithm, thus forming the initial path scheme. S3. Determine whether there are spatiotemporal conflicts between the paths of all robots in the initial path scheme, and generate corresponding spatiotemporal constraints for the robots based on the spatiotemporal conflict determination results. S4. Based on the spatiotemporal constraints, simplified road network, road network nodes, and the task objectives of multiple robots, the shortest path with spatiotemporal constraints is recalculated using the shortest path search algorithm to complete the task objectives of the robots and form a new path scheme. S5. The new path scheme is used as the current path scheme. It is evaluated through the cost function and returned to S3 for iterative processing until a path scheme in which there is no spatiotemporal conflict between the paths of all robots is obtained, and this path scheme is taken as the final conflict-free path scheme.

2. The multi-robot path search method for optical communication scenarios according to claim 1, characterized in that, The specific steps of S1 include: S101. Obtain the road network of the optical communication scenario and mark the optical communication sites and non-optical communication sites within it; S102. Based on the optical communication scenario road network, traverse all non-optical communication sites. For any non-optical communication site that has not been merged, find all other non-optical communication sites connected to it to obtain the non-optical communication connected area. S103. Merge all non-optical communication sites in the non-optical communication connected area to obtain a new non-optical communication site; S104. Based on the new non-optical communication sites and optical communication sites, a simplified road network and its corresponding road network nodes are obtained according to the road network connection of the optical communication scenario.

3. The multi-robot path search method for optical communication scenarios according to claim 1, characterized in that, The specific steps of S2 include: S201. Based on the initial multi-robot task objective, determine the path start information and path end information of each robot, and map them to the corresponding road network nodes in the simplified road network to obtain the start node and end node of each robot. S202. Based on the starting and ending nodes of each robot, ignoring conflicts between robot paths, the shortest path search algorithm is used to independently traverse each robot on the simplified road network nodes to obtain the initial shortest path for each robot to complete the task objective. S203. Integrate the initial shortest paths for each robot to complete the task objective to form an initial path scheme.

4. The multi-robot path search method for optical communication scenarios according to claim 1, characterized in that, The specific steps of S3 include: S301. Based on the initial shortest paths of all robots in the initial path scheme, determine whether there is a spatiotemporal conflict between the initial shortest paths of any two different robots. If so, mark the two different robots as a conflicting robot group and proceed to S302. Otherwise, it is a path scheme without spatiotemporal conflict. The types of spatiotemporal conflicts include vertex conflicts and edge conflicts; The condition for determining vertex conflict is: at the same time step, two robots occupy the same road network node position; The condition for determining edge conflict is: after one time step, the two robots exchanged the positions of their respective road network nodes. S302. Based on the type of spatiotemporal conflict, generate spatiotemporal constraints corresponding to the type of spatiotemporal conflict for one of the conflicting robot groups. The spatiotemporal conflict type is vertex conflict, and the spatiotemporal constraint is: the robot is prohibited from occupying the node at the time step in which the spatiotemporal conflict occurs; The spatiotemporal conflict type is an edge conflict, and the spatiotemporal constraint is: prohibiting the robot from performing a movement that causes an edge conflict at the corresponding time step in which the edge conflict occurs.

5. The multi-robot path search method for optical communication scenarios according to claim 1, characterized in that, The specific steps of S4 include: S401. Based on the spatiotemporal constraints, determine the target robot corresponding to the spatiotemporal constraints; S402. Based on the target robot, determine the target robot's task objectives, path start information, and path end information, and map them to the corresponding road network nodes in the simplified road network to obtain the target robot's start node and end node. S403. Take the starting node of the target robot as the starting point of the path search, perform node expansion search through the shortest path search algorithm, and check whether the spatiotemporal constraints are violated according to the time step. If so, abandon the node expansion search direction and start the node expansion search again. Otherwise, include the feasible node expansion search direction and proceed to S404. S404. Based on the node expansion search direction and road network nodes, form a node expansion search path, and stop the shortest path search algorithm when the node expansion search path first reaches the target robot's termination node, thus obtaining the constrained shortest path for the robot to complete the task objective with spatiotemporal constraints. S405. Integrate the shortest constrained paths for all robots with spatiotemporal constraints to complete the task objective into the initial path scheme to obtain a new path scheme.

6. The multi-robot path search method for optical communication scenarios according to claim 1, characterized in that, The expression for the cost function in S5 is as follows: in, For path scheme The cost function, For path scheme Total length, To generate a path scheme The number of constraints added.

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