A consistent path planning method for multi-robot cooperative work

Abstract

The application belongs to the technical field of multi-robot path planning, and specifically relates to a consistency path planning method for multi-robot cooperative operation. Firstly, an environment map is converted into a grid map, and the starting point and the ending point of each robot are set; then, the priority score of each robot is calculated, all the robots are layered according to the priority score to obtain the inter-layer priority, and the intra-layer priority of the robot is determined according to the priority score; finally, according to the starting point and the ending point of the robot, Yen's algorithm is used to generate multiple optional paths for the robot, the motion path of the robot is distributed according to the intra-layer priority to realize the primary distribution of the motion path, and the motion path of the robot is distributed again according to the inter-layer priority to fine-tune the primary distributed motion path, so that the motion paths of all the robots do not conflict. The method adopts the strategy of combining hierarchical path distribution, intra-layer priority sorting and inter-layer conflict detection, can effectively reduce path conflicts, the newly generated path is a continuation or a subset of the previous path, ensures the continuity and consistency of path planning, and reduces path changes.

Description

Technical Field

[0001] The present invention belongs to the technical field of multi-robot path planning, and in particular relates to a consistent path planning method for multi-robot collaborative operation. Background Art

[0002] With the development of industrial automation, multi-robot systems are increasingly being used in industrial production, logistics, and other fields. Collaboration improves production efficiency and reduces reliance on human labor, making them a vital component of modern industry. Path continuity and smooth transitions are crucial in multi-robot collaborative operations. Sudden changes in the path can not only cause discontinuous robot motion but also lead to system instability and, in some cases, even safety issues.

[0003] While existing path planning methods, such as the A* and Dijkstra algorithms, can plan effective paths for a single robot, dynamically adjusting paths to avoid collisions between robots in multi-robot scenarios remains challenging. For multi-robot path planning, whether using the WHCA* (Windowed Hierarchical Cooperative A*) algorithm or the CBS (Conflict-Based Search) algorithm, when one robot adjusts its path, it can affect the paths of other robots, creating a chain reaction and degrading system stability. When a path is interrupted by an obstacle or other unexpected situation, a new path is typically planned for the robot. However, this replanning often fails to consider previously planned path information, resulting in a lack of continuity between the new path and the previous one. This means that the new path is not a natural continuation or subset of the previous path. This path change can cause the robot to engage in inefficient behaviors, such as turning and slowing down, impacting overall path planning efficiency and responsiveness. Therefore, effectively replanning the path so that the newly generated path maximizes the use of existing path information is a key challenge in path planning. Summary of the Invention

[0004] In view of the deficiencies in the prior art, the technical problem to be solved by the present invention is to provide a consistent path planning method for multi-robot collaborative operations.

[0005] The present invention solves the technical problem by adopting the following technical solutions:

[0006] A method for consistent path planning for multi-robot collaborative operation, characterized in that the method comprises the following steps:

[0007] Step 1: Convert the environment map into a grid map and set the starting and ending points of each robot on the grid map;

[0008] Step 2: Set the robot's priority and calculate the priority score according to the following formula:

[0009]

[0010] Where, is the priority score; is the task importance coefficient, Is the battery percentage, is the current priority, 、 and are weight factors;

[0011] All robots are divided into layers according to the priority scores to obtain inter-layer priorities, where each inter-layer priority corresponds to a priority score interval; at the same time, the robot's intra-layer priority is determined according to the priority scores;

[0012] Step 3: Based on the robot's starting point and end point, use Yen's algorithm to generate multiple optional paths for the robot and assign the robot's movement path according to priority;

[0013] First, for all robots corresponding to the inter-layer priority, a motion path is assigned to each robot in turn according to the intra-layer priority, that is, the robot with the highest priority in the layer selects the shortest optional path as the motion path; the robot with the second highest priority in the layer performs conflict detection on multiple optional paths according to the alternative sorting and the motion path of the robot with the highest priority in the layer, and selects the optional path without conflict as the motion path of the robot with the second highest priority in the layer; according to the above operation, motion paths are assigned to the remaining robots in turn to complete the initial allocation of motion paths; then, the motion paths initially assigned to the robots are redistributed according to the inter-layer priority, that is, the motion paths initially assigned to each robot with the second highest inter-layer priority are sequentially detected for conflict with the motion paths of each robot with the highest inter-layer priority; if there is a conflict, the remaining optional paths are selected until a non-conflicting motion path is assigned;

[0014] The conflict detection process is as follows: perform cubic spline interpolation on the robot's path to obtain the path interpolation equation; make the interpolation equations of the two paths equal, and determine whether the two paths have an intersection by solving the equation group. If there is no intersection, it indicates that there will be no conflict, so the two paths do not conflict; if there is an intersection, it indicates that there may be a conflict in space, and then determine whether there will be a conflict in time according to the following formula. If the following formula is true, it is considered that the two paths will conflict in time, so the two paths conflict; if the following formula is not true, it is considered that the two paths do not conflict in time, so the two paths do not conflict;

[0015]

[0016] In the formula, 、 is the integral distance of two robots from the starting point to the intersection point, 、 is the movement speed of the two robots, is the time conflict threshold.

[0017] Compared with the prior art, the beneficial effects of the present application are:

[0018] 1. Unlike the conventional image conversion algorithm (such as OpenCV) for building a grid map, the present application uses the MISSFormer model to perform semantic segmentation on the environment map to obtain the class label of each pixel; the class labels of all pixels in the grid cell are aggregated to obtain the class label of the grid cell, realizing pixel-level conversion of the map, especially in complex or dynamic environments, not only can accurately identify obstacles and free space, but also can provide rich semantic information for other objects (such as vehicles, pedestrians, etc.), which can significantly improve the accuracy of converting the environment map into a grid map, and is conducive to improving the accuracy of path planning and conflict detection.

[0019] 2. The priority of the robot is determined based on the importance of the task, the power and the current priority, including the inter-layer priority and the intra-layer priority, the movement path of all robots in the layer is initially allocated according to the intra-layer priority, and the movement path of the robot is allocated again according to the inter-layer priority, the strategy of combining hierarchical path allocation, intra-layer priority sorting and inter-layer conflict detection can effectively reduce path conflicts. When multiple robots suddenly stop due to obstacles during movement according to the planned path, and the path is re-planned again, most of the newly generated paths are part of the path planned before the last stop, and the newly generated path is not only a continuation of the previous path, but also a subset of it, thereby ensuring the continuity and consistency of the path, reducing the additional computational burden caused by path changes, and improving the adaptability and response speed of the robot to environmental changes.

[0020] 3. The discrete path points are converted into continuous path functions using cubic spline interpolation, which is used to detect whether the two robot optional paths exist in space conflict, and further judge whether the two optional paths conflict in time by comparing the size between the time difference and the time conflict threshold, realizing accurate detection of conflicts.

[0021] 4. Yen's algorithm is used to further generate multiple optional paths for each robot based on the shortest path, when the preferred path of the robot conflicts, other optional paths can be switched, so as to achieve the purpose of reducing path changes.

[0022] 5. The robot's priority changes dynamically and can be adjusted dynamically based on actual conditions, especially in complex scenarios, to maximize the overall system's operating efficiency while avoiding conflicts. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 is an overall flow chart of the method of the present invention;

[0024] Figure 2 is the initial path planning result of the method of the present invention;

[0025] Figure 3 is the initial path planning result of the CBS algorithm;

[0026] Figure 4 is the secondary path planning result of the method of the present invention;

[0027] Figure 5 This is the secondary path planning result of the CBS algorithm. DETAILED DESCRIPTION

[0028] Specific embodiments are given below in conjunction with the accompanying drawings. The specific embodiments are only used to introduce the technical solutions of the present invention in detail and are not intended to limit the scope of protection of the present application.

[0029] The present invention provides a consistent path planning method for multi-robot collaborative operation (hereinafter referred to as the method, see Figures 1 to 5 ), including the following steps:

[0030] Step 1: Convert the environment map into a grid map and set the starting and ending points of each robot on the grid map;

[0031] The environment map usually contains open areas for robot movement and some impassable obstacles, such as walls, machinery and equipment, or shelves for storing goods. The environment map can be a simplified representation of an actual industrial scene, such as the layout of a factory workshop or the floor plan of a logistics warehouse.

[0032] The MISSFormer model is used to segment each pixel of the environment map, predict its category, and obtain the category label of each pixel; first, the probability of the pixel belonging to each category is calculated to obtain the probability distribution of the pixel; the pixel in the environment map I The probability distribution of is: ,in, Represents pixels Belong to category The probability of Represents the number of categories; then, the category corresponding to the maximum probability is selected as the category label, then the pixel The category labels are: ; The category labels of all pixels are output as a segmentation map, which has the same size as the environment map and contains one category label for each pixel.

[0033] The environment map is evenly divided into multiple grid cells, each grid cell corresponds to an area, and the grid cell contains all the pixels in the area; according to the segmentation map, the grid cells are Aggregate the category labels of all pixels in to get the grid unit Category label The environment map is rasterized according to the class labels of the network cells to obtain a grid map. For example, if the class label is an obstacle, the value of the network cell is 1; if the class label is free space, the value of the grid cell is 0. The grid map provides the basis for setting the starting and ending points of the robot, because each grid cell is a potential location or target location of the robot.

[0034] The robot's starting point represents its initial position at the start of a mission. It's typically set manually by the user based on the specific mission, selecting a traversable location on the grid map. It's important to note that the starting point cannot be set on an obstacle, otherwise the robot will be unable to begin moving. Ideally, the starting point should be set in a suitable location within the robot's workspace, such as a loading and unloading point in a logistics warehouse, next to a machine on a factory floor, or near a processing area. In complex multi-robot scenarios, multiple robots may share the same or adjacent starting points. In such cases, care must be taken to prevent collisions with other robots.

[0035] The robot's endpoint represents the target location it needs to reach at the end of its mission. This is also manually set by the user based on the specific task. For example, in a logistics warehouse, the endpoint might be the location of a specific shelf; on a factory floor, it might be next to another processing machine or assembly line. The endpoint should not overlap with obstacles and should also avoid potential collisions with other robots. This is especially true in complex multi-robot scenarios, where multiple robots may reach the same endpoint simultaneously. Prioritization and path planning are essential for proper scheduling to avoid congestion or conflicts at the endpoint.

[0036] Step 2: Set the robot's priority, including inter-layer priority and intra-layer priority;

[0037] In multi-robot task scenarios, paths often intersect or conflict between robots. Therefore, a priority is set for each robot. Robots with high priorities will have more freedom in path selection and can give priority to the optimal path, while robots with low priorities need to make adjustments based on the existing path selection results. Priorities are set based on a variety of factors, such as the urgency, importance, execution time, and robot model of the task. In some application scenarios, users may manually set priorities based on the importance of the task, such as assigning high-importance tasks to robots with high priorities to ensure that high-importance tasks can be completed first. The present invention takes into account the robot's task importance, power, and current priority, and calculates the priority score according to the following formula:

[0038]

[0039] Where, is the priority score; is the task importance coefficient, which ranges from 0 to 1 and is used to represent the importance of the task. The importance of the task directly affects the priority of the robot. The more important the task, the higher the priority. It is the percentage of battery power. The lower the battery power, the higher the robot priority. The higher the battery power, the lower the robot priority. is the current priority, with a value range of 1 to N, where N is the number of robots. The lower the current priority, the greater the impact on the priority score; 、 and are weight factors;

[0040] All robots are layered according to their priority scores to obtain inter-layer priorities. Each inter-layer priority corresponds to a priority score interval, so each inter-layer priority contains multiple robots. At the same time, the robot's intra-layer priority is determined according to the priority score. The higher the priority score, the higher the intra-layer priority.

[0041] As can be seen from the above formula, robot priority is not static but is dynamically adjusted through real-time assessment of the importance of the robot's task and its current working status (such as remaining battery power and path occupancy). For example, a robot with low battery power may be given a higher priority to ensure its movement path is not delayed; a high-priority robot can be lowered after completing its task to optimize the collaborative efficiency of the robots in the system. When a robot stops due to encountering an obstacle or other unforeseen reasons, the robot's priority can be lowered before the next path planning, allowing other robots to be assigned a path first. This ensures that the path planning result is as close as possible to the previous planning result. This ensures that the movement paths of all robots, except the stopped robot, are as consistent as possible, avoiding unnecessary path adjustments as much as possible, ensuring the efficiency and stability of the system, and reducing the resource consumption and complexity caused by frequent path changes.

[0042] To ensure the flexibility and robustness of the multi-robot system, the robot priorities can be adjusted dynamically in real time based on actual conditions. For example, if a high-priority robot stagnates or experiences a task delay, its priority can be temporarily lowered to allow other robots to pass, thereby improving the overall efficiency of the system. Conversely, if a robot has an urgent task, its priority can be increased to ensure it completes the task in a short period of time.

[0043] Step 3: Use Yen's algorithm to generate multiple optional paths for each robot and assign motion paths to the robots based on priority;

[0044] In multi-robot tasks, conflicts may arise between paths, especially when multiple robots pass through the same area at the same time. Therefore, a single path often cannot meet the needs. Based on the starting and ending points of the robots, Yen's algorithm is used to generate multiple optional paths for the robots. When the preferred optional path is occupied or a conflict occurs, it can flexibly switch to other optional paths. This is also an important means to resolve conflicts and improve efficiency.

[0045] First, in each inter-layer priority, motion paths are assigned to each robot in order according to the intra-layer priority. For the robot with the highest priority in the layer, the shortest optional path is directly selected as the motion path. This is because no other robot in the layer occupies any path in the environment. For the robot with the second highest priority in the layer, multiple optional paths are checked for conflicts with the motion path of the robot with the highest priority in the layer according to the alternative ranking, and the optional path that does not conflict is selected as the motion path of the robot with the second highest priority in the layer. According to the above operation, motion paths are assigned to the remaining robots in turn. The robots with lower priorities in the layer need to check for conflicts with the robots with higher priorities in the layer one by one to complete the initial motion path assignment. Then, the motion paths initially assigned to the robots are reassigned according to the inter-layer priority, that is, the initially assigned motion paths are fine-tuned. That is, the motion paths initially assigned to each robot with the second highest priority in the layer are checked for conflicts with the motion paths of each robot with the highest priority in the layer in turn. If conflicts exist, the remaining optional paths are selected. The robots with lower priorities in the layer need to check for conflicts with the robots with higher priorities in the layer one by one until a non-conflicting motion path is assigned. Different from the conventional motion path allocation strategy based on priority, the present invention expands the existing serial priority path allocation strategy into a combination of hierarchical path allocation, intra-layer priority sorting, and inter-layer conflict detection, which can effectively reduce path conflicts between different layers and improve computational efficiency.

[0046] Path conflict detection is one of the key steps to ensure the safe operation of robots. When multiple robots move in the same environment, it is crucial to avoid conflicts, especially when they share part of the path or the paths are close. In order to accurately detect whether a conflict may occur during the movement of the robots, cubic spline interpolation is performed on the robot's path to obtain the path interpolation equation; the interpolation equations of the two paths are made equal, and the equation group is solved to determine whether the two paths have an intersection. If there is no intersection, it indicates that there will be no conflict in space; if there is an intersection, it indicates that there may be a conflict in space, and it is necessary to further determine whether there will be a conflict in time, that is, to calculate the integral distance from the starting point to the intersection of the two robots respectively. Due to the different movement rates of the robots, the same path may also produce a large time difference at the intersection. Therefore, the movement rate is introduced to calculate the time difference between the two robots. If the time difference is less than the time conflict threshold, it is considered that the two paths will conflict in time. If the time difference is greater than or equal to the time conflict threshold, it is considered that the two paths will not conflict in time. The conditions for conflict are:

[0047]

[0048] Where, 、 is the integrated distance of robots A and B from the starting point to the intersection point, 、 is the movement speed of robots A and B, is the time conflict threshold.

[0049] In some complex scenarios, a robot with a lower priority might generate alternative paths that conflict with those of other robots. In this case, the robot's path can be dynamically replanned, increasing the number of alternative paths until a non-conflicting path is found. In extreme cases, the robot's priority can be dynamically adjusted. For example, if a robot with a lower priority cannot find a non-conflicting alternative path for a long time, its priority can be increased to give it priority in movement path allocation.

[0050] In order to verify the superiority of the method of the present invention, the method of the present invention is compared with the CBS algorithm. The initial path planning results of the method of the present invention and the CBS algorithm are shown in Figure 2 and 3 , which contains a total of five robot motion paths (one colored line represents the motion path of one robot, with the darker end being the starting point). In both methods, the starting and ending positions of each robot are the same. The secondary path planning is performed with the intersection of all robot motion paths at a certain intermediate moment in the initial path planning result as the starting point and the end point unchanged. The secondary path planning results of the method of the present invention and the CBS algorithm are shown in [1]. Figure 4 and 5 . Experimental results show that the method of the present invention finds a reasonable motion path in the initial path planning, and the path generated in the secondary planning is a subset of the initial path, showing consistency, ensuring the stability and continuity of the path. Although the path planning results of the CBS algorithm are reliable, they are not consistent and have a large computational overhead. The consistency characteristics of the method of the present invention make it perform particularly well in complex scenarios where frequent adjustments to the robot path are required. It can provide more stable and efficient path planning for multi-robot systems, has high adaptability and flexibility, and can effectively reduce path conflicts and improve path allocation efficiency.

[0051] Any matters not described in the present invention are applicable to the prior art.

Claims

1. A consistent path planning method for multi-robot collaborative operation, characterized in that: The method comprises the following steps: Step 1: Convert the environment map into a grid map and set the starting and ending points of each robot on the grid map; Step 2: Set the robot's priority and calculate the priority score according to the following formula: Where, is the priority score; is the task importance coefficient, Is the battery percentage, is the current priority, 、 and are weight factors; All robots are divided into layers according to the priority scores to obtain inter-layer priorities, where each inter-layer priority corresponds to a priority score interval; at the same time, the robot's intra-layer priority is determined according to the priority scores; Step 3: Based on the robot's starting point and end point, use Yen's algorithm to generate multiple optional paths for the robot and assign the robot's movement path according to priority; First, for all robots corresponding to the inter-layer priority, a motion path is assigned to each robot in turn according to the intra-layer priority, that is, the robot with the highest priority in the layer selects the shortest optional path as the motion path; the robot with the second highest priority in the layer performs conflict detection on multiple optional paths according to the alternative sorting and the motion path of the robot with the highest priority in the layer, and selects the optional path without conflict as the motion path of the robot with the second highest priority in the layer; according to the above operation, motion paths are assigned to the remaining robots in turn to complete the initial allocation of motion paths; then, the motion paths initially assigned to the robots are redistributed according to the inter-layer priority, that is, the motion paths initially assigned to each robot with the second highest inter-layer priority are sequentially detected for conflict with the motion paths of each robot with the highest inter-layer priority; if there is a conflict, the remaining optional paths are selected until a non-conflicting motion path is assigned; The conflict detection process is as follows: perform cubic spline interpolation on the robot's path to obtain the path interpolation equation; make the interpolation equations of the two paths equal, and determine whether the two paths have an intersection by solving the equation group. If there is no intersection, it indicates that there will be no conflict, so the two paths do not conflict; if there is an intersection, it indicates that there may be a conflict in space, and then determine whether there will be a conflict in time according to the following formula. If the following formula is true, it is considered that the two paths will conflict in time, so the two paths conflict; if the following formula is not true, it is considered that the two paths do not conflict in time, so the two paths do not conflict; Where, 、 is the integrated distance between the two robots from the starting point to the intersection point, 、 are the movement rates of the two robots, is the time conflict threshold.

2. The consistent path planning method for multi-robot collaborative operation according to claim 1 is characterized in that: The specific process of the first step is: use the MISSFormer model to segment each pixel of the environment map and obtain the category label of each pixel; first, calculate the probability of the pixel belonging to each category to obtain the probability distribution of the pixel; then, select the category corresponding to the maximum probability as the category label of the pixel; divide the environment map evenly into multiple grid cells, aggregate the category labels of all pixels in each grid cell separately, and obtain the category label of each grid cell; rasterize the environment map according to the category label of the network cell to obtain a raster map.

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