Solving multi-unmanned platform scheduling method under multi-task frequent mode

Abstract

The application relates to a multi-unmanned platform scheduling method under a multi-task frequent mode, and belongs to the technical field of path planning. The method comprises the following steps: assigning a polling-received new task to an unmanned platform; calculating the Manhattan distance required by the unmanned platform assigned with the new task and / or the unmanned platform with a planned non-executed task to complete the corresponding task, determining the priority of each unmanned platform based on the Manhattan distance and the running speed of the unmanned platform; and sequentially performing path planning for each unmanned platform according to the priority, which comprises the following steps: obtaining obstacle information based on the running path of an executing task and a planned task, determining whether a path scheme exists for a node in path planning to reach a next adjacent node based on the obstacle information, and determining a next reaching node from the adjacent node without conflict. The method can obtain a suboptimal solution in a short time, and solves the problems of high algorithm time cost and high priority sensitivity in the prior art.

Description

Technical Field

[0001] This invention relates to the field of path planning technology, and in particular to a method for scheduling multiple unmanned platforms under a multi-task-frequent mode. Background Technology

[0002] With the rapid development of low-cost sensors and computing devices, an increasing number of manufacturing applications are able to support concurrent control of large-scale mobile robots, unmanned vehicles, and other unmanned platforms. These large-scale unmanned platform applications often place high demands on the scheduling system's task assignment flexibility, system structural robustness, and planning effectiveness.

[0003] Currently, methods for solving the scheduling problem of multiple unmanned platforms are mainly based on two algorithms. One algorithm utilizes a higher-level search operator. Once a collision or conflict is found in the current planning scheme, it triggers a lower-level planner to replan until the higher-level search operator detects no collisions or conflicts. This method requires a high computational time cost. The other algorithm saves significantly in computational time, but due to its sequential planning characteristics, this method is highly sensitive to priority. Summary of the Invention

[0004] Based on the above analysis, the embodiments of the present invention aim to provide a scheduling method for multiple unmanned platforms in a multi-task frequent mode, in order to solve the problems of high time cost and high priority sensitivity of existing scheduling methods.

[0005] This invention provides a method for scheduling multiple unmanned platforms under a multi-task-frequent mode, comprising the following steps:

[0006] Assign new tasks received from polling to the unmanned platform;

[0007] Calculate the Manhattan distance required for each unmanned platform to complete its assigned new task and / or planned but not yet carried out task, and determine the priority of each unmanned platform based on the Manhattan distance and the operating speed of the unmanned platform;

[0008] Path planning is performed for each of the unmanned platforms in order of priority; the path planning for one of the unmanned platforms includes: obtaining obstacle information based on the running paths of the tasks in progress and the planned but not yet carried out tasks; determining whether there is a conflict in the path planning of a node to the next adjacent node based on the obstacle information; and determining the next destination node from the adjacent nodes that do not have a conflict.

[0009] Based on further improvements to the above method, the Manhattan distance required for each unmanned platform, assigned a new task and / or planned but not yet carried out a task, to complete the corresponding task is calculated, including:

[0010] Update the status of each unmanned platform as the initial status for this path planning;

[0011] The Manhattan distance required for each unmanned platform to complete its task is obtained based on the node where each unmanned platform is located in its initial state and the task it is assigned.

[0012] Based on a further improvement to the above method, the state of the unmanned platform is updated as the initial state for this path planning, including:

[0013] For unmanned platforms undertaking new tasks and / or those that have planned but not yet carried out tasks and are currently performing tasks that have already been carried out, obtain the node and time point where the unmanned platform is located after the completion of its current task as the initial state of the platform;

[0014] For unmanned platforms that have not yet started a mission, obtain the node where the platform is when the system receives a new mission as the initial state of the platform.

[0015] Based on a further improvement of the above method, the Manhattan distance is obtained by the following formula:

[0016]

[0017] Where, d Manhattan Represents the Manhattan distance value, (x c ,y c (x) represents the coordinates of the node where the unmanned platform is initially located. io ,y io (x) represents the coordinates of the i-th task starting point of the unmanned platform. id ,y id ) represents the coordinates of the i-th task endpoint of the unmanned platform, and tasknum represents the total number of tasks of the unmanned platform.

[0018] Further improvements to the above method, determining the priority of each unmanned platform based on the Manhattan distance and the operating speed of the unmanned platform, include: estimating the task completion time using the Manhattan distance and the operating speed in the unmanned platform data matrix, and determining the priority of the unmanned platforms from high to low according to the length of the task completion time.

[0019] Based on further improvements to the above method, obstacle information includes the departure time and node of the unmanned platform, the arrival time and node of the destination, the running trajectory, the nodes along the route, and the time of arrival at each node along the route.

[0020] Based on further improvements to the above method, conflicts include point conflicts and / or edge conflicts; determining whether a point conflict exists in the path planning of a path from one node to the next adjacent node based on the obstacle information includes:

[0021] The distance between a node and its next adjacent node in the path scheme of the unmanned platform and the running speed, acceleration, and deceleration in the unmanned platform data matrix are obtained to obtain the running time period; wherein, the distance is obtained from the map distance matrix;

[0022] By utilizing the departure time, arrival time, and arrival time of each unmanned platform in the obstacle information, the unmanned platforms with running trajectories in the obstacle information within the time period are obtained.

[0023] For each time point within the time period, calculate the distance between the center points of the unmanned platform and the unmanned platforms whose operating trajectories exist in the obstacle information; wherein, the distance is obtained using the nodes from which the unmanned platform departs, the nodes from which it reaches its destination, the operating trajectory, and the nodes along the route within the time period in the obstacle information;

[0024] Using the longest distance from the center point of the unmanned platform to the edge of the platform in the unmanned platform data matrix, calculate the sum of the longest distances between the two unmanned platforms;

[0025] A point conflict is considered to exist when the distance between the center points is less than or equal to the sum of the longest distance values.

[0026] Determining whether there are edge conflicts in the path planning from one node to the next adjacent node based on the obstacle information includes:

[0027] The unmanned platform is obtained to travel from one node to the next according to the path scheme, along the road and the travel time. The travel time is obtained by the distance between the starting and ending nodes and the unmanned platform's operating speed, acceleration, and deceleration.

[0028] By utilizing the departure time, arrival time, and arrival time of each unmanned platform in the obstacle information, the unmanned platforms with running trajectories in the obstacle information within the time period are obtained.

[0029] The path traveled by the unmanned platform is obtained from the nodes of departure, arrival, trajectory, and route of the unmanned platform within the time period specified in the obstacle information.

[0030] Determine whether the unmanned platform and the obstacle information within the time period enter the same road in opposite directions. If so, it is considered that there is an edge conflict.

[0031] Based on further improvements to the above method, the map and unmanned platform to be run in the task are modeled in advance to obtain the distance matrix of the map and the data matrix of the unmanned platform; the distance matrix includes the distance between each node; the data matrix of the unmanned platform includes the number of the unmanned platform, its running speed, acceleration, deceleration, and the longest distance from the center point of the unmanned platform to the edge of the platform.

[0032] Further improvements to the above method include determining the next node from non-conflicting neighboring nodes, including:

[0033] Calculate the cost value of each adjacent node that does not conflict;

[0034] The point with the smallest cost value is selected as the next node; where the cost value represents the sum of the path length from the starting point to the node and the estimated path length from the node to the destination.

[0035] Based on further improvements to the above method, new tasks received through polling are assigned to the unmanned platform, including:

[0036] Newly received tasks are arranged in chronological order of receipt and allocated using the FIFO principle. The load of each unmanned platform is calculated, and tasks are assigned to the unmanned platform with the lowest load in sequence. If multiple unmanned platforms have the lowest load, the task is assigned to the unmanned platform with the lowest number.

[0037] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0038] This invention calculates the Manhattan distance between each unmanned platform and its execution time based on the Manhattan distance and platform speed. It then prioritizes the unmanned platforms using their execution times and performs path planning accordingly. This approach reduces algorithm complexity and time cost while maintaining the suboptimal nature of the solution, making it practical. Furthermore, this solution addresses the task balancing problem in multi-unmanned platform scheduling by focusing on load balancing, ensuring nearly equal load across platforms. Finally, the path planning algorithm incorporates the time dimension during path search, avoiding collisions and conflicts during scheduling and achieving scheduling effectiveness.

[0039] In this invention, the above-described technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of this invention will be set forth in the following description, and some advantages may become apparent from the description or be learned by practicing the invention. The objects and other advantages of this invention can be realized and obtained from what is particularly pointed out in the description and drawings. Attached Figure Description

[0040] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts.

[0041] Figure 1 A flowchart illustrating the scheduling method for multiple unmanned platforms under a multi-task-frequent mode;

[0042] Figure 2 This is a flowchart of the path planning algorithm. Detailed Implementation

[0043] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0044] A specific embodiment of the present invention discloses a scheduling method for multiple unmanned platforms in a multi-task frequent operation mode, comprising the following steps:

[0045] Assign new tasks received from polling to the unmanned platform;

[0046] Calculate the Manhattan distance required for each unmanned platform to complete its assigned new task and / or planned but not yet carried out task, and determine the priority of each unmanned platform based on the Manhattan distance and the operating speed of the unmanned platform;

[0047] Path planning is performed for each of the unmanned platforms in order of priority. Path planning for one of the unmanned platforms includes: obtaining obstacle information based on the operational paths of ongoing and planned but not yet initiated tasks; determining whether there is a conflict in the path planning of a path from one node to the next adjacent node based on the obstacle information; and determining the next destination node from the adjacent nodes without conflict. Figure 1 As shown.

[0048] In practice, before receiving tasks, the scheduling system pre-models the map to be used by the task to obtain a distance matrix between nodes, and models the unmanned platform to obtain a data matrix for the unmanned platform. For example, the unmanned platform may include unmanned vehicles, unmanned boats, etc.

[0049] Specifically, modeling the map required for the task involves: inputting the coordinates of each node in the task scenario map and the distances between each node, and outputting a distance matrix representing the distances between nodes. The data in the i-th row and j-th column of the distance matrix represents the distance between the i-th node and the j-th node. Here, a node refers to a location point in the task scenario involved in multiple tasks; for nodes that are not directly connected, the distance between nodes is set to infinity. For example, in a scenario where multiple unmanned platforms collaboratively load and unload goods in a warehouse, each loading and unloading point in the warehouse is set as a node, and by default, all paths are single-lane but allow two-way traffic.

[0050] Specifically, modeling the unmanned platforms involves taking as input the platform's ID, operating speed, acceleration, deceleration, and the longest distance from its center point to its edge. The output is a data matrix for each unmanned platform. In the data matrix, the i-th row represents the i-th unmanned platform, the first column is the platform's ID, the second column is its operating speed, the third column is its acceleration, the fourth column is its deceleration, and the fifth column is the longest distance from its center point to its edge.

[0051] The scheduling system receives tasks using a polling method with a polling interval of N, for example, N is set to 1 second; when a new task is received during the polling process, path planning begins; otherwise, polling continues. For example, the system receives a new task at time T; further, the task includes a starting node and a target node.

[0052] Newly received tasks are arranged in chronological order of receipt and allocated using the FIFO (First-In, First-Out) principle. Before each task allocation, the load of each unmanned platform is recalculated, and tasks are assigned to the unmanned platform with the lowest load. If multiple unmanned platforms have the lowest load, they are arranged in ascending order of their platform numbers, and tasks are assigned to the unmanned platform with the lowest number. This process is repeated for all newly received tasks. For example, "lowest load" means that the unmanned platform handles the fewest tasks.

[0053] It should be noted that the types of tasks described in this embodiment include: new tasks, planned but not yet carried out tasks, and planned but carried out tasks. Both new tasks and planned but not yet carried out tasks can be re-planned.

[0054] The path schemes of the tasks that have been carried out are treated as obstacle information in the planning process of other unmanned platforms and added to the set obstacle. The obstacle includes the departure time and node of the unmanned platform, the arrival time and node of the destination, the running trajectory, the nodes along the way, and the time of arrival at each node along the way. The planned but not carried out tasks in the obstacle are deleted. All unmanned platforms containing new tasks and / or planned but not carried out tasks are added to the set open-set.

[0055] It should be noted that updating the position of each unmanned platform in the open-set as the initial state includes: for unmanned platforms undertaking new tasks and / or those that have planned but not yet started tasks and are currently executing tasks, obtaining the node and time point where the unmanned platform is located after the completion of its current task as the initial state of the platform; for unmanned platforms that have not started tasks, obtaining the node where the unmanned platform is located at time T as the initial state of the platform.

[0056] Specifically, for each unmanned platform in the open set, the task execution order of the unmanned platform is determined according to the FIFO principle, thereby obtaining the order of arrival at each target node. The Manhattan distance required for an unmanned platform in the open set to complete all its assigned tasks is calculated using the following formula:

[0057]

[0058] Where, d Manhattan Represents the Manhattan distance value, (x c ,y c (x) represents the coordinates of the node where the unmanned platform is initially located. io ,y io (x) represents the coordinates of the i-th task starting point of the unmanned platform. id ,y id ) represents the coordinates of the i-th task endpoint of the unmanned platform, and tasknum represents the total number of tasks of the unmanned platform.

[0059] Furthermore, the time it takes for the platform to complete all its assigned tasks is estimated using the Manhattan distance and the speed of the unmanned platform, where the speed is obtained from the unmanned platform's data matrix; the unmanned platforms in the open set are prioritized according to the length of their task completion time, with unmanned platforms having longer task completion times having higher priority.

[0060] Path planning is performed on each unmanned platform according to its priority from high to low. Specifically, the tasks assigned to each platform are planned sequentially based on their initial allocation. The path planning algorithm includes the following steps:

[0061] 1. Initialize the starting node as s and the target node as g, and put s into the set clo_set; set the variable current to the initial value of s, and the initial state of current is...

[0062] [Tc,localization], where Tc represents the time when the unmanned platform arrives at the current point, the initial value of Tc is the time when the platform is in its initial state, and localization represents the node currently set by current;

[0063] 2. Obtain the current neighboring nodes (including those waiting in place, i.e., the current point itself), and update the state of each neighboring point to [Tc+tn, localization], where tn is the estimated time for the unmanned platform to travel from the starting point to that point. If it is waiting in place, the estimated time is set to 1 second. The estimated time is calculated from the distance from the starting point to that point and the speed of the unmanned platform. The distance is obtained through the map matrix. The speed includes the acceleration, deceleration, and running speed recorded in the unmanned platform matrix.

[0064] 3. Iterate through the nodes adjacent to current in sequence and check whether there is a conflict between the path of the unmanned platform from current to the adjacent node and the path scheme in the obstacle;

[0065] For example, point conflicts for an unmanned platform to reach the adjacent node from the current node include:

[0066] The distance between a node and its next adjacent node in the path scheme of the unmanned platform is obtained, along with the running speed, acceleration, and deceleration in the unmanned platform data matrix, to obtain the running time period; wherein, the distance is obtained from the map distance matrix;

[0067] By using the departure time, arrival time, and arrival time of each unmanned platform in the obstacle to obtain the unmanned platforms with running trajectories in the obstacle during the time period;

[0068] For each time point within the time period, calculate the distance between the center points of the unmanned platform and the unmanned platforms with running trajectories in the obstacle; wherein, the distance is obtained using the nodes where the unmanned platform departs, the nodes where it reaches its destination, the running trajectory, and the nodes along the route within the time period in the obstacle;

[0069] Using the longest distance from the center point of the unmanned platform to the edge of the platform in the unmanned platform data matrix, calculate the sum of the longest distances between the two unmanned platforms;

[0070] A point conflict is considered to exist when the distance between the center points is less than or equal to the sum of the longest distance values.

[0071] For example, edge conflicts for an unmanned platform to reach the adjacent node from the current node include:

[0072] The unmanned platform obtains the road it travels from one node to the next according to the path scheme and the travel time of the travel on the road; wherein, the travel time is obtained by the distance between the starting and ending nodes and the operating speed, acceleration and deceleration of the unmanned platform;

[0073] By using the departure time, arrival time, and arrival time of each unmanned platform in the obstacle to obtain the unmanned platforms with running trajectories in the obstacle during the time period;

[0074] The path traveled by the unmanned platform in the obstacle is obtained by using the nodes of departure, arrival, trajectory, and route of the unmanned platform within the time period specified in the obstacle.

[0075] Determine whether the unmanned platform and the unmanned platform in the obstacle enter the same road in opposite directions during the time period. If so, it is considered that there is an edge conflict.

[0076] 4. Add any neighboring nodes without any conflicts to the set neighbor_list, update the cost value of each node in neighbor_list, where the cost value represents the sum of the path length from the starting point to the point and the estimated path length from the point to the destination, and add the points in neighbor_list to ope_set;

[0077] 5. Determine if the end point has been found. If the end point has been found, end the current search; otherwise, select the point with the lowest cost in ope_set, add the point to clo_set and set current=point, and repeat steps 2 to 6.

[0078] Furthermore, the unmanned platform with completed planning is added to the close_set and removed from the open_set; since the planned path trajectory will be regarded as an obstacle by other vehicles, the calculated path of the unmanned platform is updated to the obstacle; the planned path is stored as motion instructions.

[0079] If open-set is not empty, repeat steps 1 to 6 to plan the paths of the unmanned platforms in open_set; if open-set is empty, end the calculation, and the path planning for all unmanned platforms is now complete.

[0080] Next, the newly planned path will be sent to the corresponding unmanned platform in close_set through the scheduling system.

[0081] Compared with existing technologies, the solution provided in this embodiment offers a method for scheduling multiple unmanned platforms under a multi-task-frequent mode. This method calculates the Manhattan distance between each unmanned platform and the task execution time based on the Manhattan distance and platform speed. It then prioritizes the unmanned platforms based on their execution time and performs path planning according to priority. This approach reduces the complexity and time cost of the algorithm while ensuring the suboptimal nature of the solution, making it practical. Furthermore, this solution aims at load balancing, solving the task balancing problem in the scheduling process of multiple unmanned platforms, resulting in nearly equal loads among them. Moreover, the path planning algorithm used in this solution incorporates the time dimension when searching for paths, avoiding collisions and conflicts during the scheduling process and achieving scheduling effectiveness.

[0082] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0083] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A scheduling method for multiple unmanned platforms under a multi-task frequent occurrence mode, characterized in that, Includes the following steps: Assign new tasks received from polling to the unmanned platform; Calculate the Manhattan distance required for each unmanned platform to complete its assigned new task and / or planned but not yet carried out task, and determine the priority of each unmanned platform based on the Manhattan distance and the operating speed of the unmanned platform; Path planning is performed for each of the aforementioned unmanned platforms in order of priority; right One of the path planning methods for an unmanned platform includes: obtaining obstacle information based on the operation paths of ongoing tasks and planned but not yet carried out tasks; determining whether there is a conflict in the path planning of a path from one node to the next adjacent node based on the obstacle information; and determining the next destination node from the adjacent nodes that do not have a conflict. The obstacle information includes the departure time and node of the unmanned platform, the arrival time and node of the destination, the running trajectory, the nodes along the way, and the time of arrival at each node along the way. The conflicts include point conflicts and / or edge conflicts; determining whether a point conflict exists in the path planning from a node to its next adjacent node based on the obstacle information includes: The distance between a node and its next adjacent node in the path scheme of the unmanned platform and the running speed, acceleration, and deceleration in the unmanned platform data matrix are obtained to obtain the running time period; wherein, the distance is obtained from the map distance matrix; By utilizing the departure time, arrival time, and arrival time of each unmanned platform in the obstacle information, the unmanned platforms with running trajectories in the obstacle information within the time period are obtained. For each time point within the time period, calculate the distance between the center points of the unmanned platform and the unmanned platforms whose operating trajectories exist in the obstacle information; wherein, the distance is obtained using the nodes from which the unmanned platform departs, the nodes from which it reaches its destination, the operating trajectory, and the nodes along the route within the time period in the obstacle information; Using the longest distance from the center point of an unmanned platform to its edge in the unmanned platform data matrix, calculate the sum of the longest distances between two unmanned platforms; A point conflict is considered to exist when the distance between the center points is less than or equal to the sum of the longest distance values. Determining whether there are edge conflicts in the path planning from one node to the next adjacent node based on the obstacle information includes: The unmanned platform is obtained to travel from one node to the next according to the path scheme, along the road and the travel time. The travel time is obtained by the distance between the starting and ending nodes and the unmanned platform's operating speed, acceleration, and deceleration. By utilizing the departure time, arrival time, and arrival time of each unmanned platform in the obstacle information, the unmanned platforms with running trajectories in the obstacle information within the time period are obtained. The path traveled by the unmanned platform is obtained from the nodes of departure, arrival, trajectory, and route of the unmanned platform within the time period specified in the obstacle information. Determine whether the unmanned platform and the obstacle information within the time period enter the same road in opposite directions. If so, it is considered that there is an edge conflict.

2. The scheduling method for multiple unmanned platforms under frequent multi-task operation mode according to claim 1, characterized in that, The calculation of the Manhattan distance required for each unmanned platform, assigned a new task and / or planned but not yet carried out a task, to complete the corresponding task includes: Update the status of each unmanned platform as the initial status for this path planning; The Manhattan distance required for each unmanned platform to complete its task is obtained based on the node where each unmanned platform is located in its initial state and the task it is assigned.

3. The scheduling method for multiple unmanned platforms under a multi-task frequent occurrence mode according to claim 2, characterized in that, The process of updating the state of the unmanned platform as the initial state for this path planning includes: For unmanned platforms undertaking new tasks and / or those that have planned but not yet carried out tasks and are currently performing tasks that have already been carried out, obtain the node and time point where the unmanned platform is located after the completion of its current task as the initial state of the platform; For unmanned platforms that have not yet started a mission, obtain the node where the platform is when the system receives a new mission as the initial state of the platform.

4. The scheduling method for multiple unmanned platforms under a multi-task frequent occurrence mode according to claim 2 or 3, characterized in that... The Manhattan distance is obtained by the following formula: in, Represents the Manhattan distance value. This indicates the coordinates of the node where the unmanned platform is initially located. The unmanned platform is described as the first The coordinates of the starting point of each task. The unmanned platform is described as the first The coordinates of the endpoint of each task. This indicates the total number of tasks performed by the unmanned platform.

5. The scheduling method for multiple unmanned platforms under a multi-task frequent occurrence mode according to claim 1, characterized in that, The process of determining the priority of each unmanned platform based on the Manhattan distance and the operating speed of the unmanned platform includes: estimating the task completion time using the Manhattan distance and the operating speed in the unmanned platform data matrix, and determining the priority of the unmanned platforms from high to low according to the length of the task completion time.

6. The scheduling method for multiple unmanned platforms under a multi-task frequent occurrence mode according to any one of claims 1 or 5, characterized in that, The map and unmanned platform to be operated in the task are modeled in advance to obtain the distance matrix of the map and the data matrix of the unmanned platform. The distance matrix includes the distance between each node. The data matrix of the unmanned platform includes the number of the unmanned platform, its running speed, acceleration, deceleration, and the longest distance from the center point of the unmanned platform to the edge of the platform.

7. The scheduling method for multiple unmanned platforms under a multi-task frequent occurrence mode according to claim 1, characterized in that, Determining the next node from non-conflicting neighboring nodes includes: Calculate the cost value of each adjacent node that does not conflict; The point with the smallest cost value is selected as the next node; where the cost value represents the sum of the path length from the starting point to the node and the estimated path length from the node to the destination.

8. The scheduling method for multiple unmanned platforms under a multi-task frequent occurrence mode according to claim 1, characterized in that, Assigning newly received tasks to the unmanned platform includes: Newly received tasks are arranged in chronological order of receipt and allocated using the FIFO principle. The load of each unmanned platform is calculated, and tasks are assigned to the unmanned platform with the lowest load in sequence. If multiple unmanned platforms have the lowest load, the task is assigned to the unmanned platform with the lowest number.

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