Multi-unmanned aerial vehicle cooperative route planning method and system for unmanned aerial vehicles

Abstract

The invention discloses a multi-unmanned aerial vehicle cooperative route planning method and system for unmanned aerial vehicles, and relates to the technical field of aviation route planning, and the method comprises the steps: forming a circling circle based on a designated operation region, obtaining patrol parameters of a flight path of the unmanned aerial vehicle according to the tangency point radius of circling, the number of circling turns and the circling direction, carrying out the obstacle avoidance of a no-fly zone, and meanwhile, carrying out the obstacle avoidance of the unmanned aerial vehicle; adopting a DUBINS curve to complete obstacle avoidance of a transition section for an air route which does not meet the dynamic constraint; based on the planned route of the patrol task, the multiple unmanned aerial vehicles are controlled to avoid the threat area, and target strike is achieved from different designated directions at the same time and the shortest path. According to the method, the technical design of multi-aircraft cooperative route planning of the unmanned aerial vehicle is completed, the patrol task is met, the execution of the multi-aircraft cooperative attack task is also met, and a new technical enlightenment is provided for the unmanned aerial vehicle technology.

Description

Technical Field

[0001] The present invention relates to the technical field of aviation path planning, and in particular to a multi-machine collaborative route planning method and system for unmanned aerial vehicles. Background Art

[0002] With the rapid development and widespread application of UAV technology, multi-drone collaborative operations and mission execution have become a hot topic of research. Especially in complex environments, when multiple UAVs work together to complete tasks, how to achieve efficient and safe route planning is a key issue. Traditional single-drone route planning methods are difficult to meet the needs of multi-drone collaborative tasks, especially in complex mission environments, multi-target conflicts, and tight mission time. The independent planning of a single UAV often leads to mission delays or even failures. Summary of the invention

[0003] In order to solve the above problems, the present invention provides a multi-machine collaborative route planning method for unmanned aerial vehicles, comprising the following steps:

[0004] In response to controlling the UAV to perform patrol tasks in the designated operation area, a circling circle is formed based on the designated operation area. According to the tangent radius, number of circling circles and circling direction of the circling, the patrol parameters of the flight trajectory of the UAV are obtained, and obstacle avoidance is performed in the no-fly zone. At the same time, the DUBINS curve is used to complete the obstacle avoidance in the transition section for the route that does not meet the dynamic constraints;

[0005] In response to controlling drones to perform multi-machine coordinated strike missions in designated operating areas, based on the planned routes of patrol missions, multiple drones are controlled to avoid threat areas and achieve target strikes from different designated directions with the same time and shortest path.

[0006] Preferably, in the process of performing patrol tasks, based on the circling point of the circling circle and according to the patrol time, a patrol route for the designated operating area is planned within the interval of the minimum parameter set and the maximum parameter set, wherein the minimum parameter set information includes the take-off point, circling tangent point and patrol time information; the maximum parameter set information includes the take-off point, turning point, circling tangent point, patrol time, circular no-fly zone, polygonal no-fly zone, flyable airspace, landing point, minimum turning radius and shortest distance information.

[0007] Preferably, in the process of planning patrol routes, for UAVs carrying strike payloads, a single-machine strike route is planned by setting strike targets based on the minimum parameter set and maximum parameter set used for strike route planning, wherein the minimum parameter set for strike route planning includes take-off point, strike point and payload information; the maximum parameter set for strike route planning includes take-off point, turning point, strike point, payload information, circular no-fly zone, polygonal no-fly zone, flyable airspace, landing point, minimum turning radius and shortest distance information.

[0008] Preferably, in the process of planning patrol routes, for multiple UAVs carrying strike payloads, by setting strike targets, based on the planning method of single-machine strike routes, multiple strike routes that meet the load requirements and are applied to multiple UAVs under various constraints are planned.

[0009] Preferably, in the process of avoiding obstacles in the no-fly zone, an improved A* algorithm is used for path planning to avoid obstacles in the no-fly zone, wherein the Manhattan distance is used as the heuristic function H(n) of the A* algorithm to improve the A* algorithm.

[0010] Preferably, in the process of generating a threat avoidance route by using an improved A* algorithm, an optimal path planning is performed based on a grid based on the improved A* algorithm, and redundant points in the middle are optimized by collinear optimization to avoid obstacles in the no-fly zone.

[0011] Preferably, in the process of avoiding obstacles in a no-fly zone, static path planning is first performed, and the turning points of the path are detected using second-order derivatives on the planned route. Then, starting from the starting point, every turning point is connected to determine whether there is an obstacle on the line; if so, the original path is retained; if not, the original path is deleted and the connection is retained until the end point is determined.

[0012] Preferably, in the process of performing path planning by using the improved A* algorithm, an A* grid is constructed according to the information of the starting point, the ending point, the obstacle, and the task area;

[0013] Run the improved A* algorithm based on the generated A* grid information to perform global obstacle avoidance and generate a feasible obstacle avoidance path;

[0014] Perform collinear optimization according to the generated feasible obstacle avoidance path to generate a feasible path based on collinear optimization;

[0015] Redundant inflection points are optimized according to the path generated by the collinear optimization to generate a feasible path based on the redundant inflection point optimization.

[0016] The present invention discloses a multi-machine collaborative route planning system for unmanned aerial vehicles, which is used to implement the multi-machine collaborative route planning method for unmanned aerial vehicles mentioned above. The system includes:

[0017] The patrol mission planning module is used to form a hovering circle based on the specified operation area, obtain the patrol parameters of the UAV's flight trajectory according to the tangent radius, number of hovering circles and hovering direction, and avoid obstacles in the no-fly zone. At the same time, the DUBINS curve is used to complete the obstacle avoidance in the transition section for the route that does not meet the dynamic constraints;

[0018] The multi-machine coordinated strike module is used to plan routes based on patrol missions, control multiple drones to avoid threat areas, and achieve target strikes from different designated directions with the same time and shortest path.

[0019] The present invention discloses the following technical effects:

[0020] The present invention completes the technical design of multi-machine cooperative route planning for UAVs, which can meet the execution of multi-machine cooperative strike missions while meeting patrol missions, and provides new technical inspiration for UAV technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 is a flow chart of the patrol algorithm of the present invention;

[0023] Figure 2 It is a flow chart of the single-machine coordinated attack algorithm described in the present invention;

[0024] Figure 3 It is a flow chart of the multi-machine coordinated strike algorithm described in the present invention;

[0025] Figure 4 is a flow chart of the A* algorithm described in the present invention;

[0026] Figure 5 is the optimization flow chart of the present invention;

[0027] Figure 6 This is a flow chart for eliminating redundant inflection points according to the present invention. DETAILED DESCRIPTION

[0028] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0029] like Figure 1-6 As shown, the present invention provides a multi-machine cooperative route planning method for unmanned aerial vehicles, including a patrol algorithm and a cooperative strike algorithm;

[0030] On the one hand, the patrol algorithm requires the aircraft to complete the patrol mission above the designated operating area. For the patrol algorithm, the four points of the circling circle are given. The algorithm needs to calculate the three parameters of the tangent radius, the number of circling circles and the circling direction based on the four points to determine the patrol parameters of the aircraft's flight trajectory. After determining the patrol parameters, the optimized A* algorithm completes the obstacle avoidance in the no-fly zone, and the DUBINS curve is used to complete the obstacle avoidance in the transition section for routes that do not meet the dynamic constraints.

[0031] On the other hand, the task of multi-aircraft coordinated strike requires that multiple aircraft be able to avoid the threat area and eventually enter the bombing start area from different designated directions, at the same time and with the shortest path, and assist powered bombs to achieve target strikes.

[0032] The patrol algorithm requires carrying one or more drones, and automatically planning a patrol route that meets the patrol mission requirements based on the four hovering points and patrol time. The algorithm needs to be able to plan patrol routes within the minimum parameter set and the maximum parameter set. The minimum parameter set information required by the algorithm includes take-off point, hovering point, and patrol time information. The maximum parameter set information required by the algorithm includes take-off point, turning point, hovering point, patrol time, circular no-fly zone, polygonal no-fly zone, flyable airspace, landing point, minimum turning radius, and shortest distance information.

[0033] For the take-off point information, the algorithm needs to guide with the take-off point as the starting point; for the circling tangent point information, the algorithm needs to complete the generation of the patrol route based on the circling tangent point; for the patrol time information, the patrol route segment designed by the algorithm needs to meet the time requirement; for the landing point information, the algorithm needs to guide with the landing point as the end point; for the turning point information, the algorithm needs to guide along the input turning point sequence; for the circular no-fly zone and polygonal no-fly zone information, the trajectory generated by the algorithm needs to avoid the no-fly zone; for the flyable airspace information, all waypoints in the trajectory generated by the algorithm need to be located in the flyable airspace; for the minimum turning radius information, all waypoints in the trajectory generated by the algorithm need to meet the minimum turning radius constraint; for the shortest distance information, the distance between adjacent waypoints in the trajectory generated by the algorithm must be greater than the shortest distance. The detailed information of the minimum parameter set and the maximum parameter set are shown in Tables 1 and 2 below.

[0034] Table 1 Minimum parameter set table

[0035] Serial number content illustrate Take-off point Contains the latitude and longitude information of the take-off point Spiral Tangent The longitude and latitude information of all spiral intersection points Patrol time The time required to perform the patrol mission phase

[0036] Table 2 Maximum parameter set table

[0037]

[0038]

[0039] The single-machine strike algorithm requires the drone carrying the strike payload to automatically plan a strike route that meets the payload requirements and various constraints for the set strike target. The algorithm needs to be able to plan a single-machine strike route within the minimum parameter set and the maximum parameter set. The minimum parameter set information required by the algorithm includes the take-off point, strike point, and payload information. The maximum parameter set information required by the algorithm includes the take-off point, turning point, strike point, payload information, circular no-fly zone, polygonal no-fly zone, flyable airspace, landing point, minimum turning radius, and shortest distance information.

[0040] For take-off point information, the algorithm needs to guide with the take-off point as the starting point; for strike point information, the algorithm needs to complete the generation of strike route based on strike point; for load information, the algorithm needs to design the strike route in combination with the working principle of load; for landing point information, the algorithm needs to guide with the landing point as the end point; for turning point information, the algorithm needs to guide along the input turning point sequence; for circular no-fly zone and polygonal no-fly zone information, the trajectory generated by the algorithm needs to avoid the no-fly zone; for flyable airspace information, all waypoints in the trajectory generated by the algorithm need to be located in the flyable airspace; for minimum turning radius information, all waypoints in the trajectory generated by the algorithm need to meet the minimum turning radius constraint; for shortest distance information, the distance between adjacent waypoints in the trajectory generated by the algorithm must be greater than the shortest distance. The detailed information of the minimum parameter set and the maximum parameter set are shown in Table 3 and Table 4.

[0041] Table 3 Minimum parameter set table

[0042] Serial number content illustrate Take-off point Contains the latitude and longitude information of the take-off point Hit Point Latitude and longitude information of all strike points Load information Load type, load parameters

[0043] Table 4 Maximum parameter set table

[0044] Serial number content illustrate Take-off point Contains the latitude and longitude information of the take-off point Turning Point Contains the latitude and longitude information of the turning point Point target information Latitude and longitude information of all strike points Load information Load type, load parameters Circular No Fly Zone Contains the center and radius information of the circular no-fly zone Polygonal No Fly Zone Contains the latitude and longitude information of all vertices of the polygonal no-fly zone Flyable area Contains the latitude and longitude information of all flyable areas Landing point Contains the latitude and longitude information of the landing point Minimum turning radius Contains the minimum turning radius information corresponding to the aircraft Shortest distance Contains the shortest distance information between adjacent waypoints required by the aircraft

[0045] The multi-machine coordinated strike algorithm requires drones carrying strike payloads to automatically plan multiple strike routes that meet the payload requirements and various constraints for the set strike targets. The algorithm needs to be able to plan multi-machine coordinated strike routes within the minimum parameter set and the maximum parameter set. The minimum parameter set information required by the algorithm includes take-off point, strike point, and payload information. The maximum parameter set information required by the algorithm includes take-off point, turning point, strike point, payload information, circular no-fly zone, polygonal no-fly zone, flyable airspace, landing point, minimum turning radius, and shortest distance information.

[0046] For take-off point information, the algorithm needs to guide with the take-off point as the starting point; for strike point information, the algorithm needs to complete the generation of strike route based on strike point; for load information, the algorithm needs to design strike route in combination with the working principle of load; for landing point information, the algorithm needs to guide with the landing point as the end point; for turning point information, the algorithm needs to guide along the input turning point sequence; for circular no-fly zone and polygonal no-fly zone information, the trajectory generated by the algorithm needs to avoid the no-fly zone; for flyable airspace information, all waypoints in the trajectory generated by the algorithm need to be located in the flyable airspace; for minimum turning radius information, all waypoints in the trajectory generated by the algorithm need to meet the minimum turning radius constraint; for shortest distance information, the distance between adjacent waypoints in the trajectory generated by the algorithm must be greater than the shortest distance. The detailed information of the minimum parameter set and the maximum parameter set are shown in Table 5 and Table 6.

[0047] Table 5 Minimum parameter set table

[0048] Serial number content illustrate Take-off point Contains the latitude and longitude information of the take-off point Hit Point Latitude and longitude information of all strike points Load information Load type, load parameters

[0049] Table 6 Maximum parameter set table

[0050]

[0051]

[0052] In view of the requirements of the above patrol algorithm, the patrol algorithm flow chart is designed as shown in Figure 1. The patrol algorithm first obtains input information to determine whether there is a turning point after the take-off point. If so, the aircraft is guided to the first circling tangent point in the order of the turning points. At this time, the information of the four circling tangent points is obtained from the algorithm input information, and the patrol algorithm is called to calculate the circling radius, direction and number of circles to generate a circling tangent point that meets the input requirements. At this time, it is determined whether there is a turning point after the circling tangent point. If so, the aircraft is guided in sequence in the order of the turning points. At this time, it is determined whether there is a landing point. If so, the aircraft is guided to the landing point along the last turning point. Then all the above waypoints are connected in sequence to generate the final route point. At this time, it is determined whether there is a shortest distance constraint. If so, the adjacent waypoints in the above generated route are taken in turn to see if they meet the shortest distance constraint. If not, the point that meets the shortest distance is selected on the median line of the two points, and it is iterated until the last waypoint is reached. At this time, it is determined whether there is a flyable area constraint. If so, the adjacent waypoints in the above-generated route are taken in turn to see whether they meet the flyable area constraint. If not, the two areas where the adjacent waypoints are located are judged separately, and the overlapping area of ​​the two areas is calculated. The center point of the overlapping area is taken as the insertion point, and this iteration is carried out until the last waypoint. At this time, it is determined whether there is a no-fly zone. If so, the adjacent waypoints in the above-generated route are taken in turn to see whether they meet the no-fly zone constraint. If not, the optimized A-star algorithm is called to generate avoidance waypoints, and this iteration is carried out until the last waypoint. At this time, it is determined whether there is a minimum turning radius constraint. If so, the transition section route planning algorithm is called to generate waypoints, and this iteration is carried out until the last waypoint. After the above design, the algorithm can generate a patrol route that meets the requirements for any parameter set within the interval between the minimum parameter set and the maximum parameter set.

[0053] In view of the requirements of the above-mentioned single-machine attack algorithm, a single-machine attack algorithm flow chart is now designed as follows: Figure 2As shown. The single-aircraft strike algorithm first obtains input information, determines whether there is a turning point after the take-off point, and if so, guides the aircraft to the first strike point in the order of the turning points. At this time, the traditional single-aircraft strike algorithm is called to generate a single-aircraft strike route. At this time, it is determined whether there is a turning point after the strike point. If so, the aircraft is guided in sequence in the order of the turning points. At this time, it is determined whether there is a landing point. If so, the aircraft is guided to the landing point along the last turning point. Then all the above waypoints are connected in sequence to generate the final route point. At this time, it is determined whether there is a shortest distance constraint. If so, the adjacent waypoints in the above-generated route are taken in turn to see whether they meet the shortest distance constraint. If not, the point that meets the shortest distance is selected on the median line of the two points, and it is iterated until the last waypoint is reached. At this time, it is determined whether there is a flyable area constraint. If so, the adjacent waypoints in the above-generated route are taken in turn to see whether they meet the flyable area constraint. If not, the two areas where the adjacent waypoints are located are determined respectively, and the overlapping area of ​​the two areas is calculated. The center point of the overlapping area is taken as the insertion point, and it is iterated until the last waypoint is reached. At this time, determine whether there is a no-fly zone. If so, then check the adjacent waypoints in the above generated route to see if they meet the no-fly zone constraints. If not, call the optimized A-star algorithm to generate avoidance waypoints, and iterate until the last waypoint. At this time, determine whether there is a minimum turning radius constraint. If so, call the transition segment route planning algorithm to generate waypoints, and iterate until the last waypoint. After the above design, the algorithm can generate a single-aircraft strike route that meets the requirements for any parameter set within the interval between the minimum parameter set and the maximum parameter set.

[0054] In view of the requirements of the above-mentioned multi-machine coordinated strike algorithm, a multi-machine coordinated strike algorithm flow chart is now designed as follows: Figure 3As shown. The multi-aircraft coordinated strike algorithm first obtains input information, starting from the first drone, and determines whether there is a turning point after the take-off point. If so, the aircraft is guided to the first strike point in the order of the turning points, and traverses in sequence until all aircraft are guided to the first strike point. At this time, the previous waypoint of all aircraft is obtained as the initial position of the aircraft, and then the traditional multi-aircraft coordinated strike algorithm is called to generate the strike routes of different aircraft. Starting from the first drone again, it is determined whether there is a turning point after the strike point. If so, the aircraft is guided in sequence in the order of the turning points. At this time, it is determined whether there is a landing point. If so, the aircraft is guided to the landing point along the last turning point. Then all the above waypoints are connected in sequence to generate the final route point. At this time, it is determined whether there is a shortest distance constraint. If so, the adjacent waypoints in the above generated route are taken in turn to see if they meet the shortest distance constraint. If not, the point that meets the shortest distance is selected on the median line of the two points, and it is iterated until the last waypoint is reached. At this time, it is determined whether there is a constraint on the flyable area. If so, the adjacent waypoints in the above-generated route are taken in turn to see whether they meet the constraint on the flyable area. If not, the two areas where the adjacent waypoints are located are determined respectively, and the overlapping area of ​​the two areas is calculated. The center point of the overlapping area is taken as the insertion point, and the iteration is repeated until the last waypoint. At this time, it is determined whether there is a no-fly zone. If so, the adjacent waypoints in the above-generated route are taken in turn to see whether they meet the no-fly zone constraint. If not, the optimized A-star algorithm is called to generate avoidance waypoints, and the iteration is repeated until the last waypoint. At this time, it is determined whether there is a minimum turning radius constraint. If so, the transition section route planning algorithm is called to generate waypoints, and the iteration is repeated until the last waypoint. Traverse in sequence until all aircraft generate all waypoints after the attack route is generated according to the above process. At this time, the route conflict detection algorithm is called to determine whether there is a conflict between the waypoints of all drones. If there is a conflict, the route conflict resolution algorithm is called to resolve the conflicting route. After the above design, the algorithm can meet the requirements of generating a multi-machine coordinated strike route that meets the requirements with any parameter set within the interval between the minimum parameter set and the maximum parameter set.

[0055] In the above algorithm, the threat avoidance algorithm optimization and transition segment route planning algorithm design used are as follows:

[0056] The A* algorithm is a heuristic search algorithm based on a grid map, which can quickly search for the shortest path from a specified node to a target node while avoiding collisions between the drone and obstacles. Therefore, the traditional threat avoidance algorithm generally uses the A* algorithm to complete the generation of threat avoidance routes.

[0057] The salient feature of this method is that it uses a heuristic function to evaluate the cost of expanding a certain node, providing an optimal direction for node search, thereby greatly improving the search efficiency and effectively reducing useless searches during the search process. Therefore, the A* algorithm has the advantages of high efficiency and strong optimality, and as long as there is a feasible solution, A* can search it out to ensure that the result can be solved. By transforming the actual map, a transformed grid map can be obtained. In the grid map, the center of each grid is used as a node, starting from the initial node and gradually expanding outward. In the process of each outward expansion from the current node, it is necessary to calculate the cost function of the node to the surrounding nodes, and then select the node with the smallest cost function from all the surrounding reachable nodes as the next node until it is expanded to the target node, thereby obtaining the optimal path. For the A* algorithm, the selection of the cost function is very important, and the cost function F(n) can be expressed as:

[0058] F(n)=G(n)+H(n);

[0059] In the above formula, n represents the current node to be evaluated, G(n) represents the actual cost from the starting point to the node to be evaluated, and H(n) represents the cost from the node to be evaluated to the target node. The cost here refers to the distance. In the expansion process, H(n) provides guidance on the search direction for the current node, which is inspiring, so it is also called the heuristic term. The more reasonable the design of H(n), the more F(n) can represent the true value, and the closer the obtained path is to the true optimal path. However, the calculation of H(n) will affect the running speed of the algorithm to a certain extent, so the selection of F(n) needs to be determined according to specific needs. The following are two common ways to select heuristic functions:

[0060] Manhattan distance: Assume that the coordinates of the node to be evaluated and the target node are (X 1 , Y 1 ) and (X 2 , Y 2 ), then the Manhattan distance is the sum of the distances in the X and Y directions between two squares, that is:

[0061] H(n)=|X 2 -X 1 |+|Y 2 -Y 1 |;

[0062] Euclidean distance: the straight-line distance between the node to be evaluated and the target node, that is,

[0063]

[0064] Manhattan distance is relatively simple to calculate and has a fast calculation speed. However, Manhattan distance does not accurately estimate the diagonal movement of nodes, which may cause the heuristic cost from the current node to the target node to be greater than the actual cost, and the final path may not be optimal. Although the Euclidean path can provide a more accurate estimate of the cost of reaching the target node, the calculation complexity is high, which increases the calculation time. Therefore, these two heuristic functions have their own advantages and disadvantages, and they need to be reasonably selected according to actual needs. In this paper, in order to ensure that the calculation results are closer to the actual optimal results, the Euclidean distance is used as the heuristic function for the search.

[0065] In addition, during the extended search process, multiple nodes may have the same cost function value at the same time, which will have a certain impact on the program running efficiency. This can be improved by increasing the proportion of the heuristic function H(n) in the cost function F(n):

[0066] F(n)=G(n)+k*H(n);

[0067] In the above formula, k is an improvement factor, which is generally a constant slightly greater than 1. This approach can make the search process more "inspiring", effectively reduce the outward expansion range, and improve program operation efficiency.

[0068] The A* algorithm is implemented by maintaining two lists, OpenList and CloseList. OpenList stores the nodes to be examined during the expansion process, and CloseList stores the nodes that have been examined. The overall process of the A* algorithm is as follows: Figure 4 shown.

[0069] Since the A* algorithm is a path planning algorithm based on grid optimization, the path obtained by the A* algorithm is obtained on the basis of each grid, so many collinear path points are generated, but these collinear path points are useless to us. We only need to know the starting point and end point of the path, and do not need to know the points in the middle. Therefore, we can optimize the redundant points in the middle through collinear optimization. The optimization flowchart is as follows: Figure 5 As shown:

[0070] Due to the heuristic function of the A* algorithm, the A* algorithm search has a sense of direction, which can reduce a lot of calculations. However, in some specific cases, this clear sense of direction will lead to the generation of redundant corners. Using Manhattan distance as the estimated cost h(x) of the A* algorithm is a common method for solving path planning problems, and it is relatively accurate, but due to the sense of direction of the search, redundant corners will be generated. In order to reduce redundant corners and make the path shorter, the path planning of the A* algorithm is improved.

[0071] First, perform static path planning. Use the second-order derivative to detect the path inflection points on the planned route. Then, connect every inflection point from the starting point to determine whether there is an obstacle on the connection line. If there is, keep the original path; if not, delete the original path and keep the connection until the end point is determined. Eliminate redundant inflection points. Figure 6 As shown:

[0072] The design of threat avoidance algorithm is mainly divided into the following four steps.

[0073] Step 1: Construct the A-star grid based on the starting point, end point, obstacle information, and mission area information.

[0074] Step 2: Run the A-star algorithm for global obstacle avoidance based on the generated A-star grid information to generate a feasible obstacle avoidance path.

[0075] Step 3: Perform collinear optimization based on the generated feasible obstacle avoidance path to generate a feasible path based on collinear optimization.

[0076] Step 4: Optimize redundant inflection points based on the path generated by collinear optimization to generate a feasible path based on redundant inflection point optimization.

[0077] The coordinate information of the current starting point of the aircraft is known, that is: (x_s, y_s), where x_s is the horizontal coordinate of the starting point, and y_s is the vertical coordinate of the starting point; the coordinate information of the end point is known, that is: (x_e, y_e), where x_e is the horizontal coordinate of the end point, and y_e is the vertical coordinate of the end point; the coordinate information of the lower left corner of the mission area is known, that is: (x_leftbottom, y_leftbottom), where x_leftbottom is the horizontal coordinate of the lower left corner of the mission area, and y_leftbottom is the vertical coordinate of the lower left corner of the mission area; the size information of the mission area is known, that is: (dis_horizon, dis_vertical), where dis_horizon is the horizontal distance of the mission area, d is_vertical is the vertical distance of the mission area; circular obstacle coordinate information, i.e. (x_circle_i, y_circle_i, r_i), where x_circle_i is the horizontal coordinate of the center of the i-th circular obstacle, y_circle_i is the vertical coordinate of the center of the i-th circular obstacle, and r_i is the radius of the i-th circular obstacle; polygonal obstacle information, i.e. (x_polygon_i_j, y_polygon_i_j), where x_polygong_i_j is the horizontal coordinate of the j-th vertex of the i-th polygon, and y_polygong_i_j is the vertical coordinate of the j-th vertex of the i-th polygon; A-star grid size, i.e. net_length.

[0078] According to the size of the task area and the grid size, the length of the rows and columns of the A-star grid can be determined. The calculation formula is as follows:

[0079]

[0080] in:

[0081] row – the row size of the A-star grid;

[0082] column – the column size of the A-star grid;

[0083] After determining the row and column sizes of the A-star grid, you can build the fence grid map map required by the A-star algorithm, where map is a two-dimensional array containing row rows and coloum columns, and set the map array to all zeros.

[0084] After constructing the overall map grid of the A-star algorithm, it is necessary to fill the obstacle information according to the input polygonal obstacle information and circular obstacle information. First, the filling of polygonal obstacle information is introduced. Since each grid in the A-star fence grid is a square, each grid is composed of four edges. Each edge can be represented as a line segment, so each grid is composed of four line segments. For each grid, first determine whether each line segment is within the polygonal area. If it is within the polygonal area, then the grid is considered to have an obstacle, and its coordinates are set to 1; if the line segment is not within the polygonal area, then determine whether the line segment intersects with each edge of the polygon. As long as there is an intersection, the grid is considered to have an obstacle, and its coordinates are set to 1. Each grid is geometrically screened according to the above ideas, and then the polygonal obstacle information can be filled into the A-star fence grid.

[0085] Next, we will introduce the filling of circular obstacle information. The filling judgment of circular obstacle information is also judged on the four sides of the A-star fence grid. For each grid, first determine whether each line segment is within the circular area. If it is within the circular area, then the grid is considered to have an obstacle, and its coordinates are set to 1; if the line segment is not within the circular area, then determine whether the distance from the center of the circle to the line segment is less than the radius of the circular obstacle. If it is less than the radius of the circular obstacle, then the line segment intersects with the circular obstacle, then the grid is considered to have an obstacle, and its coordinates are set to 1. If it is greater than the radius of the circular obstacle, then the line segment does not intersect with the circular obstacle, then the grid has no obstacle, and its coordinates do not change. Each grid is geometrically screened according to the above ideas, and then the circular obstacle information can be filled into the A-star fence grid.

[0086] After determining the row and column sizes of the A-star grid, filling the polygonal obstacles and filling the circular obstacles, the first step of creating the A-star grid is completed.

[0087] After completing the creation of the A-star grid, the global optimal obstacle avoidance path is obtained through the traditional A-star algorithm.

[0088] Since the A-star algorithm is a path planning algorithm that searches for optimal paths based on grids, the path obtained by the A-star algorithm is obtained based on each grid, resulting in many collinear path points. However, these collinear path points are useless to us. We only need to know the starting and end points of the path, but not the points in the middle. Therefore, we can use collinear optimization to optimize the redundant points in the middle.

[0089] First, select the starting point i, the middle point i+1 and the end point i+2 from the path points generated by the A-star algorithm, and their corresponding coordinates are (x i ,y i )、(x i+1 ,y i+1 ) and (x i+2 ,y i+2 ), where x i is the horizontal coordinate of the starting point i, y i is the ordinate of the starting point i, x i+1 is the horizontal coordinate of the middle point i+1, y i+1 is the ordinate of the middle point i+1, x i+2 is the horizontal coordinate of the end point i+2, y i+2 is the ordinate of the midpoint i+2.

[0090] Find the slope k of the line connecting the starting point i and the middle point i+1 1 :

[0091]

[0092] Find the slope k of the line connecting the middle point i+1 and the end point i+2 2 :

[0093]

[0094] Determine the slope k 1 and slope k 2 Are they the same? If the slopes of the two are the same, then delete the middle point i+1. At this time, the end point advances one point, and continue to judge whether the slope of the line connecting the starting point and the middle point is consistent with the slope of the line connecting the middle point and the end point according to the above idea. If they are consistent, delete the middle point, and so on, until there are no collinear points, then the collinear optimization is completed.

[0095] Using Manhattan distance as the estimated cost h(x) of the A-star algorithm is a common method for solving path planning problems, and it is relatively accurate, but due to the sense of direction of the search, redundant corners will be generated. In order to reduce redundant corners and make the path shorter, redundant turning point optimization is performed on the basis of collinear optimization. First, static path planning is performed. The second-order derivative is used to detect the turning points of the path on the planned route. Then, starting from the starting point, every other turning point is connected to determine whether the line intersects with the obstacle. For circular obstacles, determine whether the distance from the center of the circle to the straight line is greater than the radius. If it is greater than the radius, the straight line does not intersect with the circular obstacle. If it is less than the radius, the straight line intersects with the circular obstacle. For polygonal obstacles, first determine whether the line segment intersects with each edge of the polygon. As long as one edge intersects, the line segment intersects with the polygonal obstacle. If the line intersects with the obstacle, the waypoints will not be deleted and the original path will be retained. If the line does not intersect with the obstacle, the middle point will be deleted and only the starting point and the turning point will be retained. The process is repeated until the end point is reached, at which point all redundant inflection points are deleted, thus completing the optimization of redundant inflection points.

[0096] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0097] In the description of the present invention, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0098] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A multi-machine collaborative route planning method for unmanned aerial vehicles, characterized in that: The following steps are involved: In response to controlling the UAV to perform patrol tasks in the designated operation area, a circling circle is formed based on the designated operation area. According to the tangent radius, number of circling circles and circling direction of the circling, the patrol parameters of the flight trajectory of the UAV are obtained, and obstacle avoidance is performed in the no-fly zone. At the same time, the DUBINS curve is used to complete the obstacle avoidance in the transition section for the route that does not meet the dynamic constraints; In response to controlling drones to perform multi-machine coordinated strike missions in designated operating areas, based on the planned routes of patrol missions, multiple drones are controlled to avoid threat areas and achieve target strikes from different designated directions with the same time and shortest path.

2. According to claim 1, a multi-machine collaborative route planning method for unmanned aerial vehicles is characterized by: In the process of performing patrol tasks, based on the circling point of the circling circle and the patrol time, a patrol route for the designated operating area is planned within the range of the minimum parameter set and the maximum parameter set, wherein the minimum parameter set information includes the take-off point, circling tangent point and patrol time information; the maximum parameter set information includes the take-off point, turning point, circling tangent point, patrol time, circular no-fly zone, polygonal no-fly zone, flyable airspace, landing point, minimum turning radius and shortest distance information.

3. According to claim 2, a multi-machine collaborative route planning method for unmanned aerial vehicles is characterized by: In the process of planning patrol routes, for drones carrying strike payloads, by setting strike targets, a single-machine strike route is planned based on the minimum parameter set and maximum parameter set used for strike route planning. The minimum parameter set for strike route planning includes take-off point, strike point and payload information; the maximum parameter set for strike route planning includes take-off point, turning point, strike point, payload information, circular no-fly zone, polygonal no-fly zone, flyable airspace, landing point, minimum turning radius and shortest distance information.

4. The method for multi-drone collaborative route planning for unmanned aerial vehicles according to claim 3, characterized in that: In the process of planning patrol routes, for multiple UAVs carrying strike payloads, by setting strike targets and based on the planning method of single-aircraft strike routes, multiple strike routes that meet the payload requirements and are applicable to multiple UAVs under various constraints are planned.

5. The method for multi-drone collaborative route planning for unmanned aerial vehicles according to claim 4, characterized in that: In the process of avoiding obstacles in the no-fly zone, the improved A* algorithm is used for path planning to avoid obstacles in the no-fly zone, wherein the Manhattan distance is used as the heuristic function H(n) of the A* algorithm to improve the A* algorithm.

6. The method for multi-drone collaborative route planning for unmanned aerial vehicles according to claim 5, characterized in that: In the process of generating threat avoidance routes through the improved A* algorithm, the improved A* algorithm is used to perform optimal path planning on a grid basis, and redundant points in the middle are optimized through collinear optimization to avoid obstacles in no-fly zones.

7. The method for multi-drone collaborative route planning for unmanned aerial vehicles according to claim 6, characterized in that: In the process of avoiding obstacles in the no-fly zone, static path planning is first performed. The second-order derivative is used to detect the turning points of the path on the planned route. Then, every turning point is connected from the starting point to determine whether there are obstacles on the connection line. If it exists, keep the original path; if it does not exist, delete the original path and keep the connection until the end point is determined.

8. The method for multi-drone collaborative route planning for unmanned aerial vehicles according to claim 7, characterized in that: In the process of path planning through the improved A* algorithm, an A* grid is constructed based on the information of the starting point, the end point, obstacles, and the task area; Run the improved A* algorithm based on the generated A* grid information to perform global obstacle avoidance and generate a feasible obstacle avoidance path; Perform collinear optimization according to the generated feasible obstacle avoidance path to generate a feasible path based on collinear optimization; Redundant inflection points are optimized according to the path generated by the collinear optimization to generate a feasible path based on the redundant inflection point optimization.

9. A multi-machine collaborative route planning system for unmanned aerial vehicles, characterized in that: The system is used to implement a multi-machine collaborative route planning method for unmanned aerial vehicles as described in any one of claims 1 to 8, and the system includes: The patrol mission planning module is used to form a hovering circle based on the specified operation area, obtain the patrol parameters of the UAV's flight trajectory according to the tangent radius, number of hovering circles and hovering direction, and avoid obstacles in the no-fly zone. At the same time, the DUBINS curve is used to complete the obstacle avoidance in the transition section for the route that does not meet the dynamic constraints; The multi-machine coordinated strike module is used to plan routes based on patrol missions, control multiple drones to avoid threat areas, and achieve target strikes from different designated directions with the same time and shortest path.

Images