Unmanned aerial vehicle group path planning system for complex scene cruise

Abstract

The invention belongs to the field of unmanned aerial vehicle group path planning, relates to a data analysis technology, and aims to solve the problem that comprehensive analysis cannot be performed by combining all flight parameters in the prior art, in particular to an unmanned aerial vehicle group path planning system for complex scene cruise. The path optimization platform is in communication connection with an effective rate test module, a coverage analysis module, a planning processing module and a database; the efficiency test module is used for monitoring and analyzing the unmanned aerial vehicle group cruising efficiency of the complex scene: setting a cruising starting point and a cruising ending point in the complex scene, controlling all unmanned aerial vehicles of the unmanned aerial vehicle group to start from the cruising starting point and run to the cruising ending point for cruising, and then obtaining an efficiency sequence of the unmanned aerial vehicle group; the cruise efficiency of the unmanned aerial vehicle group in a complex scene can be monitored and analyzed, the cruise process is primarily screened from the angle of the cruise efficiency, and it is guaranteed that the navigation efficiency of the finally generated execution path can be higher than the average level.

Description

Technical Field

[0001] The present invention belongs to the field of UAV swarm path planning and relates to data analysis technology, and specifically to a UAV swarm path planning system for cruising in complex scenarios. Background Art

[0002] Drone swarm path planning refers to planning a reasonable flight path for drones in three-dimensional space so that they can complete their tasks safely and efficiently. Path planning is one of the key technologies for autonomous flight of drones. It can determine the trajectory of drones through algorithms and models.

[0003] The existing drone swarm path planning system can only analyze the shortest path flight plan, but cannot conduct a comprehensive analysis based on all flight parameters, and thus cannot synchronously optimize the drone's navigation speed, collision probability, power loss and other performance based on path planning.

[0004] In view of the above technical problems, this application proposes a solution. Summary of the invention

[0005] The purpose of the present invention is to provide a UAV swarm path planning system for complex scene cruising, which is used to solve the problem that the existing technology cannot combine all flight parameters for comprehensive analysis;

[0006] The technical problem to be solved by the present invention is: how to provide a drone swarm path planning system for complex scene cruising that can combine all flight parameters for comprehensive analysis.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A drone swarm path planning system for complex scene cruising includes a path planning platform, wherein the path optimization platform is communicatively connected to an efficiency test module, a coverage analysis module, a planning processing module, and a database;

[0009] The efficiency test module is used to monitor and analyze the cruising efficiency of the drone swarm in complex scenarios: set the cruising starting point and cruising end point in the complex scenario, control all drones in the drone swarm to start from the cruising starting point and run to the cruising end point for cruising, and then obtain the efficiency sequence of the drone swarm; send the efficiency sequence of the drone swarm to the coverage analysis module through the path planning platform;

[0010] The coverage analysis module is used to analyze the coverage of the cruise path of the drone group and mark the analyzed path;

[0011] The planning and processing module is used to plan, process and analyze the execution path of the drone group and generate an execution path, and send the execution path to the mobile terminal of the manager through the path planning platform.

[0012] Furthermore, the process of obtaining the efficiency sequence of the drone swarm includes: marking the process from the departure of the drone from the cruising starting point to the arrival of the drone at the cruising end point as the cruising process, obtaining the duration of the cruising process and marking it as the efficiency value of the cruising process, and arranging the cruising processes of all drones in order of efficiency values ​​from small to large to obtain the efficiency sequence of the drone swarm.

[0013] Furthermore, the specific process of the coverage analysis module for analyzing the coverage of the cruise path of the drone group includes: intercepting the Q1 cruise processes ranked first in the efficiency sequence as the analysis process, and the value of Q1 is calculated by the formula Q1=Q2*t1 e Get, where Q2 is a numerical constant, and the specific value of Q1 is set by the management personnel; t1 is the proportional coefficient, 1.15≤t1≤1.25, e=0; obtain the cruise paths of all analysis processes and draw the cruise paths on the map of the complex scene, divide the complex scene into several cruise areas, and determine whether all cruise areas contain at least one cruise path:

[0014] If so, it is determined that the coverage of the cruise path meets the requirements and the analysis process is output;

[0015] If not, it is determined that the cruise path coverage does not meet the requirements, and e is assigned a value: e=e+1. The value of Q1 is recalculated and the analysis process is re-intercepted in the complete efficiency sequence; and so on, until the cruise path coverage meets the requirements; the cruise path of the analysis process is marked as the analysis path.

[0016] Furthermore, the specific process of the planning and processing module planning, processing and analyzing the execution path of the drone swarm includes: marking the obstacle avoidance priority process, the power loss priority process and the rate priority process; determining whether the obstacle avoidance priority process, the power loss priority process and the rate priority process contain the same analysis process: if contained, the corresponding analysis process is marked as the candidate process, the sum of the sequence numbers of the candidate process in the obstacle avoidance sequence, the power loss sequence and the rate sequence is marked as the candidate value of the candidate process, and the analysis path corresponding to the candidate process with the smallest candidate value is marked as the execution path; if not contained, the obstacle avoidance priority process, the power loss priority process and the rate priority process are randomly combined to obtain several path combinations, each path combination includes a randomly selected obstacle avoidance priority process, power loss priority process and rate priority process; and the path combination is overlapped and analyzed to obtain the execution path.

[0017] Furthermore, the specific process of marking the obstacle avoidance priority process, the power loss priority process and the rate priority process includes: marking the number of obstacles avoided in the analysis path of the analysis process as the obstacle avoidance value of the analysis process, arranging the analysis processes in order from small to large according to the obstacle avoidance value values ​​to obtain an obstacle avoidance sequence, marking the difference between the power level of the drone at the start of the analysis process and the power level of the drone at the end of the analysis process as the power loss value of the analysis process, arranging the analysis processes in order from small to large according to the power loss value values ​​to obtain a power loss sequence; arranging the analysis processes in order from small to large according to the efficiency value values ​​to obtain a rate sequence; and intercepting K1 analysis processes from the obstacle avoidance sequence, the power loss sequence and the rate sequence respectively and marking them as the obstacle avoidance priority process, the power loss priority process and the rate priority process.

[0018] Furthermore, the specific process of performing overlap analysis on the path combination includes: marking the parts of the analysis paths corresponding to the obstacle avoidance priority process, the power loss priority process and the rate priority process in the path combination that overlap with each other as the overlapped parts of the path combination, summing up the length values ​​of the analysis paths corresponding to the obstacle avoidance priority process, the power loss priority process and the rate priority process in the path combination and taking the average value to obtain the standard analysis value, marking the ratio of the sum of the length values ​​of all overlapping parts in the path combination to the standard analysis value as the overlap coefficient of the path combination, obtaining the overlap threshold through the database, and comparing the overlap coefficient with the overlap threshold: if the overlap coefficient is less than the overlap threshold, an independent recommendation method is used to generate the execution path; if the overlap coefficient is greater than or equal to the overlap threshold, a fusion recommendation method is used to generate the execution path.

[0019] Furthermore, the specific process of generating an execution path using an independent recommendation method includes: marking the analysis path of the analysis process ranked first in the obstacle avoidance sequence, the power loss sequence, and the rate sequence as the obstacle avoidance recommended path, the power loss recommended path, and the rate recommended path, respectively, and the obstacle avoidance recommended path, the power loss recommended path, and the rate recommended path together constitute the execution path.

[0020] Furthermore, the specific process of generating an execution path using a fusion recommendation method includes: marking the path combination with the largest overlap coefficient value as a fusion combination; arranging all overlapping parts in the fusion combination in order of distance from the cruise starting point from near to far to obtain an overlap sequence; connecting all overlapping parts end to end in the order of the overlap sequence to obtain a fusion path, and the fusion path includes overlapping parts and connecting parts.

[0021] The present invention has the following beneficial effects:

[0022] 1. The efficiency test module can monitor and analyze the cruising efficiency of drone groups in complex scenarios, and preliminarily screen the cruising process from the perspective of cruising efficiency to ensure that the navigation efficiency of the final generated execution path is higher than the average level;

[0023] 2. The coverage analysis module can be used to analyze the coverage of the drone group's cruise path. After intercepting the analysis process in the efficiency sequence, the coverage rate of the navigation path relative to the complex scene is analyzed in a regional analysis manner to improve the reliability of the generated analysis path;

[0024] 3. The planning and processing module can be used to plan, process and analyze the execution path of the drone swarm. The execution path can be obtained by analyzing multiple parameters of the drone's navigation. When the drone is navigating on the execution path, its navigation efficiency, collision risk and power shortage risk can be controlled to the optimal state. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] 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 or the description of the prior art 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.

[0026] Figure 1 is a system block diagram of Embodiment 1 of the present invention;

[0027] Figure 2 This is a flow chart of the method of Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0028] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] Embodiment 1: Figure 1 As shown, the UAV swarm path planning system for complex scene cruising includes a path planning platform, a path optimization platform communication connection efficiency test module, a coverage analysis module, a planning processing module and a database.

[0030] The efficiency test module is used to monitor and analyze the cruising efficiency of drone swarms in complex scenarios: set the cruising starting point and cruising end point in the complex scenario, control all drones in the drone swarm to start from the cruising starting point and run to the cruising end point for cruising, mark the process from the starting point of the drone to the arrival of the drone at the cruising end point as the cruising process, obtain the duration of the cruising process and mark it as the efficiency value of the cruising process, arrange the cruising processes of all drones in order of efficiency values ​​from small to large to obtain the efficiency sequence of the drone swarm; send the efficiency sequence of the drone swarm to the coverage analysis module through the path planning platform; monitor and analyze the cruising efficiency of the drone swarm in complex scenarios, and preliminarily screen the cruising process from the perspective of cruising efficiency to ensure that the navigation efficiency of the final generated execution path can be higher than the average level.

[0031] The coverage analysis module is used to analyze the coverage of the cruise path of the drone group: the Q1 cruise processes ranked first in the efficiency sequence are intercepted as the analysis process, and the value of Q1 is given by the formula Q1=Q2*t1 e It is obtained that Q2 is a numerical constant, and the specific value of Q1 is set by the management personnel; t1 is a proportional coefficient, 1.15≤t1≤1.25, e=0; obtain the cruise paths of all analysis processes and draw the cruise paths on the map of the complex scene, divide the complex scene into several cruise areas, and determine whether all cruise areas contain at least one cruise path: if so, determine that the coverage of the cruise path meets the requirements, and output the analysis process; if not, determine that the coverage of the cruise path does not meet the requirements, assign e: e=e+1, recalculate the value of Q1 and re-intercept the analysis process in the complete efficiency sequence, and so on, until the coverage of the cruise path meets the requirements; mark the cruise path of the analysis process as an analysis path, and send all analysis processes and corresponding analysis paths to the planning processing module through the path planning platform; after intercepting the analysis process in the efficiency sequence, analyze the coverage rate of the process navigation path relative to the complex scene in a regional analysis manner to improve the reliability of the generated analysis path.

[0032] The planning processing module is used to perform planning, processing and analysis on the execution path of the drone group: the number of obstacles avoided in the analysis path of the analysis process is marked as the obstacle avoidance value of the analysis process, and the analysis process is arranged in the order of obstacle avoidance values ​​from small to large to obtain an obstacle avoidance sequence; the difference between the power supply value of the drone at the beginning of the analysis process and the power supply value of the drone at the end of the analysis process is marked as the power loss value of the analysis process, and the analysis process is arranged in the order of power loss values ​​from small to large to obtain a power loss sequence; the analysis process is arranged in the order of efficiency values ​​from small to large to obtain a rate sequence;

[0033] K1 analysis processes are respectively intercepted from the obstacle avoidance sequence, the power loss sequence and the rate sequence and marked as the obstacle avoidance priority process, the power loss priority process and the rate priority process.

[0034] Determine whether the obstacle avoidance priority process, power loss priority process, and speed priority process contain the same analysis process:

[0035] If it is included, the corresponding analysis process is marked as a candidate process, which means that there is an analysis process that takes into account rate, power loss and obstacle avoidance at the same time. The sum of the sequence numbers of the candidate process in the obstacle avoidance sequence, power loss sequence and rate sequence is marked as the candidate value of the candidate process. The candidate value indicates the comprehensive priority of the candidate process. The smaller the value of the candidate value, the higher the comprehensive priority of the candidate process. The analysis path corresponding to the candidate process with the smallest candidate value is marked as the execution path.

[0036] If not included, the obstacle avoidance priority process, the power loss priority process and the speed priority process are randomly combined to obtain several path combinations, each of which includes a randomly selected obstacle avoidance priority process, power loss priority process and speed priority process.

[0037] Perform overlap analysis on the path combination: mark the overlapped parts of the analysis paths corresponding to the obstacle avoidance priority process, power loss priority process, and rate priority process in the path combination as the overlapped parts of the path combination, sum and average the length values ​​of the analysis paths corresponding to the obstacle avoidance priority process, power loss priority process, and rate priority process in the path combination to obtain the standard analysis value, mark the ratio of the sum of the length values ​​of all overlapping parts in the path combination to the standard analysis value as the overlap coefficient of the path combination, obtain the overlap threshold through the database, and compare the overlap coefficient with the overlap threshold:

[0038] If the overlap coefficient is less than the overlap threshold, an independent recommendation method is used to generate an execution path: the analysis paths of the analysis processes ranked first in the obstacle avoidance sequence, power loss sequence, and rate sequence are marked as obstacle avoidance recommended paths, power loss recommended paths, and rate recommended paths, respectively, and the obstacle avoidance recommended paths, power loss recommended paths, and rate recommended paths together constitute the execution path; this process directly outputs the corresponding recommended paths from different angles when the overlap rate of all analysis paths in the path combination is low, so that users with different needs can directly choose the best path plan at the corresponding angle; for example, users who pursue efficiency can choose the rate recommended path for drone cruise control;

[0039] If the overlap coefficient is greater than or equal to the overlap threshold, a fusion recommendation method is used to generate an execution path: the path combination with the largest overlap coefficient value is marked as a fusion combination; all overlapping parts in the fusion combination are arranged in order of distance from the cruise starting point from near to far to obtain an overlap sequence, and all overlapping parts are connected end to end in the order of the overlap sequence to obtain a fusion path (the starting point of the first-ranked overlapping part is connected to the cruise starting point, and the end point of the last-ranked overlapping part is connected to the cruise end point). The fusion path contains overlapping parts and connecting parts (the connecting part is the end-to-end connection part of adjacent overlapping parts), and the connecting part is randomly selected from the two analysis paths corresponding to the latter overlapping part (when the number of identical analysis paths in the analysis paths corresponding to the two overlapping parts is not one, they are directly randomly selected from the same analysis path; when the number of identical analysis paths in the analysis paths corresponding to the two overlapping parts is one, the same analysis path is directly selected as the connecting part).

[0040] Embodiment 2: Figure 2 As shown, the UAV swarm path planning method for complex scene cruising includes the following steps:

[0041] Step 1: Monitor and analyze the cruising efficiency of the drone swarm in complex scenarios and generate an efficiency sequence. Perform a preliminary screening of the cruising process from the perspective of cruising efficiency to ensure that the navigation efficiency of the final generated execution path is higher than the average level.

[0042] Step 2: Analyze the coverage of the cruise path of the drone group and mark the analysis path. Analyze the coverage of the navigation path relative to the complex scene in a regional analysis manner to improve the reliability of the generated analysis path.

[0043] Step 3: Plan, process and analyze the execution path of the drone swarm and generate the execution path. The execution path is obtained by analyzing multiple parameters of the drone navigation. When the drone navigates on the execution path, its navigation efficiency, collision risk and power shortage risk can be controlled to the optimal state.

[0044] A drone swarm path planning system for cruising in complex scenarios sets a cruising start point and a cruising end point in a complex scenario when working, controls all drones in the drone swarm to start from the cruising start point and run to the cruising end point for cruising, generates an efficiency sequence of the drone swarm according to the efficiency of the cruising process; intercepts the analysis process from the efficiency sequence, generates an obstacle avoidance sequence, a power loss sequence and a rate sequence according to the intercepted analysis process, and performs comprehensive analysis and processing on the obstacle avoidance sequence, the power loss sequence and the rate sequence to generate an execution path; comprehensively analyzes multiple parameters of drone navigation to obtain the execution path, and when the drone navigates on the execution path, its navigation efficiency, collision risk and power shortage risk can be controlled to the optimal state.

[0045] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

[0046] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0047] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A drone swarm path planning system for complex scene cruising, characterized by: It includes a path planning platform, wherein the path optimization platform is communicatively connected with an efficiency testing module, a coverage analysis module, a planning processing module and a database; The efficiency test module is used to monitor and analyze the cruising efficiency of the drone swarm in complex scenarios: set the cruising starting point and cruising end point in the complex scenario, control all drones in the drone swarm to start from the cruising starting point and run to the cruising end point for cruising, and then obtain the efficiency sequence of the drone swarm; send the efficiency sequence of the drone swarm to the coverage analysis module through the path planning platform; The coverage analysis module is used to analyze the coverage of the cruise path of the drone group and mark the analyzed path; The planning and processing module is used to plan, process and analyze the execution path of the drone group and generate an execution path, and send the execution path to the mobile terminal of the manager through the path planning platform.

2. The UAV swarm path planning system for complex scene cruising according to claim 1 is characterized in that: The process of obtaining the efficiency sequence of the drone swarm includes: marking the process from the drone starting from the cruise starting point to the drone arriving at the cruise end point as the cruise process, obtaining the duration of the cruise process and marking it as the efficiency value of the cruise process, and arranging the cruise processes of all drones in order of efficiency values ​​from small to large to obtain the efficiency sequence of the drone swarm.

3. The UAV swarm path planning system for complex scene cruising according to claim 2 is characterized in that: The specific process of the coverage analysis module for analyzing the coverage of the cruise path of the drone group includes: intercepting the Q1 cruise processes ranked first in the efficiency sequence as the analysis process, and the value of Q1 is calculated by the formula Q1=Q2*t1 e Get, where Q2 is a numerical constant, and the specific value of Q1 is set by the management personnel; t1 is the proportional coefficient, 1.15≤t1≤1.25, e=0; obtain the cruise paths of all analysis processes and draw the cruise paths on the map of the complex scene, divide the complex scene into several cruise areas, and determine whether all cruise areas contain at least one cruise path: If so, it is determined that the coverage of the cruise path meets the requirements and the analysis process is output; If not, it is determined that the cruise path coverage does not meet the requirements, and e is assigned a value: e=e+1. The value of Q1 is recalculated and the analysis process is re-intercepted in the complete efficiency sequence; and so on, until the cruise path coverage meets the requirements; the cruise path of the analysis process is marked as the analysis path.

4. The UAV swarm path planning system for complex scene cruising according to claim 3 is characterized in that: The specific process of the planning and processing module for planning and analyzing the execution path of the drone swarm includes: marking the obstacle avoidance priority process, the power loss priority process and the rate priority process; determining whether the obstacle avoidance priority process, the power loss priority process and the rate priority process contain the same analysis process: if they do, the corresponding analysis process is marked as the candidate process, the sum of the sequence numbers of the candidate process in the obstacle avoidance sequence, the power loss sequence and the rate sequence is marked as the candidate value of the candidate process, and the analysis path corresponding to the candidate process with the smallest candidate value is marked as the execution path; if not, the obstacle avoidance priority process, the power loss priority process and the rate priority process are randomly combined to obtain several path combinations, each path combination includes a randomly selected obstacle avoidance priority process, power loss priority process and rate priority process; the path combination is overlapped and analyzed to obtain the execution path.

5. The UAV swarm path planning system for complex scene cruising according to claim 4 is characterized in that: The specific process of marking the obstacle avoidance priority process, the power loss priority process and the rate priority process includes: marking the number of obstacles avoided in the analysis path of the analysis process as the obstacle avoidance value of the analysis process, arranging the analysis processes in order of the obstacle avoidance value from small to large to obtain an obstacle avoidance sequence, marking the difference between the power supply value of the drone at the start of the analysis process and the power supply value of the drone at the end of the analysis process as the power loss value of the analysis process, arranging the analysis processes in order of the power loss value from small to large to obtain a power loss sequence; arranging the analysis processes in order of the efficiency value from small to large to obtain a rate sequence; and intercepting K1 analysis processes from the obstacle avoidance sequence, the power loss sequence and the rate sequence respectively and marking them as the obstacle avoidance priority process, the power loss priority process and the rate priority process.

6. The UAV swarm path planning system for complex scene cruising according to claim 5 is characterized in that: The specific process of performing overlap analysis on the path combination includes: marking the overlapping parts of the analysis paths corresponding to the obstacle avoidance priority process, the power loss priority process and the rate priority process in the path combination as the overlapping parts of the path combination, summing up the length values ​​of the analysis paths corresponding to the obstacle avoidance priority process, the power loss priority process and the rate priority process in the path combination and taking the average value to obtain the standard analysis value, marking the ratio of the sum of the length values ​​of all overlapping parts in the path combination to the standard analysis value as the overlap coefficient of the path combination, obtaining the overlap threshold through the database, and comparing the overlap coefficient with the overlap threshold: if the overlap coefficient is less than the overlap threshold, an independent recommendation method is used to generate the execution path; if the overlap coefficient is greater than or equal to the overlap threshold, a fusion recommendation method is used to generate the execution path.

7. The UAV swarm path planning system for complex scene cruising according to claim 6 is characterized in that: The specific process of generating an execution path using an independent recommendation method includes: marking the analysis path of the analysis process ranked first in the obstacle avoidance sequence, the power loss sequence, and the rate sequence as the obstacle avoidance recommended path, the power loss recommended path, and the rate recommended path, respectively, and the obstacle avoidance recommended path, the power loss recommended path, and the rate recommended path together constitute the execution path.

8. The UAV swarm path planning system for complex scene cruising according to claim 7 is characterized in that: The specific process of generating an execution path using a fusion recommendation method includes: marking the path combination with the largest overlap coefficient value as a fusion combination; arranging all the overlapping parts in the fusion combination in order of distance from the cruise starting point from near to far to obtain an overlap sequence; connecting all the overlapping parts end to end in the order of the overlap sequence to obtain a fusion path, and the fusion path includes the overlapping part and the connecting part.

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