Multi-population chaos grey wolf algorithm-based multi-unmanned aerial vehicle cooperative path planning method

A route planning and multi-UAV technology, applied in navigation calculation tools, three-dimensional position/course control, vehicle position/route/altitude control, etc., can solve problems such as poor development ability and local optimum, and achieve improved search accuracy and The effect of stability, fast convergence speed and less adjustment parameters

Active Publication Date: 2019-12-24
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS +2
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Problems solved by technology

However, the original algorithm has poor development ability in the route planning of multiple UAVs,

Method used

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  • Multi-population chaos grey wolf algorithm-based multi-unmanned aerial vehicle cooperative path planning method
  • Multi-population chaos grey wolf algorithm-based multi-unmanned aerial vehicle cooperative path planning method
  • Multi-population chaos grey wolf algorithm-based multi-unmanned aerial vehicle cooperative path planning method

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Embodiment Construction

[0051] The technical solution of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0052] Such as figure 1 , 2 As shown, a multi-UAV collaborative route planning method based on the multi-population chaotic gray wolf algorithm includes the following steps:

[0053] Step 1: Establish a three-dimensional environment for multi-UAV route planning, initialize the number of UAVs to N, the starting point S and target point E of each UAV, the number of waypoints Dim, and the flight speed range of UAVs [v min ,v max ]. Taking the mountainous background as the task environment, the digital elevation model (DEM) of the mountainous area is established by using random functions to simulate mountain peaks and threat areas. In this planning space, the UAV route is represented as multiple three-dimensional track nodes {S,P 1 ,P 2 ,...,P n ,E}, track node denoted as P i =(x i ,y i ,z i ), connect the track nodes in turn to obtain...

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Abstract

The invention discloses a multi-population chaos grey wolf algorithm-based multi-unmanned aerial vehicle cooperative path planning method. The method comprises the steps of firstly, building a multi-vehicle cooperative path planning model on the basis of a three-dimensional planning space; secondly, building a multi-unmanned aerial vehicle initial track set by combining a multi-population idea; and finally, searching optimal path of each unmanned aerial vehicle by a chaos grey wolf optimization algorithm. According to the method, cooperative path numbering of multiple unmanned aerial vehiclesis achieved by introducing the multi-population idea, the path searching range is expanded by chaos local searching, the problem that an original algorithm is easy to get in local optimization is effectively solved, the algorithm stability is improved, and a better path planning effect can be achieved. The employed multi-population grey wolf algorithm involves a few adjustment parameters and is rapid in convergence speed, multi-dimensional space searching and path planning under different conditions can be achieved, different constraint conditions and planning requirement are met, and optimalpath searching of an unmanned aerial vehicle group is achieved.

Description

technical field [0001] The invention relates to a multi-unmanned aerial vehicle cooperative route planning method based on a multi-group chaotic gray wolf algorithm, which belongs to the field of UAV route planning. Background technique [0002] In modern warfare, a single UAV cannot meet diverse operational requirements due to its operational scope and its own functional limitations. Multi-aircraft cooperative operations have become a trend in the future development of UAVs because they can cooperate with each other to perform various combat tasks and improve combat effectiveness. Multi-machine cooperative route planning is the key technology for multiple UAVs to achieve cooperative operations. The purpose is to quickly plan for each UAV the optimal route that meets the relevant constraints and planning requirements according to the current environmental situation, so as to ensure that the UAV group can Accurately reach the destination and implement the corresponding tasks...

Claims

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Application Information

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IPC IPC(8): G01C21/20G05D1/10
CPCG01C21/20G05D1/104
Inventor 杨柳庆郭锦张勇王鹏飞杨婷婷
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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