Multi-unmanned aerial vehicle route planning method based on bee colony collaborative foraging algorithm

Abstract

The invention belongs to the field of unmanned aerial vehicle technology, and more specifically relates to a multi-unmanned aerial vehicle route planning method which can be used for multi-unmanned aerial vehicle target tracking, route optimization, cooperative management, cooperative flight, and task distribution. The multi-unmanned aerial vehicle route planning method comprises following steps: detailed information of route planning task is initialized, and coordinate discrete transformation is performed; parameters of the bee colony collaborative foraging algorithm is initialized; route cost of each unmanned aerial vehicle is calculated; current route cost is calculated according to a current position by each unmanned aerial vehicle; guide aerial vehicles are selected by followed aerial vehicles, wherein one followed aerial vehicle is recruited by each guide aerial vehicle; the current route is abandoned and a new route is searched; parameters of optimized route are saved, and an optimal value is calculated; inspection is performed so as to determine whether an upper limit of iteration number is reached. The multi-unmanned aerial vehicle route planning method is capable of resolving the initialization sensitive problem of a traditional intelligent optimization route planning method, and can be used for improvement of accuracy of the algorithm so as to obtain an optimal solution or a second best solution sufficiently close to the optimal solution, and improve stability of route planning and search efficiency.

Description

technical field [0001] The invention belongs to the technical field of unmanned aerial vehicles, and specifically relates to a multi-unmanned aerial vehicle track planning method that can be used for multi-unmanned aerial vehicle target tracking, track optimization, collaborative management, cooperative flight, and task assignment. Background technique [0002] In the complex and changeable information battlefield environment in the future, it will be difficult for a single UAV to complete the task. In many cases, it must be completed through cooperative control of multiple UAVs; each UAV requires a 1 A crew of up to 3 assigns, negotiates and coordinates many human fighters. In addition to the cost of human operators, this approach encounters unsolvable challenges in how to achieve coordination. Under the constraints of today's technology, it is still quite difficult for unmanned aerial vehicles to reach the powerful information processing capabilities and intelligence of p...

AI Technical Summary

Problems solved by technology

None of them have solved key issues such as cooperative control, track planning, and collision avoidance in UAV swarm flight from the perspective of simulating the biological behavior of bee colonies. limited role

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