Author:
Li Sen,Zhang Ran,Ding Yuanming,Qin Xutong,Han Yajun,Zhang Huiting
Abstract
Multi-UAV (multiple unmanned aerial vehicles) flying in three-dimensional (3D) mountain environments suffer from low stability, long-planned path, and low dynamic obstacle avoidance efficiency. Spurred by these constraints, this paper proposes a multi-UAV path planning algorithm that consists of a bioinspired neural network and improved Harris hawks optimization with a periodic energy decline regulation mechanism (BINN-HHO) to solve the multi-UAV path planning problem in a 3D space. Specifically, in the procession of global path planning, an energy cycle decline mechanism is introduced into HHO and embed it into the energy function, which balances the algorithm’s multi-round dynamic iteration between global exploration and local search. Additionally, when the onboard sensors detect a dynamic obstacle during the flight, the improved BINN algorithm conducts a local path replanning for dynamic obstacle avoidance. Once the dynamic obstacles in the sensor detection area disappear, the local path planning is completed, and the UAV returns to the trajectory determined by the global planning. The simulation results show that the proposed Harris hawks algorithm has apparent superiorities in path planning and dynamic obstacle avoidance efficiency compared with the basic Harris hawks optimization, particle swarm optimization (PSO), and the sparrow search algorithm (SSA).
Funder
National Natural Science Foundation of China
General Project Fund in the Field of Equipment Development Department
Dalian University
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Cited by
3 articles.
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