Three-dimensional multi-constraint route planning of unmanned aerial vehicle low-altitude penetration based on coevolutionary multi-agent genetic algorithm |
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Authors: | Zhi-hong Peng Jin-ping Wu and Jie Chen |
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Affiliation: | PENG Zhi-hong1,2,WU Jin-ping1,CHEN Jie1,2 1.School of Automation,Beijing Institute of Technology,Beijing 100081,China,2.Key Laboratory of Complex System Intelligent Control and Decision of Ministry of Education |
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Abstract: | To address the issue of premature convergence and slow convergence rate in three-dimensional (3D) route planning of unmanned
aerial vehicle (UAV) low-altitude penetration, a novel route planning method was proposed. First and foremost, a coevolutionary
multi-agent genetic algorithm (CE-MAGA) was formed by introducing coevolutionary mechanism to multi-agent genetic algorithm
(MAGA), an efficient global optimization algorithm. A dynamic route representation form was also adopted to improve the flight
route accuracy. Moreover, an efficient constraint handling method was used to simplify the treatment of multi-constraint and
reduce the time-cost of planning computation. Simulation and corresponding analysis show that the planning results of CE-MAGA
have better performance on terrain following, terrain avoidance, threat avoidance (TF/TA2) and lower route costs than other existing algorithms. In addition, feasible flight routes can be acquired within 2 s, and
the convergence rate of the whole evolutionary process is very fast. |
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Keywords: | unmanned aerial vehicle (UAV) low-altitude penetration three-dimensional (3D) route planning coevolutionary multi-agent genetic algorithm (CE-MAGA) |
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