Hybrid Form of Particle Swarm Optimization and Genetic Algorithm For Optimal Path Planning in Coverage Mission by Cooperated Unmanned Aerial Vehicles

• Autor(a) principal: Haghighi,Hassan
• Data de Publicação: 2020
• Outros Autores: Sadati,Seyed Hossein, Dehghan,S.M. Mehdi, Karimi,Jalal
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Journal of Aerospace Technology and Management (Online)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2175-91462020000100339
• topic: Hybrid algorithm; Path planning; Evolutionary method; Multiple UAVs; Optimal patrolling; Cooperated control

ABSTRACT: In this paper, a new form of open traveling salesman problem (OTSP) is used for path planning for optimal coverage of a wide area by cooperated unmanned aerial vehicles (UAVs). A hybrid form of particle swarm optimization (PSO) and genetic algorithm (GA) is developed for the current path planning problem of multiple UAVs in the coverage mission. Three path-planning approaches are introduced through a group of the waypoints in a mission area: PSO, genetic algorithm, and a hybrid form of parallel PSO-genetic algorithm. The proposed hybrid optimization tries to integrate the advantages of the PSO, i.e. coming out from local minimal, and genetic algorithm, i.e. better quality solutions within a reasonable computational time. These three approached are compared in many scenarios with different levels of difficulty. Statistical analyses reveal that the hybrid algorithm is a more effective strategy than others for the mentioned problem.