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

• Main Authors: Hassan Haghighi, Seyed Hossein Sadati, S.M. Mehdi Dehghan, Jalal Karimi
• Format: Article
• Language: English
• Published: Departamento de Ciência e Tecnologia Aeroespacial 2020-09-01
• Series: Journal of Aerospace Technology and Management
• Subjects: hybrid algorithm; path planning; evolutionary method; multiple uavs; optimal patrolling; cooperated control
• Online Access: https://www.scielo.br/pdf/jatm/v12/2175-9146-jatm-12-e4320.pdf
• ISSN: 1984-9648; 2175-9146

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.