Three dimensional path planning using Grey wolf optimizer for UAVs

Robot path planning is essential to identify the most feasible path between a start point and goal point by avoiding any collision in the given environment. This task is an NP-hard problem and can be modeled as an optimization problem. Many researchers have proposed various deterministic and meta-heuristic algorithm to obtain better results for the path planning problem. The path planning for 3D multi-Unmanned Aerial Vehicle (UAV) is very difficult as the UAV has to find a viable path between start point and goal point with minimum complexity. This work utilizes a newly proposed methodology named ‘grey wolf optimization (GWO)’ to solve the path planning problem of three Dimensional UAV, whose task is to find the feasible trajectory while avoiding collision among obstacles and other UAVs. The performance of GWO algorithm is compared with deterministic algorithms such as Dijkstra, A* and D*, and meta-heuristic algorithms such as Intelligent BAT Algorithm (IBA), Biogeography Based Optimization (BBO), Particle Swarm Optimization (PSO), Glowworm Swarm Optimization (GSO), Whale Optimization Algorithm (WOA) and Sine Cosine Algorithm (SCA), so as to find the optimal method. The results show that GWO algorithm outperforms the other deterministic and meta-heuristic algorithms in path planning for 3D multi-UAV.