3D Path Planning Method for Multi-UAVs Inspired by Grey Wolf Algorithms

Abstract

Efficient and collision-free pathfinding, between source and destination locations for multi-Unmanned Aerial Vehicles (UAVs), in a predefined environment is an important topic in 3D Path planning methods. Since path planning is a Non-deterministic Polynomial-time (NP-hard) problem, metaheuristic approaches can be applied to find a suitable solution. In this study, two efficient 3D path planning methods, which are inspired by Incremental Grey Wolf Optimization (I-GWO) and Expanded Grey Wolf Optimization (Ex-GWO), are proposed to solve the problem of determining the optimal path for UAVs with minimum cost and low execution time. The proposed methods have been simulated using two different maps with three UAVs with diverse sets of starting and ending points. The proposed methods have been analyzed in three parameters (optimal path costs, time and complexity, and convergence curve) by varying population sizes as well as iteration numbers. They are compared with well-known different variations of grey wolf algorithms (GWO, mGWO, EGWO, and RW-GWO). According to path cost results of the defined case studies in this study, the I-GWO-based proposed path planning method (PP_I-GWO) outperformed the best with %36.11. In the other analysis parameters, this method also achieved the highest success compared to the other five methods.