Hybrid Metaheuristic Approach for Robot Path Planning in Dynamic Environment

Abstract

Path planning for mobile robots refers to searching for an optimal or near-optimal path under different types of constrains in complex environments. Autonomous mobile robots require an efficient navigation system to navigate from one location to another location fast and safely without hitting static or dynamic obstacles. Existing solutions for robot path planning in the static and dynamic environment have been presented based on different searching algorithms with some limitations and constraints. The static environment was designed with six fixed obstacles and the dynamic environment was implemented with two moving and four fixed obstacles. To cover part of the robot path planning research area, in this thesis, three metaheuristic algorithms were applied in both static and dynamic environments. These three algorithms are the Particle Swarming Optimization PSO, the Ant Colony Optimization ACO and the hybrid PSO-ACO. This research aims to keep the robot far away from the obstacles boundaries by a minimum safe distance and achieve the best path. A set of certain constraints must be met simultaneously to achieve the goals; the shortest path, the least time, and free from collisions. The results are calculated for the two algorithms separately and then that of the hybrid algorithm is calculated. The effectiveness and superiority of the hybrid algorithm were verified on both PSO and ACO algorithms.