Path Planning Optimization of Automated Guided Vehicles using Chaotic Marine Predators Algorithm

This paper presents the collision-free path planning approach for automated guided vehicle (AGV) in an intelligent warehouse environment, optimized by means of recent well-known meta-heuristic algorithms. This novel approach is an assessment and possibilities for path planning and scheduling of the multi-AGVs to complete the given tasks in a minimal travel distance with optimal operation time. Six different metaheuristic algorithms such as PSO, MELGWO, GTO, SFS, MPA and chaotic-improved MPA are compared for the AGVs path optimization capability. In order to test the robustness of the proposed approaches, four different scenarios are presented which include a general obstacle avoidance and three tasks in simple maps that treated as an intelligent warehouse environment. In each scenario, the obstacles are placed in such a way to increase the overall path complexity for an AGV to reach the target destination. The exploration and exploitation phases in the MPA algorithm are enhanced simultaneously by replacing the conventional Gaussian random with chaotic operators to ensure its effectiveness in AGVs path planning optimization. The chaotic MPA algorithm outperforms other algorithms based on the statistical analysis results with overall improvement of 11.0171% in comparison to unoptimized probabilistic roadmap method (PRM) planner. In conclusion, the chaotic MPA algorithm can be efficiently optimized the AGVs path planning in all aforementioned environments.