Two-layer path planning framework for WMRs in dynamic environments: Optimized ant colony algorithm and dynamic window approach

Abstract

This paper proposes a two-layer path planning method for wheeled mobile robots (WMRs), where an improved ant colony optimization (ACO) and optimized dynamic window approach (DWA) algorithms are used, at the global and local layer, respectively. This method allows WMRs to plan a high-quality path under complex dynamic scenarios, while costing less traveling time and energy consumption. At the level of global path planning, a modified ACO algorithm is presented which incorporates a path duplicate counter, a new heuristic function and path smoothing operation to enhance the feasibility and robustness of global path planning. At the level of local path planning, based on DWA, an optimization method composed by energy evaluation and dynamic obstacle avoidance evaluation sub-function is proposed to save the energy cost, while enhancing the ability of WMRs to avoid moving obstacles. This study aims to enhance the efficiency and effectiveness of path planning for WMRs using a combination of ACO and DWA algorithms, such that the proposed algorithm can be applied to multi-obstacle environment to execute dynamic objects avoidance. Finally, a multi-blockage environment involved with dynamic obstacles are simulated to verify the proposed path planning method.