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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. 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