M2PP: Effective Path Planning Based on Multi-Phase Particle Swarm Optimization and Multi-Scenario Adaptative DWA

To address the challenge of autonomous navigation for mobile robots in complex environments, many recent path planning methods employ a two-layer motion framework that integrates both global and local planning. However, global planning algorithms based on evolutionary algorithms often yield suboptimal paths, primarily due to premature convergence and wasted iterations in infeasible regions. Meanwhile, local planning algorithms, particularly the dynamic window approach (DWA) and its variants, are frequently tailored to specific scenarios, limiting their generalizability. To address these issues, this paper proposes an effective path planning method, namely M2PP. The global planning algorithm in M2PP enhances particle swarm optimization (PSO) by introducing multiple new search phases, creating a multi-phase PSO that can quickly identify feasible global paths and thoroughly explore the sampling space. To effectively handle multiple scenarios in a more generalized manner, the local planning component, multi-scenario adaptative DWA, integrates two novel terms into its cost function, enhancing both static and dynamic obstacle avoidance. Extensive simulations demonstrate that multi-phase PSO exhibits strong performance and robustness, especially in highly complex scenarios, and multi-scenario adaptative DWA can generate safe and efficient local trajectories in hazardous conditions. Additionally, experiments deploying the M2PP method on a real-world mobile robot further confirm its feasibility. Note to Practitioners—The motivation of this study is to address the diverse path planning needs of mobile robots across a wide range of scenarios. To this end, we propose the M2PP method, which integrates global and local path planning algorithms with strong performance and robustness. The global path planning component is based on the computationally efficient PSO algorithm, augmented with a multi-phase search framework that enables stable identification of feasible and efficient paths in various scenarios, especially in highly complex ones. The local path planning algorithm is an improved version of the DWA. This approach not only reduces unnecessary maneuvers when avoiding static obstacles, but also allows the robot to select safer and more efficient paths when navigating around dangerous dynamic obstacles—scenarios where the canonical DWA may struggle. Simulations and experimental results across multiple static and dynamic scenarios demonstrate that the proposed method can guide and control the mobile robot in a safe, smooth, and efficient manner. However, the reliance of M2PP on prior environmental knowledge poses certain limitations. In future work, we aim to modify this method to enable its application in more complex, unknown environments, thereby further expanding its applicability.