Research on path planning of mobile robot in complex environment

To address inefficiencies in search performance, slow convergence, and redundant node generation in mobile robot path planning within complex environments, this paper introduces an enhanced A* pathfinding algorithm. The proposed algorithm improves search efficiency and accuracy by segmenting the path planning process into distinct stages, applying different heuristic functions at each stage, and integrating an artificial potential field to guide traversal, reducing unnecessary node exploration. Additionally, a random escape strategy prevents the algorithm from getting trapped in local minima. Various optimization methods refine the final path for practical applications. Simulation results demonstrate that, compared to heuristic A*, potential field, Weighted A*, and D* algorithms, the improved approach significantly reduces node traversal, execution time, and enhances planning success rates, making it well-suited for complex environments.