基于改进的A*算法与动态窗口法的移动机器人路径规划

提出了一种改进的A^*算法与动态窗口法相结合的混合算法，以解决移动机器人在多目标复杂环境中的路径规划问题．首要，为了提升算法的运行效率，实现单次规划的路径可通过多个目标点，同时提升路径平滑处理的灵活性并满足移动机器人非完整约束条件，本文利用目标成本函数对所有目标进行优先级判定，进而利用改进的A^*算法规划一条经过多个目标点的最优路径，同时采用自适应圆弧优化算法与加权障碍物步长调节算法，有效地将路径长度缩短5%，转折角总度数降低26.62%．其次，为实现移动机器人在动态复杂环境中局部避障并追击动态目标点．提出将改进动态窗口算法与全局路径规划信息相结合的在线路径规划法，采用预瞄偏差角追踪法成功捕捉移动目标点，并提升了路径规划效率．最后，对所提方法进行仿真实验，结果表明该方法能够在复杂动态环境中更有效地实现路径规划．

Mobile Robot Path Planning Based on Improved A* Algorithm and Dynamic Window Method

A hybrid algorithm combining the improved A^* algorithm and the dynamic window method is proposed, to solve the problem of mobile robot path planning in multi-target complex environments. In order to improve the algorithm efficiency by planning a path passing through multiple target points in a single run, and to improve the flexibility of path smoothing and meet the non-holonomic constraints of mobile robots, the target cost function is used to prioritize all targets firstly. Furthermore, the improved A^* algorithm is used to plan an optimal path passing through multiple target points. Meanwhile, the adaptive arc optimization algorithm and the weighted obstacle step adjustment algorithm are used to effectively shorten the path length by 5% and reduce the total turning angle by 26.62%. Secondly, an online path planning method combining the improved dynamic window algorithm and the global path planning information is proposed for mobile robots to avoid the local obstacles and pursue the dynamic target points in dynamic complex environments. The preview-deviation-yaw based tracking method is used to successfully capture the moving target points and improve the path planning efficiency. Finally, the simulation experiments with the proposed method are carried out, and the results show that it can achieve the path planning more effectively in complex dynamic environments.