Wang Te, Li Aijuan, Guo Dongjin, Du Guangkai, He Weikai
School of Automotive Engineering, Shandong Jiaotong University, Jinan 250357, China.
Research and Development Department, Shandong Huali Electromechanical Co., Ltd., Jining 250101, China.
Sensors (Basel). 2024 Mar 21;24(6):2011. doi: 10.3390/s24062011.
Designed to meet the demands of AGV global optimal path planning and dynamic obstacle avoidance, this paper proposes a combination of an improved A* algorithm and dynamic window method fusion algorithm. Firstly, the heuristic function is dynamically weighted to reduce the search scope and improve the planning efficiency; secondly, a path-optimization method is introduced to eliminate redundant nodes and redundant turning points in the path; thirdly, combined with the improved A* algorithm and dynamic window method, the local dynamic obstacle avoidance in the global optimal path is realized. Finally, the effectiveness of the proposed method is verified by simulation experiments. According to the results of simulation analysis, the path-planning time of the improved A* algorithm is 26.3% shorter than the traditional A* algorithm, the search scope is 57.9% less, the path length is 7.2% shorter, the number of path nodes is 85.7% less, and the number of turning points is 71.4% less. The fusion algorithm can evade moving obstacles and unknown static obstacles in different map environments in real time along the global optimal path.
为满足AGV全局最优路径规划和动态避障的需求，本文提出了一种改进A算法与动态窗口法融合算法。首先，对启发式函数进行动态加权，缩小搜索范围，提高规划效率；其次，引入路径优化方法，消除路径中的冗余节点和冗余转折点；第三，结合改进A算法和动态窗口法，实现全局最优路径中的局部动态避障。最后，通过仿真实验验证了所提方法的有效性。根据仿真分析结果，改进A算法的路径规划时间比传统A算法缩短了26.3%，搜索范围减少了57.9%，路径长度缩短了7.2%，路径节点数减少了85.7%，转折点数量减少了71.4%。该融合算法能够沿全局最优路径实时躲避不同地图环境中的移动障碍物和未知静态障碍物。
