摘要
针对传统A^(*)算法和动态窗口算法在叉车式自主导航车(AGV)路径规划中搜索慢、冗余点多和灵活性差等问题,改进传统A^(*)算法和动态窗口算法;提出融合算法开展叉车式AGV的路径规划和避障研究,使其在规划全局路径的同时规划局部路径,实现兼具全局避障的最优路径规划。通过与4种算法的仿真实验结果对比,融合算法能够达到全局路径最优且轨迹平滑性最好,同等条件下能够减少5.73%路径长度和节约40.90%时间。
For slow search,plenty redundancy points and poor flexibility of traditional A^(*)algorithm and dynamic window algorithm in forklift autonomous guided vehicle(AGV)path planning,the traditional A^(*)algorithm and dynamic window algorithm is upgraded,and the fusion algorithm is proposed to carry out the path planning and obstacle avoidance research of forklift AGV,so that the local path is planned while the global path is planned to realize the optimal path planning with global obstacle avoidance.The comparison conducted among the simulation results of the four algorithms shows that the proposed fusion algorithm can achieve the optimal global path and the best trajectory smoothness,with a reduction of path length by 5.73%and time saving by 40.90%under the same conditions.
作者
严小虎
邱亚峰
田浩杰
李前位
刘康
YAN Xiaohu;QIU Yafeng;TIAN Haojie;LI Qianwei;LIU Kang(Nanjing University of Science and Technology,Nanjing 210094,China)
出处
《机械制造与自动化》
2025年第1期119-122,127,共5页
Machine Building & Automation
基金
国防预研基金项目(1171011485)。
