Abstract: In order to solve the problems of low efficiency and large tracking errors in outdoor unmanned forklift path planning, a kinematic model of outdoor unmanned forklift is established. By constructing a path cost function and combining simulated annealing optimization, redundant nodes are removed to improve the search efficiency and path smoothness of the A* algorithm. On this basis, with speed and heading angle as input variables, a fuzzy controller is designed to dynamically adjust the preview distance of the pure tracking algorithm, and simulation and real vehicle experiments are carried out. The results indicate that the improved A* path planning algorithm reduces path length by 4.5% and turns by 98.7%. The dynamic preview distance pure tracking algorithm using fuzzy control reduces the maximum error by 58.7% in curve tracking, significantly improves path smoothness and tracking accuracy, and can be effectively applied to complex industrial application scenarios.

Key words: outdoor unmanned forklift; simulated annealing; path planning; pure tracking

摘要： 为了解决室外无人叉车路径规划效率低、跟踪误差大的问题，建立室外无人叉车运动 学模型，通过构造路径成本函数并结合模拟退火优化，去除冗余节点，以提升 A*算法的搜索 效率和路径平滑度。在此基础上，以无人叉车的速度和航向角为输入变量，设计模糊控制器 动态调节纯跟踪算法的预瞄距离，开展仿真和实车试验。结果表明，改进的 A*路径规划算法 路径长度缩短 4.5%，转角减小 98.7%。应用模糊控制的动态预瞄距离纯跟踪算法，在曲线跟 踪中最大误差降低 58.7%，显著提升了路径平滑度与跟踪精度，能够满足作业任务要求。

关键词: 室外无人叉车；模拟退火；路径规划；纯跟踪