This paper proposes two novel path planning algorithms, Roadmap Hybrid A* and Waypoints Hybrid A*, for car-like autonomous vehicles in logistics and industrial contexts with obstacles (e.g., pallets or containers) and narrow corridors. Roadmap Hybrid A* combines Hybrid A* with a graph search algorithm applied to a static roadmap. The former enables obstacle avoidance and flexibility, whereas the latter provides greater robustness, repeatability, and computational speed. Waypoint Hybrid A*, on the other hand, generates waypoints using a topological map of the environment to guide Hybrid A* to the target pose, reducing complexity and search time. Both algorithms enable predetermined control over the shape of desired parts of the path, for example, to obtain precise docking maneuvers to service machines and to eliminate unnecessary steering changes produced by Hybrid A* in corridors, thanks to the roadmap and/or the waypoints. To evaluate the performance of these algorithms, we conducted a simulation study in an industrial plant where a robot must navigate narrow corridors to serve machines in different areas. In terms of computational time, total length, reverse length path, and other metrics, both algorithms outperformed the standard Hybrid A*.

翻译：本文针对在存在障碍物（如托盘或集装箱）及窄走廊的物流与工业场景中的类车自主车辆，提出了两种新型路径规划算法：道路混合A*与航点混合A*。道路混合A*将混合A*算法与应用于静态道路图的图搜索算法相结合：前者实现避障能力与灵活性，后者增强鲁棒性、可重复性与计算速度。航点混合A*则利用环境拓扑地图生成航点，引导混合A*算法抵达目标位姿，从而降低复杂度与搜索时间。两种算法均能实现对路径特定部分的形状进行预定义控制（例如获取精准对接机动以服务机器），并借助道路图或航点消除混合A*在走廊中产生的非必要转向变化。为评估算法性能，我们在某工业厂房中开展仿真研究——机器人需穿越窄走廊以服务不同区域的机器。在计算时间、总路径长度、倒车路径长度及其他指标上，两种算法均优于标准混合A*。