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This paper proposes two novel path planning algorithms, Roadmap Hybrid A* andWaypoints Hybrid A*, for car-like autonomous vehicles in logistics andindustrial contexts with obstacles (e.g., pallets or containers) and narrowcorridors. Roadmap Hybrid A* combines Hybrid A* with a graph search algorithmapplied to a static roadmap. The former enables obstacle avoidance andflexibility, whereas the latter provides greater robustness, repeatability, andcomputational speed. Waypoint Hybrid A*, on the other hand, generates waypointsusing a topological map of the environment to guide Hybrid A* to the targetpose, reducing complexity and search time. Both algorithms enable predeterminedcontrol over the shape of desired parts of the path, for example, to obtainprecise docking maneuvers to service machines and to eliminate unnecessarysteering changes produced by Hybrid A* in corridors, thanks to the roadmapand/or the waypoints. To evaluate the performance of these algorithms, weconducted a simulation study in an industrial plant where a robot must navigatenarrow corridors to serve machines in different areas. In terms ofcomputational time, total length, reverse length path, and other metrics, bothalgorithms outperformed the standard Hybrid A*.

Improved path planning algorithms for non-holonomic autonomous vehicles  in industrial environments with narrow corridors: Roadmap Hybrid A* and  Waypoints Hybrid B*. Roadmap hybrid A* and Waypoints hybrid A* Pseudocodes