Efficient path planning for automated guided vehicles using A* (Astar) algorithm incorporating turning costs in search heuristic

The path planned for an automated guided vehicle in, for example, a production facility is often the lowest-cost path in a (weighted) geometric graph. The weights in the graph may represent a distance or travel time. Sometimes turning costs are taken into account; turns (and decelerations before and accelerations after turning) take time, so it is desirable to minimise turns in the path. Several well-known algorithms can be used to find the lowest-cost path in a geometric graph. In this paper, we focus on the A $ ^* $ ∗ algorithm, which uses an (internal) search heuristic to find the lowest-cost path. In the current literature, generally, either turning costs are not taken into account in the heuristic or the heuristic can only be used for specific graph structures. We propose an improved heuristic for the A $ ^* $ ∗ algorithm that can be used to find the lowest-cost path in a geometric graph with turning costs. Our heuristic is proven to be monotone and admissible. Moreover, our heuristic provides a higher lower bound estimate for the actual costs compared to other heuristics found in the literature, causing the lowest-cost path to be found faster (i.e. with less iterations). We validate this through an extensive comparative study.