Accelerated A* Trajectory Planning: Grid-based Path Planning Comparison

The contribution of the paper is a high performance pathplanning algorithm designed to be used within a multi-agent planning framework solving a UAV collision avoidance problem. Due to the lack of benchmark examples and available algorithms for 3D+time planning, the algorithm performance has been compared in the classical domain of path planning in grids with blocked and unblocked cells. The Accelerated A* algorithm has been compared against the Theta* path planner, Rapid-Exploring Random Trees-based planners and the original A* searching in graphs providing the shortest any-angle paths. Experiments have shown that Accelerated A* finds the shortest paths in all scenarios including many randomized configurations. Experiments document that Accelerated A* is slower than Theta* and RRT-based planers in many cases, but it is faster than the original A*. In comparison to the original A*, Accelerated A* reduces memory requirements which makes it usable for large-scale worlds where the original A* is not usable.