Boundary layer heuristic for search-based nonholonomic path planning in maze-like environments

Automatic valet parking is widely viewed as a milestone towards fully autonomous driving. One of the key problems is nonholonomic path planning in maze-like environments (e.g. parking lots). To balance efficiency and passenger comfort, the planner needs to minimize the length of the path as well as the number of gear shifts. Lattice A* search is widely adopted for optimal path planning. However, existing heuristics do not evaluate the nonholonomic dynamic constraint and the collision avoidance constraint simultaneously, which may mislead the search. To efficiently search the environment, the boundary layer heuristic is proposed which puts large cost in the area that the vehicle must shift gear to escape. Such area is called the boundary layer. A simple and efficient geometric method to compute the boundary layer is proposed. The admissibility and consistency of the additive combination of the boundary layer heuristic and existing heuristics are proved in the paper. The simulation results verify that the introduction of the boundary layer heuristic improves the search performance by reducing the computation time by 56.1%.