Landmark Guided Probabilistic Roadmap Queries
This work addresses motion planning efficiency for robotics applications, but it is incremental as it builds on existing PRM methods with a heuristic improvement.
The paper tackled the problem of reducing query phase run-time in probabilistic roadmap (PRM) motion planning by introducing a landmark-based heuristic that uses minimum spanning trees to approximate shortest path costs, resulting in faster queries in multi-query applications compared to Dijkstra's or A* with conventional heuristics.
A landmark based heuristic is investigated for reducing query phase run-time of the probabilistic roadmap (\PRM) motion planning method. The heuristic is generated by storing minimum spanning trees from a small number of vertices within the \PRM graph and using these trees to approximate the cost of a shortest path between any two vertices of the graph. The intermediate step of preprocessing the graph increases the time and memory requirements of the classical motion planning technique in exchange for speeding up individual queries making the method advantageous in multi-query applications. This paper investigates these trade-offs on \PRM graphs constructed in randomized environments as well as a practical manipulator simulation.We conclude that the method is preferable to Dijkstra's algorithm or the ${\rm A}^*$ algorithm with conventional heuristics in multi-query applications.