Landmark guided probabilistic roadmap queries

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 A* algorithm with conventional heuristics in multi-query applications.

I. Introduction

The probabilistic roadmap (PRM) [1] is a cornerstone of robot motion planning. It is widely used in practice or as the foundation for more complex planning algorithms. The method is divided into two phases: the PRM graph is first constructed followed by, potentially multiple, shortest path queries on this graph to solve motion planning problems. For a single motion planning query, a feasibility checking subroutine executed repeatedly during PRM construction dominates run-time. However, once the PRM is constructed it can be reused for multiple motion planning queries or modified slightly according to minor changes in the environment. Applicability to multi-query problems is one of the advantages of the PRM over tree-based planners such as Rapidly exploring Random Trees (RRT) [2] and Expansive Space Trees (EST) [3] which are tailored to single-query problems.