Motion planning for carlike robots using a probabilistic learning approach

In this article, a recently developed learning approach for robot motion planning is extended and applied to two types of carlike robots: normal ones, and robots that can only move forward In this learning approach the motion planning process is split into two phases: the learning phase and the query phase. In the learning phase, a probabilistic roadmap is incrementally constructed in configuration space. This roadmap is an undirected graph where nodes correspond to randomly chosen configurations in free space and edges correspond to simple collision-free paths between the nodes. These simple motions are computed using a fast local method. In the query phase, this roadmap can be used to find paths between different pairs of configurations. The approach can be applied to normal carlike robots (with nonholonomic constraints) by using suitable local methods that compute paths feasible for the robots. Application to carlike robots that can move only forward demands a more fundamental adaptation of the learning method; that is, the roadmaps must be stored in directed graphs instead of undirected ones. We have implemented the planners, and we present experimental results that demonstrate their efficiency for both robot types, even in cluttered workspaces. Also, we prove probabilistic completeness of the planners.