Sampling-based motion planners are an effective means for generating collision-free motion paths. However, the quality of these motion paths (with respect to quality measures such as path length, clearance, smoothness or energy) is often notoriously low, especially in high-dimensional configuration spaces. We introduce a simple algorithm for merging an arbitrary number of input motion paths into a hybrid output path of superior quality, for a broad and general formulation of path quality. Our approach is based on the observation that the quality of certain sub-paths within each solution may be higher than the quality of the entire path. A dynamic-programming algorithm, which we recently developed for comparing and clustering multiple motion paths, reduces the running time of the merging algorithm significantly. We tested our algorithm in motion-planning problems with up to 12 degrees of freedom. We show that our algorithm is able to merge a handful of input paths produced by several different motion planners to produce output paths of much higher quality.
Wilmarth @cite_1 improved the local clearance of sampled configurations by sampling closer to the medial axis. Nieuwenhuisen @cite_0 improved the optimal path length in probabilistic roadmaps by closing cycles only when they significantly reduce the (graph) path length between configurations, and Geraerts @cite_36 combined both approaches. In contrast to the above techniques, the approach we present below is not tailored for any specific criterion of path quality, and is designed to allow general formulations of path quality.
@cite_0 @cite_1 @cite_36
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Several motion planning methods using networks of randomly generated nodes in the free space have been shown to perform well in a number of cases, however their performance degrades when paths are required to pass through narrow passages in the free space. In previous work we proposed MAPRM, a method of sampling the configuration space in which randomly generated configurations, free or not, are retracted onto the medial axis of the free space without having to first compute the medial axis; this was shown to increase sampling in narrow passages. In this paper we give details of the MAPRM algorithm for the case of a rigid body moving in three dimensions, and show that the retraction may be carried out without explicitly computing the C-obstacles or the medial axis. We give theoretical and experimental results to show this improves performance on problems involving narrow corridors and compare the performance to uniform random sampling from the free space. Our goal is to create road maps that are particularly suited for motion planning in virtual environments. We use our reachability roadmap method to compute an initial, resolution complete roadmap. This roadmap is small which keeps query times and memory consumption low. However, for use in virtual environments, there are additional criteria that must be satisfied. In particular, we require that the roadmap contains useful cycles. These provide short paths and alternative routes which allow for variation in the routes a moving object can take. We will show how to incorporate such cycles. In addition, we provide high-clearance paths by retracting the edges of the roadmap to the medial axis. Since all operations are performed in a preprocessing phase, high-quality paths can be extracted in real-time as is required in interactive applications