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...h therefore necessary to develop strategies that are biased towards finding paths in narrow passages. To address this problem, the bridge test for sampling narrow passages was developed by Hsu et al. =-=[32]-=-. The key idea is to only add a sample to the graph when it is found to be between two obstacles. Algorithm 9 presents the bridge sampling algorithm. Two samples, cl and c2, are first sampled from the...

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...of uniform sampling is generally referred to as the “narrow passage problem” [9]. A great deal of research in sampling-based motion planning is concerned with the resolution of this problem [1], [4], =-=[8]-=-, [10]. The approaches proposed in the literature so far use heuristics to filter configurations after examination in the configuration space but prior to insertion into the roadmap. In this paper we ...

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...ve been proposed to deal with this issue (see Section 2). The most notable ones are the filtering and the retraction methods. Filtering methods include Gaussian sampling [BOvdS99] and the bridge test =-=[HJRS03]-=-. They over-sample configuration space, but then use heuristics to retain relatively few promising milestones. Retraction methods [AW96, HKL+98, LTA03, WAS99] use configurations sampled in collision s...

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...r planner [20]. Indeed, two of the key issues in the context of PRM are the power of the local planner [4], [11], [28], [32] and the way sampling is performed [3], [12], [13], [20], [21], [23], [24], =-=[27]-=-, [32], [38], [55]. In this paper we replace the local planner of PRM with a single query sampling-based motion planner. Among the single query planners that have been developed recently, Expansive Sp...

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... methods have been proposed to generate nodes in difficult areas, called narrow passages in C-space [10]. A number of methods specifically targeted at this problem have been proposed, e.g., [1], [7], =-=[9]-=-, [10], [22]. Some methods are designed to find narrow passages. In [5], narrow passages in the workspace are found and sampled more densely. This method works well when the workspace is similar to th...

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... through a narrow passage in the C-space. Attempts have been made to generate configurations in interesting areas of C-space that are difficult to discover using uniform random sampling. For example, =-=[1,4,9,16]-=- attempt to generate samples near the surface of C-space obstacles. In [21], samples are generated and then pushed toward the approximate medial axis of C-free, and in [7], the roadmap is generated fr...

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...roblems involving more than one robot. 2) State-Sampling Submodule: The state-sampling submodule contains different sampling strategies. Current implementations include uniform, Gaussian [14], bridge =-=[21]-=-, obstacle-based [13], and goal-biased methods. 3) Path Submodule: The path submodule corresponds to what is commonly referred to in motion planning as the local planner. Note that the local planner t...

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... spaces. Our results indicate DPES achieves high efficiency and only a negligible loss in accuracy. 1 Introduction Research in motion planning has in recent years focused on sampling-based algorithms =-=[1,5,9,13,14,17,20,22]-=- for solving problems involving multiple and highly complex robots. Such algorithms rely on an efficient sampling of the configuration space and compute nearest neighbors for the sampled points. In ge...