Reachability-based analysis for Probabilistic Roadmap planners

Gerærts, Roland; Overmars, Mark H.

doi:10.1016/j.robot.2007.06.002

Cited by 28 publications

(20 citation statements)

References 19 publications

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“…These results are in Figures 7,8,9,10,11,12,13,14, and 15 (error bars show a one-sided confidence interval of 95%). Recall that roadmaps with more edges and a higher connectivity are beneficial and that the number of CD-Calls is indicative of the cost associated with connecting the roadmap.…”

Section: B Comparison Of Connection Methodsmentioning

confidence: 78%

“…In [12], Geraerts and Overmars perform a reachabilitybased analysis of the PRM method. In this study they evaluate existing node generation and connection methods based on how well they cover environments and how connected the roadmaps they produce are.…”

Section: A Probabilistic Roadmap Methodsmentioning

confidence: 99%

“…It is also commonly used in biophysical simulations where certain cutoffs are standard, e.g., a RMSD of atom positions between molecular conformations [25]. Geraerts and Overmars [15], [14] include a distance method in a study of the impact of sampling, node selection/adding strategy and local planning on coverage and connectivity of PRMs. Their study shows that the k-closest method is fairly well suited for generating connected roadmaps.…”

Section: B Candidate Selection Approachesmentioning

confidence: 99%

“…It has been previously shown that edge per edge, longer edges contribute more to roadmap quality than shorter edges [12]. At the same time, it also possible that these longer edges will be more difficult to connect, and that the advantage of having having longer edges will be offset by the fact that fewer edges are generated.…”

Section: B Localrand Candidate Neighbor Selection Policymentioning

confidence: 99%

“…Another difference is that LocalRand will likely produce roadmaps with longer edges that k-closest. This may be beneficial as it has previously been shown that longer edges have a positive impact on roadmap quality [12].…”

Section: Introductionmentioning

confidence: 99%

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Local randomization in neighbor selection improves PRM roadmap quality

McMahon

Jacobs

Boyd

et al. 2012

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-Probabilistic Roadmap Methods (PRMs) are one of the most used classes of motion planning methods. These sampling-based methods generate robot configurations (nodes) and then connect them to form a graph (roadmap) containing representative feasible pathways. A key step in PRM roadmap construction involves identifying a set of candidate neighbors for each node. Traditionally, these candidates are chosen to be the k-closest nodes based on a given distance metric. In this paper, we propose a new neighbor selection policy called LocalRand(k, k ′ ), that first computes the k ′ closest nodes to a specified node and then selects k of those nodes at random. Intuitively, LocalRand attempts to benefit from random sampling while maintaining the higher levels of local planner success inherent to selecting more local neighbors. We provide a methodology for selecting the parameters k and k ′ . We perform an experimental comparison which shows that for both rigid and articulated robots, LocalRand results in roadmaps that are better connected than the traditional k-closest policy or a purely random neighbor selection policy. The cost required to achieve these results is shown to be comparable to k-closest. I. IntroductionThe motion planning problem involves finding a valid path for a movable object (robot) from a start to a goal configuration in a given environment. Motion planning is an important component of many applications, including computer-aided design Sampling-based methods have been able to solve many motion planning problems that exact methods cannot. One of the most common randomized methods is the Probabilistic Roadmap Method, or PRM [19]. This method randomly generates valid samples (nodes) in an environment's configuration space (C-space) and then attempts to connect nearby pairs of nodes using a local planner, a simplified planner whose objective is to generate and validate transitions between the the specified pair of nodes. The resulting graph, or roadmap, encodes representative feasible paths in C-space and can be queried to obtain valid paths in the environment.One of the key steps in PRM construction is node connection. Ideally, roadmap connectivity should reflect the connectivity of the underlying C-space. From this perspective, the best strategy would be to attempt to connect all θ (n 2 ) pairs of nodes. However, the cost of all these connection attempts is not feasible for any but the simplest of problems. Hence, the selection of candidates for local transitions (neighbors) is crucial to both roadmap quality and efficiency.The objective of a good neighbor selection strategy is to identify pairs of configurations that have a high probability of being connectible by the local planner and that are useful in terms of producing good quality roadmaps. The most commonly used method for neighbor selection in PRMs uses nearest-neighbor search to select the k nodes that are closest to the node in question, where k is typically some relatively small, fixed constant, typically between 5 and 25 [10].

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Section: B Comparison Of Connection Methodsmentioning

confidence: 78%

Section: A Probabilistic Roadmap Methodsmentioning

confidence: 99%

Section: B Candidate Selection Approachesmentioning

confidence: 99%

Section: B Localrand Candidate Neighbor Selection Policymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Local randomization in neighbor selection improves PRM roadmap quality

McMahon

Jacobs

Boyd

et al. 2012

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Using probabilistic roadmaps in changing environments

Rantanen

Juhola

2013

Computer Animation & Virtual

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In this paper, we examine how a path planning problem can be solved in changing environments using probabilistic roadmap planners. A probabilistic roadmap is built in static environment where all obstacles are known in advance, but we show that a roadmap can be built in such a way that it works well even when new obstacles are added to the workspace. However, our experiments show that the roadmap graph must be built carefully. We compare three different methods that are used to decide which edges are added to the roadmap graph to connect the nodes. One of these is a distance‐based method, which we present in this paper. In the tests, we built a roadmap by using only the static obstacles. Then, we added additional obstacles to the environment and tested how well the roadmap still worked. The tests showed that our distance‐based method worked quickly and that it produced roadmaps, which could be used to find a path amid additional obstacles with a high success rate.Copyright © 2013 John Wiley & Sons, Ltd.

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Asymptotically Near-Optimal Is Good Enough for Motion Planning

Marble

Bekris

2016

Springer Tracts in Advanced Robotics

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Reachability-based analysis for Probabilistic Roadmap planners

Cited by 28 publications

References 19 publications

Local randomization in neighbor selection improves PRM roadmap quality

Local randomization in neighbor selection improves PRM roadmap quality

Using probabilistic roadmaps in changing environments

Asymptotically Near-Optimal Is Good Enough for Motion Planning

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