Hybrid PRM Sampling with a Cost-Sensitive Adaptive Strategy

Hsu, David; Sánchez-Ante, Gildardo; Sun, Zheng

doi:10.1109/robot.2005.1570712

Cited by 77 publications

(66 citation statements)

References 12 publications

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“…Another recent adaptive approach is to construct a meta-planner with several tools at its disposal; such planners employ multiple sampling strategies (Hsu et al, 2005) or multiple randomized roadmap planners (Morales et al, 2004), based on a prediction of which approach is most effective in a given setting.…”

Section: Adaptive Sampling Strategiesmentioning

confidence: 99%

Realtime Informed Path Sampling for Motion Planning Search

Knepper

Mason

2016

Springer Tracts in Advanced Robotics

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Mobile robot motions often originate from an uninformed path sampling process such as random or low-dispersion sampling. We demonstrate an alternative approach to path sampling that closes the loop on the expensive collision-testing process. Although all necessary information for collision-testing a path is known to the planner, that information is typically stored in a relatively unavailable form in a costmap or obstacle map. By summarizing the most salient data in a more accessible form, our process delivers a denser sampling of the free path space per unit time than open-loop sampling techniques. We obtain this result by probabilistically modeling-in real time and with minimal information-the locations of obstacles and free space, based on collision-test results. We present CALM, the combined adaptive locality model, along with an algorithm to bias path sampling based on the model's predictions. We provide experimental results in simulation for motion planning on mobile robots, demonstrating up to a 330% increase in paths surviving collision test.

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Section: Adaptive Sampling Strategiesmentioning

confidence: 99%

Realtime Informed Path Sampling for Motion Planning Search

Knepper

Mason

2016

Springer Tracts in Advanced Robotics

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“…(Hsu et al, 2005;Denny and Amato, 2013), and comparative studies, e.g. (Geraerts and Overmars, 2004;Hsu et al, 2006).…”

Section: Application Examplesmentioning

confidence: 99%

“…(Boor et al, 1999;Hsu et al, 2003), or those that bias the sampling using the information gathered during the construction of the roadmap or tree, e.g. (Kavraki et al, 1996;Hsu et al, 2005). In order to improve the performance of sampling-based planners, dimension-reduction techniques have also been proposed, e.g.…”

Section: Introductionmentioning

confidence: 99%

Path planning for grasping operations using an adaptive PCA-based sampling method

2013

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The planning of collision-free paths for a hand-arm robotic system is a difficult issue due to the large number of degrees of freedom involved and the cluttered environment usually encountered near grasping configurations. To cope with this problem, this paper presents a novel importance sampling method based on the use of Principal Component Analysis (PCA) to enlarge the probability of finding collision-free samples in these difficult regions of the Configuration Space with low clearance. By using collision-free samples near the goal, PCA is periodically applied in order to obtain a sampling volume near the goal that better covers the free space, improving the efficiency of sampling-based path planning methods. The approach has been tested with success on a hand-arm robotic system composed of a four-finger anthropomorphic mechanical hand (17 joints with 13 independent degrees of freedom) and an industrial robot (6 independent degrees of freedom).

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“…The Bridge test [19] for example concentrates samples in difficult areas but it also generates some samples in open areas. Several combinations of existing sampling strategies are suited to serve as a hybrid technique, see the paper of Hsu et al [20] for an elaboration.…”

Section: Samplingmentioning

confidence: 99%

Reachability-based analysis for Probabilistic Roadmap planners

Gerærts

Overmars

2007

Robotics and Autonomous Systems

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In the last fifteen years, sampling-based planners like the Probabilistic Roadmap Method (PRM) have proved to be successful in solving complex motion planning problems. While theoretically, the complexity of the motion planning problem is exponential in the number of degrees of freedom, sampling-based planners can successfully handle this curse of dimensionality in practice. We give a reachability-based analysis for these planners which leads to a better understanding of the success of the approach. This analysis compares the techniques based on coverage and connectivity of the free configuration space. The experiments show, contrary to general belief, that the main challenge is not getting the free space covered but getting the nodes connected, especially when the problems get more complicated, e.g. when a narrow passage is present. By using this knowledge, we can tackle the narrow passage problem by incorporating a refined neighbor selection strategy, a hybrid sampling strategy, and a more powerful local planner, leading to a considerable speed-up.

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Hybrid PRM Sampling with a Cost-Sensitive Adaptive Strategy

Cited by 77 publications

References 12 publications

Realtime Informed Path Sampling for Motion Planning Search

Realtime Informed Path Sampling for Motion Planning Search

Path planning for grasping operations using an adaptive PCA-based sampling method

Reachability-based analysis for Probabilistic Roadmap planners

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