MST-based method for 6DOF rigid body motion planning in narrow passages

Nowakiewicz, Michal

doi:10.1109/iros.2010.5650577

Cited by 7 publications

(3 citation statements)

References 14 publications

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“…Accordingly, many heuristic strategies that bias sampling towards narrow passages have been proposed based on geometric properties of configuration spaces and sampling history. Representative approaches include: retraction onto the medial-axis [10]- [12] and the boundary [13] of the free space 1 ; cell decomposition based sampling [14]- [17]; bridge-test sampling [18]; Gaussian sampling [19]; entropy based sampling [20]; artificial potential biased sampling [21]; human-guided sampling [22]; simultanous sampling of the free space and configuration space obstacles [23]; sampling using collision information [5], [24]; and their combinations [25].…”

Section: A Motivation and Prior Literaturementioning

confidence: 99%

Sensory steering for sampling-based motion planning

Arslan

Pacelli

Koditschek

2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Sampling-based algorithms offer computationally efficient, practical solutions to the path finding problem in high-dimensional complex configuration spaces by approximately capturing the connectivity of the underlying space through a (dense) collection of sample configurations joined by simple local planners. In this paper, we address a long-standing bottleneck associated with the difficulty of finding paths through narrow passages. Whereas most prior work considers the narrow passage problem as a sampling issue (and the literature abounds with heuristic sampling strategies) very little attention has been paid to the design of new effective local planners. Here, we propose a novel sensory steering algorithm for sampling-based motion planning that can "feel" a configuration space locally and significantly improve the path planning performance near difficult regions such as narrow passages. We provide computational evidence for the effectiveness of the proposed local planner through a variety of simulations which suggest that our proposed sensory steering algorithm outperforms the standard straight-line planner by significantly increasing the connectivity of random motion planning graphs.

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Section: A Motivation and Prior Literaturementioning

confidence: 99%

Sensory steering for sampling-based motion planning

Arslan

Pacelli

Koditschek

2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…Among the first group, for example, [9], combines the idea of Visibility-PRM [10] with multiple RRT trees. In [11], an MST is created with weighted C space cells, where weights depend on distance to boundary. In [12], an environment-guided variant of RRT is designed for kinodynamic robot systems that estimates the probability of collision under uncertainty in control and sensing.…”

Section: Introductionmentioning

confidence: 99%

A Levy Flight based Narrow Passage Sampling Method for Probabilistic Roadmap Planners

Shukla¹,

Kumar²,

Bera³

et al. 2021

Preprint

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Sampling based probabilistic roadmap planners (PRM) have been successful in motion planning of robots with higher degrees of freedom, but may fail to capture the connectivity of the configuration space in scenarios with a critical narrow passage. In this paper, we show a novel technique based on Lévy Flights to generate key samples in the narrow regions of configuration space, which, when combined with a PRM, improves the completeness of the planner. The technique substantially improves sample quality at the expense of a minimal additional computation, when compared with pure random walk based methods, however, still outperforms state of the art random bridge building method, in terms of number of collision calls, computational overhead and sample quality. The method is robust to the changes in the parameters related to the structure of the narrow passage, thus giving an additional generality. A number of 2D & 3D motion planning simulations are presented which shows the effectiveness of the method.

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“…The dominant algorithmic paradigm of these planners has been variants of the Sampling Approach such as PRM, EST, RRT, SRT, etc (see [5, p. 201]). Because this bit of information is not built into the specification of such algorithms, it has led to non-termination issues and a large literature addressing the "narrow passage problem" (e.g., [21,8]). Our present paper is based on the Subdivision Approach.…”

Section: Introductionmentioning

confidence: 99%

Rods and Rings: Soft Subdivision Planner for R^3 x S^2

Hsu,

Chiang,

Yap

2019

Preprint

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We consider path planning for a rigid spatial robot moving amidst polyhedral obstacles. Our robot is either a rod or a ring. Being axially-symmetric, their configuration space is R 3 × S 2 with 5 degrees of freedom (DOF). Correct, complete and practical path planning for such robots is a long standing challenge in robotics. While the rod is one of the most widely studied spatial robots in path planning, the ring seems to be new, and a rare example of a non-simply-connected robot. This work provides rigorous and complete algorithms for these robots with theoretical guarantees. We implemented the algorithms in our open-source Core Library. Experiments show that they are practical, achieving near real-time performance. We compared our planner to state-of-the-art sampling planners in OMPL [30].Our subdivision path planner is based on the twin foundations of ε-exactness and soft predicates. Correct implementation is relatively easy. The technical innovations include subdivision atlases for S 2 , introduction of Σ2 representations for footprints, and extensions of our feature-based technique for "opening up the blackbox of collision detection".

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MST-based method for 6DOF rigid body motion planning in narrow passages

Cited by 7 publications

References 14 publications

Sensory steering for sampling-based motion planning

Sensory steering for sampling-based motion planning

A Levy Flight based Narrow Passage Sampling Method for Probabilistic Roadmap Planners

Rods and Rings: Soft Subdivision Planner for R^3 x S^2

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