Probabilistically complete planning with end-effector pose constraints

Berenson, Dmitry; Srinivasaz, Siddhartha S.

doi:10.1109/robot.2010.5509694

Cited by 18 publications

(24 citation statements)

References 15 publications

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“…As proved in [34], under the above assumptions, any RRTlike method able to densely sample F is probabilistically complete, even if the samples are not uniformly distributed. In the AtlasRRT, the sampling is obtained using the atlas.…”

Section: Probabilistic Completenessmentioning

confidence: 95%

“…These approaches are probabilistically complete [34] and easy to implement, but a uniform distribution of samples in the ambient space does not necessarily translate to a uniform distribution in the configuration space [24], and the branch extensions are many times prematurely blocked, as noted in [35], which reduces their efficiency. This problem is illustrated in Fig.…”

Section: Related Workmentioning

confidence: 99%

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Path Planning Under Kinematic Constraints by Rapidly Exploring Manifolds

2013

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Abstract-The situation arising in path planning under kinematic constraints, where the valid configurations define a manifold embedded in the joint ambient space, can be seen as a limit case of the well-known narrow corridor problem. With kinematic constraints the probability of obtaining a valid configuration by sampling in the joint ambient space is not low but null, which complicates the direct application of sampling-based path planners. This paper presents the AtlasRRT algorithm, a planner specially tailored for such constrained systems that builds on recently developed tools for higher-dimensional continuation. These tools provide procedures to define charts that locally parametrize a manifold and to coordinate the charts forming an atlas that fully covers it. AtlasRRT simultaneously builds an atlas and a bi-directional Rapidly-Exploring Random Tree (RRT), using the atlas to sample configurations and to grow the branches of the RRTs, and the RRTs to devise directions of expansion for the atlas. The efficiency of AtlasRRT is evaluated in several benchmarks involving high-dimensional manifolds embedded in large ambient spaces. The results show that the combined use of the atlas and the RRTs produces a more rapid exploration of the configuration space manifolds than existing approaches.

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Section: Probabilistic Completenessmentioning

confidence: 95%

Section: Related Workmentioning

confidence: 99%

Path Planning Under Kinematic Constraints by Rapidly Exploring Manifolds

2013

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“…Specifically, if r m or ∆θ m have a value close to zero, then it implies that the solution requires a sample from a manifold with nearly zero volume in SE (2). The probability of randomly sampling a point on such a manifold is close to zero, so we could not expect this approach to reliably work, much like the well-known "narrow passage problem" [16].…”

Section: A Theoretical Analysismentioning

confidence: 99%

“…We define "semi-unstructured" to mean an environment that contains obstacles with arbitrary geometry, but where the walkable ground is flat and even. The restriction to flat and even ground is due to a limitation in the current implementation of foot placement sampling, which is similar to the Task Space Region approach of Berenson et al [3] [2]. This restriction will be loosened in future work, possibly by utilizing a reachable space representation similar to the work of Tonneau et al [18].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Completeness of Randomized Possibility Graphs Applied to Bipedal Walking in Semi-unstructured Environments

Grey

Ames

Liu

2017

Robotics: Science and Systems XIII

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Abstract-We present a theoretical analysis of a recent whole body motion planning method, the Randomized Possibility Graph [8], which uses a high-level decomposition of the feasibility constraint manifold in order to rapidly find routes that may lead to a solution. These routes are then examined by lowerlevel planners to determine feasibility. In this paper, we show that this approach is probabilistically complete for bipedal robots performing quasi-static walking in "semi-unstructured" environments. Furthermore, we show that the decomposition into higher and lower level planners allows for a considerably higher rate of convergence in the probability of finding a solution when one exists. We illustrate this improved convergence with a series of simulated scenarios.

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“…Specifically, we would like to understand whether various sampling methods are able to fully explore the set of feasible configurations. To this end, we provided a proof for the probabilistic completeness of our planning method when planning with constraints on end-effector pose [41]. The proof shows that, given enough time, no part of the constraint manifold corresponding to a pose constraint will be left unexplored, regardless of the dimensionality of the pose constraint.…”

Section: Manipulation Planningmentioning

confidence: 99%

Herb 2.0: Lessons Learned From Developing a Mobile Manipulator for the Home

et al. 2012

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Abstract-We present the hardware design, software architecture, and core algorithms of HERB 2.0, a bimanual mobile manipulator developed at the Personal Robotics Lab at Carnegie Mellon University. We have developed HERB 2.0 to perform useful tasks for and with people in human environments. We exploit two key paradigms in human environments: that they have structure that a robot can learn, adapt and exploit, and that they demand general-purpose capability in robotic systems. In this paper, we reveal some of the structure present in everyday environments that we have been able to harness for manipulation and interaction, comment on the particular challenges on working in human spaces, and describe some of the lessons we learned from extensively testing our integrated platform in kitchen and office environments.

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Probabilistically complete planning with end-effector pose constraints

Cited by 18 publications

References 15 publications

Path Planning Under Kinematic Constraints by Rapidly Exploring Manifolds

Path Planning Under Kinematic Constraints by Rapidly Exploring Manifolds

Probabilistic Completeness of Randomized Possibility Graphs Applied to Bipedal Walking in Semi-unstructured Environments

Herb 2.0: Lessons Learned From Developing a Mobile Manipulator for the Home

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