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

Grey, Michael X. and Ames, Aaron D. and Liu, C. Karen (2017) Probabilistic Completeness of Randomized Possibility Graphs Applied to Bipedal Walking in Semi-unstructured Environments. . (Unpublished) http://resolver.caltech.edu/CaltechAUTHORS:20190201-160913893

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Abstract

We present a theoretical analysis of a recent whole body motion planning method, the Randomized Possibility Graph, 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 lower-level 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.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
http://arxiv.org/abs/1702.00425arXivDiscussion Paper
ORCID:
AuthorORCID
Ames, Aaron D.0000-0003-0848-3177
Record Number:CaltechAUTHORS:20190201-160913893
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20190201-160913893
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:92613
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:04 Feb 2019 15:45
Last Modified:04 Feb 2019 15:45

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