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ADD-RRV for motion planning in complex environments

Published online by Cambridge University Press:  14 May 2021

Peng Cai Open the ORCID record for Peng Cai [Opens in a new window] ,
Xiaokui Yue  and
Hongwen Zhang
Peng Cai
Affiliation:
National Key Laboratory of Aerospace Flight Dynamics, Northwestern Polytechnical University, Xi’an 710072, P. R. China
Xiaokui Yue*
Affiliation:
National Key Laboratory of Aerospace Flight Dynamics, Northwestern Polytechnical University, Xi’an 710072, P. R. China
Hongwen Zhang
Affiliation:
National Key Laboratory of Aerospace Flight Dynamics, Northwestern Polytechnical University, Xi’an 710072, P. R. China
*
*Corresponding author. Email: [email protected]
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Abstract

In this paper, we present a novel sampling-based motion planning method in various complex environments, especially with narrow passages. We use online the results of the planner in the ADD-RRT framework to identify the types of the local configuration space based on the principal component analysis (PCA). The identification result is then used to accelerate the expansion similar to RRV around obstacles and through narrow passages. We also propose a modified bridge test to identify the entrance of a narrow passage and boost samples inside it. We have compared our method with known motion planners in several scenarios through simulations. Our method shows the best performance across all the tested planners in the tested scenarios.

Keywords

Motion planningComplex environmentsLocal space identificationPrincipal component analysisBridge test
Type
Article
Information
Robotica , Volume 40 , Issue 1 , January 2022 , pp. 136 - 153
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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