research-article

RRTX

Authors:

Emilio FrazzoliAuthors Info & Claims

International Journal of Robotics Research, Volume 35, Issue 7

Pages 797 - 822

https://doi.org/10.1177/0278364915594679

Published: 01 June 2016 Publication History

Abstract

Dynamic environments have obstacles that unpredictably appear, disappear, or move. We present the first sampling-based replanning algorithm that is asymptotically optimal and single-query designed for situation in which a priori offline computation is unavailable. Our algorithm, RRT^X, refines and repairs the same search-graph over the entire duration of navigation in contrast to previous single-query replanning algorithms that prune and then regrow some or all of the search-tree. Whenever obstacles change and/or the robot moves, a graph rewiring cascade quickly remodels the existing search-graph and repairs its shortest-path-to-goal sub-tree to reflect the new information. Both graph and tree are built directly in the robot's state-space; thus, the resulting plans respect the kinematics of the robot and continue to improve during navigation. RRT^X is probabilistically complete and makes no distinction between local and global planning, yet it reacts quickly enough for real-time high-speed navigation through unpredictably changing environments. Low information transfer time is essential for enabling RRT^X to react quickly in dynamic environments; we prove that the information transfer time required to inform a graph of size n about an ε-cost decrease is On log n for RRT^X-faster than other current asymptotically optimal single-query algorithms we prove RRT* is

? n {\frac{n}{\log n}}^{1 / D}

and RRT^# is

?

n log² n. In static environments RRT^X has the same amortized runtime as RRT and RRT*, ?log n, and is faster than RRT^#,

?

log² n. In order to achieve Olog n iteration time, each node maintains a set of Olog n expected neighbors, and the search-graph maintains ε-consistency for a predefined ε. Experiments and simulations confirm our theoretical analysis and demonstrate that RRT^X is useful in both static and dynamic environments.

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cover image International Journal of Robotics Research

International Journal of Robotics Research Volume 35, Issue 7

6 2016

127 pages

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Copyright © © The Authors 2015.

Publisher

Sage Publications, Inc.

United States

Publication History

Published: 01 June 2016

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Wang CZhao SZheng TCai HZhu Y(2025)Online Parallel Optimization Motion Planner for Robots Under Moving Obstacle CircumstancesIntelligent Robotics and Applications10.1007/978-981-96-0798-3_17(214-228)Online publication date: 25-Jan-2025
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