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Title: Improved Performance of Asymptotically Optimal Rapidly Exploring Random Trees

Journal Article · · Journal of Dynamic Systems, Measurement, and Control
DOI: https://doi.org/10.1115/1.4040970 · OSTI ID:1467259
ORCiD logo [1]; ORCiD logo [1];  [2]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  2. Univ. of California, San Diego, CA (United States). Dept. of Mechanical Engineering
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Three algorithms that improve the performance of the asymptotically optimal Rapidly exploring Random Tree (RRT*) are presented here. First, we introduce the Goal Tree (GT) algorithm for motion planning in dynamic environments where unexpected obstacles appear sporadically. The GT reuses the previous RRT* by pruning the affected area and then extending the tree by drawing samples from a shadow set. The shadow is the subset of the free configuration space containing all configurations that have geodesics ending at the goal and are in conflict with the new obstacle. Smaller, well defined, sampling regions are considered for Euclidean metric spaces and Dubins' vehicles. Next, the Focused-Refinement (FR) algorithm, which samples with some probability around the first path found by an RRT*, is defined. The third improvement is the Grandparent-Connection (GP) algorithm, which attempts to connect an added vertex directly to its grandparent vertex instead of parent. The GT and GP algorithms are both proven to be asymptotically optimal. Finally, the three algorithms are simulated and compared for a Euclidean metric robot, a Dubins' vehicle, and a seven degrees-of-freedom manipulator.

Research Organization:
Los Alamos National Laboratory (LANL)
Sponsoring Organization:
USDOE
Grant/Contract Number:
AC52-06NA25396
OSTI ID:
1467259
Report Number(s):
LA-UR-17-24020
Journal Information:
Journal of Dynamic Systems, Measurement, and Control, Journal Name: Journal of Dynamic Systems, Measurement, and Control Journal Issue: 1 Vol. 141; ISSN 0022-0434
Publisher:
ASMECopyright Statement
Country of Publication:
United States
Language:
English

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