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Title: Improved Performance of Asymptotically Optimal Rapidly Exploring Random Trees

Abstract

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.

Authors:
ORCiD logo [1]; ORCiD logo [1];  [2]
+ Show Author Affiliations
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  2. Univ. of California, San Diego, CA (United States). Dept. of Mechanical Engineering
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1467259
Report Number(s):
[LA-UR-17-24020]
[Journal ID: ISSN 0022-0434]
Grant/Contract Number:  
[AC52-06NA25396]
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Dynamic Systems, Measurement, and Control
Additional Journal Information:
[ Journal Volume: 141; Journal Issue: 1]; Journal ID: ISSN 0022-0434
Publisher:
ASME
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 42 ENGINEERING; Motion Planning, Robotics

Citation Formats

Boardman, Beth, Harden, Troy, and Martínez, Sonia. Improved Performance of Asymptotically Optimal Rapidly Exploring Random Trees. United States: N. p., 2018. Web. doi:10.1115/1.4040970.
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Boardman, Beth, Harden, Troy, & Martínez, Sonia. Improved Performance of Asymptotically Optimal Rapidly Exploring Random Trees. United States. doi:10.1115/1.4040970.
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Boardman, Beth, Harden, Troy, and Martínez, Sonia. Mon . "Improved Performance of Asymptotically Optimal Rapidly Exploring Random Trees". United States. doi:10.1115/1.4040970. https://www.osti.gov/servlets/purl/1467259.
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@article{osti_1467259,
title = {Improved Performance of Asymptotically Optimal Rapidly Exploring Random Trees},
author = {Boardman, Beth and Harden, Troy and Martínez, Sonia},
abstractNote = {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.},
doi = {10.1115/1.4040970},
journal = {Journal of Dynamic Systems, Measurement, and Control},
number = [1],
volume = [141],
place = {United States},
year = {2018},
month = {8}
}
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DOI:  10.1115/1.4040970
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Works referenced in this record:

Probabilistic roadmaps for path planning in high-dimensional configuration spaces
journal, January 1996

  • Kavraki, L. E.; Svestka, P.; Latombe, J. -C.
  • IEEE Transactions on Robotics and Automation, Vol. 12, Issue 4
  • DOI: 10.1109/70.508439

Motion planning using adaptive random walks
journal, February 2005

Sampling-based algorithms for optimal motion planning
journal, June 2011

  • Karaman, Sertac; Frazzoli, Emilio
  • The International Journal of Robotics Research, Vol. 30, Issue 7
  • DOI: 10.1177/0278364911406761

An elementary approach to generic properties of plane curves
journal, March 1984

Anytime Motion Planning using the RRT*
conference, May 2011

  • Karaman, Sertac; Walter, Matthew R.; Perez, Alejandro
  • 2011 IEEE International Conference on Robotics and Automation (ICRA)
  • DOI: 10.1109/ICRA.2011.5980479

Use of relaxation methods in sampling-based algorithms for optimal motion planning
conference, May 2013

  • Arslan, Oktay; Tsiotras, Panagiotis
  • 2013 IEEE International Conference on Robotics and Automation (ICRA)
  • DOI: 10.1109/ICRA.2013.6630906

Sampling heuristics for optimal motion planning in high dimensions
conference, September 2011

  • Akgun, Baris; Stilman, Mike
  • 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011)
  • DOI: 10.1109/IROS.2011.6095077

RRT∗-Smart: Rapid convergence implementation of RRT∗ towards optimal solution
conference, August 2012

  • Islam, Fahad; Nasir, Jauwairia; Malik, Usman
  • 2012 IEEE International Conference on Mechatronics and Automation (ICMA)
  • DOI: 10.1109/ICMA.2012.6284384

Replanning with RRTs
conference, January 2006

  • Ferguson, D.; Kalra, N.; Stentz, A.
  • Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006.
  • DOI: 10.1109/ROBOT.2006.1641879

Multipartite RRTs for Rapid Replanning in Dynamic Environments
conference, April 2007

  • Zucker, Matt; Kuffner, James; Branicky, Michael
  • Proceedings 2007 IEEE International Conference on Robotics and Automation
  • DOI: 10.1109/ROBOT.2007.363553

Real-time randomized path planning for robot navigation
conference, January 2002

  • Bruce, J.; Veloso, M.
  • IROS 2002: IEEE/RSJ International Conference on Intelligent Robots and Systems
  • DOI: 10.1109/IRDS.2002.1041624

Lazy Reconfiguration Forest (LRF) - An Approach for Motion Planning with Multiple Tasks in Dynamic Environments
conference, April 2007

  • Gayle, Russell; Klingler, Kristopher R.; Xavier, Patrick G.
  • Proceedings 2007 IEEE International Conference on Robotics and Automation
  • DOI: 10.1109/ROBOT.2007.363167

RRT-connect: An efficient approach to single-query path planning
conference, January 2000

  • Kuffner, J. J.; LaValle, S. M.
  • 2000 ICRA. IEEE International Conference on Robotics and Automation, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065)
  • DOI: 10.1109/ROBOT.2000.844730

Optimal kinodynamic motion planning in environments with unexpected obstacles
conference, September 2014

  • Boardman, Beth; Harden, Troy; Martinez, Sonia
  • 2014 52nd Annual Allerton Conference on Communication, Control, and Computing (Allerton)
  • DOI: 10.1109/ALLERTON.2014.7028567

Optimal kinodynamic motion planning using incremental sampling-based methods
conference, December 2010

  • Karaman, Sertac; Frazzoli, Emilio
  • 2010 49th IEEE Conference on Decision and Control (CDC)
  • DOI: 10.1109/CDC.2010.5717430
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