RRT-Connect: An Efficient Approach to Single-Query Path Planning

ICRA, pp.995-1001, (2000)

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摘要

A simple and efficient randomized algorithm is pre- sented for solving single-query path planning problems in high-dimensional configuration spaces. The method works by incrementally building two Rapidly-exploring Random Trees (RRTs) rooted at the start and the goal configurations. The trees each explore space around them and also advance...更多

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简介
  • Motion planning problems arise in such diverse fields as robotics, assembly analysis, virtual prototyping, pharmaceutical drug design, manufacturing, and computer animation.
  • Such problems involve searching the system configuration space of one or more complicated geometric bodies for a collision-free path that connects a given start and goal configuration, while satisfying constraints imposed by complicated obstacles.
  • The key is to develop randomized methods that converge quickly in
重点内容
  • Motion planning problems arise in such diverse fields as robotics, assembly analysis, virtual prototyping, pharmaceutical drug design, manufacturing, and computer animation
  • We present a simple path planning method called Rapidly-exploring Random Trees-Connect that combines Rapidly-exploring Random Trees (RRTs) [18] with a simple greedy heuristic that aggressively tries to connect two trees, one from the initial configuration and the other from the goal
  • A randomized approach to single-query path planning is proposed that yields good experimental performance over a wide variety of examples
  • The technique is based on Rapidly-exploring Random Trees (RRTs) and the Connect heuristic
  • Some of the key practical advantages of the planning method include: 1) it does not require parameter tuning; 2) preprocessing is not required, yet interactive performance can be obtained for many difficult problems; 3) simple and consistent behavior was observed through repeated experiments; 4) a reasonable balance has been struck between greedy searching and uniform exploration; 5) the method is well-suited for incremental distance computation algorithms and fast nearest-neighbor algorithms
  • Pathological cases exist for Rapidly-exploring Random Trees-Connect, and more experimental work is needed to determine conditions under which Rapidly-exploring Random Trees-Connect will yield very poor performance
方法
  • The authors present some preliminary experiments performed on a 270 MHz SGI O2 (R12000) workstation.
  • Path smoothing was performed on the final paths to reduce jaggedness
  • Some of these results are shown in Figure 7, in which the left column shows the RRTs, and the right column shows the corresponding solutions.
  • The Connect heuristic only slightly increases running time in comparison to using the EXTEND function to construct two trees.
  • It seems that the greedy behavior is worthwhile on average.
  • The authors are currently comparing some of the variants discussed in Section 3
结果
  • The RRT-Connect has proven to be very successful in the experiments, the authors are aware of several intertwined factors that could improve performance even further.
结论
  • A randomized approach to single-query path planning is proposed that yields good experimental performance over a wide variety of examples.
  • Some of the key practical advantages of the planning method include: 1) it does not require parameter tuning; 2) preprocessing is not required, yet interactive performance can be obtained for many difficult problems; 3) simple and consistent behavior was observed through repeated experiments; 4) a reasonable balance has been struck between greedy searching and uniform exploration; 5) the method is well-suited for incremental distance computation algorithms and fast nearest-neighbor algorithms.
  • The practical performance observed so far is encouraging; an extensive study that involves many benchmarking examples would be useful, and is currently under investigation.
  • Theoretical analysis of the convergence rate remains, which is one topic under current investigation
基金
  • Kuffner has been supported in part by a National Science Foundation Graduate Fellowship in Engineering, and MURI grant DAAH04-96-1-007
  • LaValle has been supported in part by NSF CAREER Award IRI9875304 (LaValle)
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