Dynamic-Domain RRTs: Efficient Exploration by Controlling the Sampling Domain
Anna Yershova, Léonard Jaillet, Thierry Siméon, Steven M. LaValle
Sampling-based planners have solved difficult
problems in many applications of motion planning in recent
years. In particular, techniques based on the Rapidly-exploring
Random Trees (RRTs) have generated highly successful singlequery
planners. Even though RRTs work well on many problems,
they have weaknesses which cause them to explore slowly
when the sampling domain is not well adapted to the problem.
In this paper we characterize these issues and propose a
general framework for minimizing their effect. We develop
and implement a simple new planner which shows significant
improvement over existing RRT-based planners. In the worst
cases, the performance appears to be only slightly worse in
comparison to the original RRT, and for many problems it
performs orders of magnitude better.
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