16,381 research outputs found
Asymptotically near-optimal RRT for fast, high-quality, motion planning
We present Lower Bound Tree-RRT (LBT-RRT), a single-query sampling-based
algorithm that is asymptotically near-optimal. Namely, the solution extracted
from LBT-RRT converges to a solution that is within an approximation factor of
1+epsilon of the optimal solution. Our algorithm allows for a continuous
interpolation between the fast RRT algorithm and the asymptotically optimal
RRT* and RRG algorithms. When the approximation factor is 1 (i.e., no
approximation is allowed), LBT-RRT behaves like RRG. When the approximation
factor is unbounded, LBT-RRT behaves like RRT. In between, LBT-RRT is shown to
produce paths that have higher quality than RRT would produce and run faster
than RRT* would run. This is done by maintaining a tree which is a sub-graph of
the RRG roadmap and a second, auxiliary graph, which we call the lower-bound
graph. The combination of the two roadmaps, which is faster to maintain than
the roadmap maintained by RRT*, efficiently guarantees asymptotic
near-optimality. We suggest to use LBT-RRT for high-quality, anytime motion
planning. We demonstrate the performance of the algorithm for scenarios ranging
from 3 to 12 degrees of freedom and show that even for small approximation
factors, the algorithm produces high-quality solutions (comparable to RRG and
RRT*) with little running-time overhead when compared to RRT
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