Sampling Heuristics for Optimal Motion Planning in High Dimensions
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Author(s)
Akgun, Baris
Stilman, Mike
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Abstract
We present a sampling-based motion planner that
improves the performance of the probabilistically optimal
RRT* planning algorithm. Experiments demonstrate that our
planner finds a fast initial path and decreases the cost of this
path iteratively. We identify and address the limitations of
RRT* in high-dimensional configuration spaces. We introduce a
sampling bias to facilitate and accelerate cost decrease in these
spaces and a simple node-rejection criteria to increase efficiency.
Finally, we incorporate an existing bi-directional approach to
search which decreases the time to find an initial path. We
analyze our planner on a simple 2D navigation problem in
detail to show its properties and test it on a difficult 7D
manipulation problem to show its effectiveness. Our results
consistently demonstrate improved performance over RRT*.
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Date
2011-09
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