S-tunnel Benchmark: The leftmost figure shows the S-tunnel benchmark.
The right two figures show the average performance of two variations
of the S-tunnel benchmark. 0.85 and 1.3 indicate the scaling factor
of the cubic robot. 0.85 does not create any narrow passages, while
1.3 creates them.
We present a novel retraction-based planner, selective
retraction-based RRT, for efficiently handling a wide
variety of environments that have different characteristics.
We first present a bridge line-test that can identify regions
around narrow passages, and then perform an optimizationbased
retraction operation selectively only at those regions.
We also propose a non-colliding line-test, a dual operator to
the bridge line-test, as a culling method to avoid generating
samples near wide-open free spaces and thus to generate more
samples around narrow passages. These two tests are performed
with a small computational overhead and are integrated with
a retraction-based RRT. In order to demonstrate benefits
of our method, we have tested our method with different
benchmarks that have varying amounts of narrow passages.
Our method achieves up to 21 times and 3.5 times performance
improvements over a basic RRT and an optimization-based
retraction RRT, respectively. Furthermore, our method
consistently improves the performances of other tested methods
across all the tested benchmarks that have or do not have