A Selective Retraction-Based RRT Planner for Various Environments

摘要

We present a novel randomized path planner for rigid robots to efficiently handle various environments that have different characteristics. We first present a bridge line test that can identify narrow passage regions and then selectively performs an optimization-based retraction only at those regions. We also propose a noncolliding line test, which is a dual operator to the bridge line test, as a culling method to avoid generating samples near wide-open free spaces. These two line tests are performed with a small computational overhead. We have tested our method with different benchmarks that have varying amounts of narrow passages. Our method achieves up to several times improvements over prior RRT-based planners and consistently shows the best performance across all the tested benchmarks.