Faster, More Effective Connection for Probabilistic Roadmaps
Lucia K. Dale, Guang Song, Nancy M. Amato · 2000
In this paper, we report on our experience attempting to improve the running times of probabilistic roadmap motion planning methods (prms). We show that significant speedups can be obtained with relatively little effort on the part of the developer by employing new connection strategies and more intelligent ways of invoking and utilizing existing off-the-shelf collision detection packages. We outline general techniques for determining when and which of the techniques we have developed are most useful. We also categorize each as being helpful in either a problem specific or problem independent way. Many techniques presented are of general usefulness. This research supported in part by NSF CAREER Award CCR-9624315 (with REU Supplement), NSF Grants IIS-9619850 (with REU Supplement), EIA-9805823, and EIA-9810937, by the Texas Higher Education Coordinating Board under grant ARP-036327-017, and by the NCSA at the University at Illinois at Urbana-Champaign. Dale is supported in part by a Dep...