Publikationsdatenbank
Publication
6 DOF path planning in dynamic environments - A parallel on-line approachDominik Henrich , Heinz Wörn , Christian Wurll
Abstract (english)
This paper presents a new approach to parallel path planning for industrial robot arms with six degrees of freedom in an on-line given 3D environment. The method is based a best-first search algorithm and needs no essential off-line computations. The algorithm works in an implicitly discrete configuration space. Collisions are detected in the Cartesian workspace by hierarchical distance computation based on polyhedral models of the robot and the obstacles. By decomposing the 6D configuration space into hypercubes and cyclically mapping them onto multiple processing units, a good load distribution can be achieved. We have implemented the parallel path planner on a workstation cluster with 9 PCs and tested the planner for several benchmark environments. With optimal discretisation, the new approach usually shows very good speedups. In on-line provided environments with static obstacles, the parallel planning times are only a few seconds.
Publication data
Year: | 1998 |
Publication date: | 16. May 1998 |
Source: | Proceedings of IEEE International Conference on Robotics and Automation (ICRA |
Project: | SKALP , PARO |
Referrer: | https://www.ai3.uni-bayreuth.de/de/publikationen/resypub/index.php?mode=pub_show&pub_ref=henrich1998a |
BibTeX
@MISC{henrich1998a, TITLE = "6 DOF path planning in dynamic environments - A parallel on-line approach", AUTHOR = "Henrich, Dominik and Wörn, Heinz and Wurll, Christian", YEAR = "1998", JOURNAL = "Proceedings of IEEE International Conference on Robotics and Automation (ICRA", HOWPUBLISHED = "\url{https://www.ai3.uni-bayreuth.de/de/publikationen/resypub/index.php?mode=pub_show&pub_ref=henrich1998a}", }
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henrich1998a.6.DOF.path.planning.in.dynamic.environments.A.p arallel.online.approach.djvu |
146.5K | english | DJVU | download preprint | |||
henrich1998a.6.DOF.path.planning.in.dynamic.environments.A.p arallel.online.approach.pdf |
122K | english | download preprint |