Computing multiple guiding paths for sampling-based motion planning
| Authors | |
|---|---|
| Year of publication | 2019 |
| Type | Article in Proceedings |
| Conference | Proceedings of the 19th International Conference on Advanced Robotics, ICAR 2019 |
| MU Faculty or unit | |
| Citation | |
| Doi | http://dx.doi.org/10.1109/ICAR46387.2019.8981589 |
| Keywords | path planning;sampling;configuration space;robotics |
| Description | Path planning of 3D solid objects leads to search in a six-dimensional configuration space, which can be solved by sampling-based motion planning. The well known issue of sampling-based planners is the narrow passage problem which is caused by the presence of small regions of the configuration space, that are difficult to cover by random samples. Guided-based planners cope with this issue by increasing probability of sampling along an estimated solution (a guiding path). In the case of six-dimensional configuration space, a guiding path needs to be computed in the configuration space rather than in the workspace. Fast computation of guiding paths can be achieved by solving similar, yet simpler problem, e.g., by reducing size of the robot. This results in an approximate solution (path) that is assumed to be located near the solution of the original problem. The guided sampling along this approximate solution may however fail if the approximate solution is too far from the desired solution. We cope with this problem by sampling the configuration space along multiple approximate solutions. We propose an iterative method to compute multiple approximate solutions in the configuration space. Exploration of the configuration space around already found paths is inhibited, which boosts the search of alternative paths. The performance of the proposed approach is verified in scenarios with multiple narrow passages and compared with several state-of-the-art planners. |
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