Random number generation and quasi-Monte Carlo methods
Random number generation and quasi-Monte Carlo methods
Robot motion planning: a distributed representation approach
International Journal of Robotics Research
A random sampling scheme for path planning
International Journal of Robotics Research
Randomized single-query motion planning in expansive spaces
Randomized single-query motion planning in expansive spaces
On the Probabilistic Foundations of Probabilistic Roadmap Planning
International Journal of Robotics Research
| Hi-index | 0.00 |
Probabilistic Roadmap approaches (PRMs) have been successfully applied in motion planning of robots with many degrees of freedom. In recent years, the community has proposed deterministic sampling as a way to improve the performance in these planners. However, our recent results show that the choice of the sampling source - pseudo-random or deterministic- has small impact on a PRM planner's performance. We used two single-query PRM planners for this comparative study. The advantage of the deterministic sampling on the pseudorandom sampling is only observable in low dimension problems. The results were surprising in the sense that deterministic sampling performed differently than claimed by the designers.