We present an RRT-based motion planning algorithm that uses the maximal Poisson-disk sampling scheme. Our approach exploits the free-disk property of the maximal Poisson-disk samples to generate nodes and perform tree expansion. Furthermore, we use an adaptive scheme to generate more samples in challenging regions of the configuration space. Our approach can be easily parallelized on multi-core CPUs and many-core GPUs. We highlight the performance of our algorithm on different benchmarks.


Parallel Motion Planning using Poisson-Disk Sampling
Chonhyon Park, Jia Pan, and Dinesh Manocha
IEEE Transactions on Robotics (T-RO), in press [PDF]

Chonhyon Park, Jia Pan, and Dinesh Manocha
IEEE International Conference on Robotics and Automation (ICRA), 2014 [PDF]

Related work

GAMMA Research Group
Motion Planning Research at GAMMA