A localization aware sampling strategy for motion planning under uncertainty

We present a localization aware efficient sampling strategy for sampling-based motion planning under uncertainty that uses a new notion of localization ability of a sample. It puts more samples in regions where sensor data is able to achieve higher uncertainty reduction while maintaining adequate samples in regions where uncertainty reduction is poor. This leads to a less dense roadmap and hence results in significant time savings in the path search phase. We provide simulation results that show stochastic planners with our sampling strategy place less samples and find a well-localized path in shorter time with little compromise on the quality of path as compared to existing sampling techniques. We also show that a stochastic planner that uses our sampling strategy is probabilistically complete under some reasonable conditions on parameters.