We present a novel approach to perform probabilistic collision detection between a high-DOF robot and high-DOF obstacles in dynamic, uncertain environments. In dynamic environments with a high-DOF robot and moving obstacles, our approach efficiently computes accurate collision probability between the robot and obstacles with upper error bounds. Furthermore, we describe a prediction algorithm for future obstacle position and motion that accounts for both spatial and temporal uncertainties. We present a trajectory optimization algorithm for high-DOF robots in dynamic, uncertain environments based on probabilistic collision detection. We highlight motion planning performance in challenging scenarios with robot arms operating in environments with dynamically moving human obstacles.
@article{arxiv.1607.04788,
title = {Fast and Bounded Probabilistic Collision Detection in Dynamic Environments for High-DOF Trajectory Planning},
author = {Chonhyon Park and Jae Sung Park and Dinesh Manocha},
journal= {arXiv preprint arXiv:1607.04788},
year = {2016}
}