  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.
