Fast and Bounded Probabilistic Collision Detection in Dynamic Environments for High-DOF Trajectory Planning
This work addresses safety-critical motion planning for robots operating near humans, representing an incremental improvement by integrating probabilistic detection with trajectory optimization.
The paper tackles the problem of probabilistic collision detection for high-DOF robots in dynamic, uncertain environments, achieving efficient computation of accurate collision probabilities with upper error bounds and demonstrating motion planning performance in scenarios with robot arms and moving human obstacles.
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.