Adaptive motion planning with high-dimensional mixture models
This paper presents a novel adaptive approach to fast sampling-based motion planning by learning models of collision and collision-free regions in configuration spaces in an online manner. The proposed approach incrementally learns Gaussian Mixture Models (GMMs) for collision detection in high dimensional configuration spaces. In practical applications for robotic manipulation, the representation of collision and collision-free regions in configuration space can change due to relative motion between the robot base and workspace. We show how to rapidly adapt to such changes by using inverse kinematics to transform the parameters of the Gaussian mixture model to new configurations. The transformed model is initially used as a prior and then continually updated and refined as the RRT planning algorithm proceeds in real-time. This approach is extremely computationally efficient, and our proposed method is compared with traditional sampling-based planning methods on a number of experimental robot arm planning scenarios.