Confidence-based roadmap using Gaussian process regression for a robot control

To achieve a realistic task by a recent complicated robot, a practical motion planning method is important. Especially in this decade, sampling-based motion planning methods have become popular thanks to recent high performance computers. In sampling-based motion planning, a graph that covers the state space is constructed based on reachability between node pairs, and the motion is planned using the graph. However, it requires an explicit model of a controlled target. In this research, we propose a motion planning method in which a system model is estimated by using Gaussian process regression. We apply our method to the control of an actual robot. Experimental results show that the control of the robot can be achieved by the proposed motion planning method.