A confidence-based roadmap using Gaussian process regression

Recent advances in high performance computing have allowed sampling-based motion planning methods to be successfully applied to practical robot control problems. In such methods, a graph representing the local connectivity among states is constructed using a mathematical model of the controlled target. The motion is planned using this graph. However, it is difficult to obtain an appropriate mathematical model in advance when the behavior of the robot is affected by unanticipated factors. Therefore, it is crucial to be able to build a mathematical model from the motion data gathered by monitoring the robot in operation. However, when these data are sparse, uncertainty may be introduced into the model. To deal with this uncertainty, we propose a motion planning method using Gaussian process regression as a mathematical model. Experimental results show that satisfactory robot motion can be achieved using limited data.