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 thecontrolledtarget.Themotionisplannedusingthisgraph. However,itisdifficulttoobtainanappropriatemathematical model in advance when the behavior of the robot is affected by unanticipated factors. Therefore, it is crucial to be able to buildamathematicalmodelfromthemotiondatagatheredby monitoringtherobotinoperation.However,whenthesedata 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.

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• A confidence-based roadmap using Gaussian process regression