Towards time-optimal exploration and control by an autonomous robot

In this paper, we address the problem of an autonomous robotic vehicle collecting a finite but unknown number of objects with non-negligible masses and unknown locations in a restricted area and moving them to a particular spot in minimum time. An adaptive certainty-equivalent navigation and control policy is introduced based on a pick-up and an exploration/drop-off mode. While the input signal in pickup mode is easily obtained in real time, complete exploration and drop-off corresponds to a hybrid optimal control problem (OCP) with exponential complexity in the finitely discretized space. We propose a trajectory planning algorithm by restricting the motion of the robot to a finite weighted graph. Further, we describe a discrete-time approximation of the hybrid OCP and compare both approaches with respect to computational complexity and accuracy.