A Learning-based Semi-autonomous Control Architecture for Robotic Exploration in Search and Rescue Environments

Abstract

Semi-autonomous control schemes can address the limitations of both teleoperation and fully autonomous robotic control of rescue robots in disaster environments by allowing cooperation and task sharing between a human operator and a robot with respect to tasks such as navigation, exploration and victim identification. Herein, a unique hierarchical reinforcement learning (HRL) -based semi-autonomous control architecture is presented for rescue robots operating in unknown and cluttered urban search and rescue (USAR) environments. The aim of the controller is to allow a rescue robot to continuously learn from its own experiences in an environment in order to improve its overall performance in exploration of unknown disaster scenes. A new direction-based exploration technique and a rubble pile categorization technique are integrated into the control architecture for exploration of unknown rubble filled environments. Both simulations and physical experiments in USAR-like environments verify the robustness of the proposed control architecture.