Haptic teleoperation of a multirotor aerial robot using path planning with human intention estimation

Classical haptic teleoperation systems heavily rely on operators’ intelligence and efforts in aerial robot navigation tasks, thereby posing significantly users’ workloads. In this paper, a novel shared control scheme is presented facilitating a multirotor aerial robot haptic teleoperation system that exhibits autonomous navigation capability. A hidden Markov model filter is proposed to identify the intention state of operator based on human inputs from haptic master device, which is subsequently adopted to derive goal position for a heuristic sampling based local path planner. The human inputs are considered as commanded velocity for a trajectory servo controller to drive the robot along the planned path. In addition, vehicle velocity is perceived by the user via haptic feedback on master device to enhance situation awareness and navigation safety of the user. An experimental study was conducted in a simulated and a physical environment, and the results verify the effectiveness of the novel scheme in safe navigation of aerial robots. A user study was carried out between a classical haptic teleoperation system and the proposed approach in the identical simulated complex environment. The flight data and task load index (TLX) are acquired and analyzed. Compared with the conventional haptic teleoperation scheme, the proposed scheme exhibits superior performance in safe and fast navigation of the multirotor vehicle, and is also of low task and cognitive loads.