Neuromuscular analysis based tuning of haptic shared control assistance for UAV collision avoidance

We describe initial steps in the design of a haptic shared control system for obstacle avoidance in unmanned aerial vehicle (UAV) teleoperation. In the considered scenario, the operator is supported by force feedback on the control interface, by which a seamless integration of his commands and the assistance from an automatic collision avoidance system is created. To find proper tuning for this haptic shared control system, analysis of the operator´s neuromuscular system is used as a guideline. Relevant properties of human NMS are discussed together with corresponding choices for tuning of the haptic shared control system. Tuning the system for the intrinsic stiffness is selected here, to ensure that during teleoperation only the minimal physical workload of the operator is required. A new way to determine the intrinsic stiffness, even when the operator needs to exert forces on the control inceptor, is described here. Proposed method uses wide bandwidth perturbation signal in the NMS identification. In this way natural reflexive feedback paths are suppressed which allows to determine the intrinsic stiffens of the operator arm. The method is discussed in details and experimental results are shown. The paper then presents results of a NMS identification experiment with 10 participants. The procedure resulted in a set of representative frequency responses functions for the NMS stiffness, on the basis of which the haptic feedback for the UAV can be tuned.