Tuning of a haptic collision avoidance system for UAV teleoperation

Abstract

This research investigates a neuromuscular analysis based tuning algorithm for haptic cues that has been hypothesized to simultaneously improve safety and workload when compared to heuristic tuning, applied to a haptic collision avoidance system for unmanned aircraft teleoperation. This novel tuning method considers the combined stiffness of the human arm and the control inceptor when computing ideal haptic cues. The `relaxed' setting of the neuromuscular system, for which neural reflexes are suppressed, is chosen as the design point for tuning haptic cues as it is expected to lead to the lowest workload, contrary to the `force' and `position' settings. Theoretical investigations using offline simulations verified the novel approach and the selection of the `relaxed' setting. Subsequently, a teleoperation experiment (n=12) in an obstacle laden urban environment was conducted with six different tuning profiles, including a manual control condition. Results showed that safety, workload and situational awareness was substantially improved over conditions that ignored the neuromuscular system. Additionally, over-tuning haptic cues was found to be worse than manual control for user acceptance of the system. No significant differences were found between the `relaxed' and `force' settings, suggesting that selection between these two options depends on the specific application of haptic cues. The admittance-trajectory relationship during teleoperation was studied, without haptic cues, to further improve the tuning method. Here, no statistical differences in admittance were observed between different obstacles. However, a significant effect was found for admittance variations within obstacles, and an inverse relationship was established between admittance and UAV velocity/yaw rate.