Comparative analysis of model-based predictive shared control for delayed operation in object reaching and recognition tasks with tactile sensing

Communication delay represents a fundamental challenge in telerobotics: on one hand, it compromises the stability of teleoperated robots, on the other hand, it decreases the user's awareness of the designated task. In scientific literature, such a problem has been addressed both with statistical models and neural networks (NN) to perform sensor prediction, while keeping the user in full control of the robot's motion. We propose shared control as a tool to compensate and mitigate the effect of communication delay. Shared control has been proven to enhance precision and speed in reaching and manipulation tasks, especially in the medical and surgical fields. We analyze the effects of added delay and propose a teleoperated leader-follower architecture that both implements a predictive system and shared control, in a 1-dimensional reaching and recognition task with haptic feedback. We propose 4 different control modalities of increasing autonomy: non-predictive human control (hc), predictive human control (phc), shared predictive human-robot control (phrc), and predictive robot control (prc). When analyzing how the added delay affects the subjects' performance, the results show that the hc is very sensitive to the delay: users are not able to stop at the desired position and trajectories exhibit wide oscillations. The degree of autonomy introduced is shown to be effective in decreasing the total time requested to accomplish the task. Furthermore, we provide a deep analysis of environmental interaction forces and performed trajectories. Overall, the shared control modality, phrc, represents a good trade-off, having peak performance in accuracy and task time, a good reaching speed and a moderate contact with the object of interest.