Human-Robot Mutual Trust in (Semi)autonomous Underwater Robots

Abstract

It is envisioned that a human operator is able to monitor and control one or more (semi)autonomous underwater robots simultaneously in future marine operations. To enable such operations, a human operator must trust the capability of a robot to perform tasks autonomously, and the robot must establish its trust to the human operator based on human performance and follow guidance accordingly. Therefore, we seek to i model the mutual trust between humans and robots (especially (semi)autonomous underwater robots in this chapter), and ii) develop a set of trust-based algorithms to control the human-robot team so that the mutual trust level can be maintained at a desired level. We propose a time series based mutual trust model that takes into account robot performance, human performance and overall human-robot system fault rates. The robot performance model captures the performance evolution of a robot under autonomous mode and teleoperated mode, respectively. Furthermore, we specialize the robot performance model of a YSI EcoMapper autonomous underwater robot based on its distance to a desired waypoint. The human performance model is inspired by the Yerkes-Dodson law in psychology, which describes the relationship between human arousal and performance. Based on the mutual trust model, we first study a simple case of one human operator controlling a single robot and propose a trust-triggered control strategy depending on the limit conditions of the desired trust region. The method is then enhanced for the case of one human operator controlling a swarm of robots. In this framework, a periodic trust-based control strategy with a highest-trust-first scheduling algorithm is proposed. Matlab simulation results are provided to validate the proposed model and control strategies that guarantee effective real-time scheduling of teleoperated and autonomous controls in both one human one underwater robot case and one human multiple underwater robots case.