Incorporating Trust and Self-Confidence Analysis in the Guidance and Control of (Semi)Autonomous Mobile Robotic Systems

We propose a trust and self-confidence-based autonomy allocation strategy to automatically choose between manual and autonomous control of (semi)autonomous mobile robots in guidance and navigation tasks. We utilize a performance-centric, computational trust and self-confidence model and automated autonomy allocation strategy, developed in our earlier work (H. Saeidi and Y. Wang, Trust and self-confidence based autonomy allocation for robotic systems, in Proc. 54th IEEE Conf. Decis. Control, 2015, pp. 6052-6057.), based on objective and unbiased performance measures for the human and the robot. A set of robot simulations with a human-in-the-loop is conducted for a teleoperated unmanned aerial vehicle tracking task. The results demonstrate that our allocation strategy can capture human autonomy allocation pattern with an accuracy of 64.05%. We also show that the strategy can improve the overall robot performance by 11.76% and reduce operator's workload by 10.07% compared to a manual allocation. Moreover, compared to a performance maximization strategy, our strategy is 23.42% more likely to he accepted and generally preferred and trusted by the participants. Furthermore, we design a decision pattern correction algorithm based on nonlinear model predictive control to help a human operator gradually adapt to a modified allocation pattern for improved overall performance.