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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 be 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.

The goal of this paper is to improve the performance of joint human–robot systems while balancing human experience through computational trust analysis in telerobotics applications. A mixed-initiative approach is enabled by scaling the manual and autonomous controls using a function of computational human-to-robot trust. A dynamic haptic force feedback scaling strategy is performed via a function of computational robot-to-human trust to adjust the assistive force and, hence, physical workload of the operator. Passivity-based methods are used to guarantee the stability of the overall framework. Moreover, guidelines are provided to adjust the transparency of force feedback and velocity signals while guaranteeing passivity. An experimental study with 30 human subjects demonstrates a 12.8% increase in task performance and a 10.7% decrease in operator workload when the proposed strategy is used as compared to a manual autonomy allocation approach. The results also indicate that the proposed method yields higher operator satisfaction compared to manual and optimal autonomy allocation methods and is 10.1% more trusted by the operators than the optimal allocation method.