Experimental Evaluation of Model Predictive Mixed-Initiative Variable Autonomy Systems Applied to Human-Robot Teams

Abstract

Adjusting the level of autonomy in human-machine systems (e.g., human-robot systems) holds great potential for achieving high system performance while maintaining operator involvement. To support operators with the task of setting the proper level of autonomy, we present a novel approach to realise a Model Predictive Controller that determines the optimal LoA for each tessellation in the robot's path plan based on the estimated performance degradation due environmental adversities. We also report on an experimental evaluation of a mixed-initiative system where both the operator and the Model Predictive Controller are in charge of dynamically adjusting the level of autonomy cooperatively while performing a challenging navigational task with a mobile ground robot in a high-fidelity simulation. To this end, we conducted a user study with 15 participants comparing the performance and user experience of the model predictive system with a state-of-the-art system. The results show significant benefits of the model predictive system in terms of a reduction of conflicts for control and an improved user experience. Additionally, there are indications of benefits in terms of robot health and, consequently, performance for the model predictive system.