A Shared-Autonomy Approach to Goal Detection and Navigation Control of Mobile Collaborative Robots

Autonomous goal detection and navigation control of mobile robots in remote environments can help to unload
human operators from simple, monotonous tasks allowing them to focus on more cognitively stimulating actions. This can result
in better task performances, while creating user-interfaces that are understandable by non-experts. However, full autonomy
in unpredictable and dynamically changing environments is still far from becoming a reality. Thus, teleoperated systems
integrating the supervisory role and instantaneous decision making capacity of humans are still required for fast and
reliable robotic operations. This work presents a novel shared autonomy framework for goal detection and navigation control
of mobile manipulators. The controller exploits human-gaze information to estimate the desired goal. This is used together
with control-pad data to predict user intention, and to activate the autonomous control for executing a target task. Using the
control-pad device, a user can react to unexpected disturbances and halt the autonomous mode at any time. By releasing the
control-pad device (e.g., after avoiding an instantaneous obstacle) the controller smoothly switches back to the autonomous
mode and navigates the robot towards the target. Experiments for reaching a target goal in the presence of unknown obstacles
are carried out to evaluate the performance of the proposed shared-autonomy framework over seven subjects. The results
prove the accuracy, time-efficiency, and ease-of-use of the presented shared-autonomy control framework.