Robot Teleoperation with Augmented Reality Virtual Surrogates

• Published in: 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI) pp. 202 - 210
• Main Authors: Walker, Michael E., Hedayati, Hooman, Szafir, Daniel
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.03.2019
• Subjects: aerial robots; ARHMD; Augmented reality; drones; interface design; mixed reality; Planning; Robot kinematics; Robots; Task analysis; teleoperation; Two dimensional displays; Unmanned aerial vehicles
• ISSN: 2167-2148
• DOI: 10.1109/HRI.2019.8673306

Teleoperation remains a dominant control paradigm for human interaction with robotic systems. However, teleoperation can be quite challenging, especially for novice users. Even experienced users may face difficulties or inefficiencies when operating a robot with unfamiliar and/or complex dynamics, such as industrial manipulators or aerial robots, as teleoperation forces users to focus on low-level aspects of robot control, rather than higher level goals regarding task completion, data analysis, and problem solving. We explore how advances in augmented reality (AR) may enable the design of novel teleoperation interfaces that increase operation effectiveness, support the user in conducting concurrent work, and decrease stress. Our key insight is that AR may be used in conjunction with prior work on predictive graphical interfaces such that a teleoperator controls a virtual robot surrogate, rather than directly operating the robot itself, providing the user with foresight regarding where the physical robot will end up and how it will get there. We present the design of two AR interfaces using such a surrogate: one focused on real-time control and one inspired by waypoint delegation. We compare these designs against a baseline teleoperation system in a laboratory experiment in which novice and expert users piloted an aerial robot to inspect an environment and analyze data. Our results revealed that the augmented reality prototypes provided several objective and subjective improvements, demonstrating the promise of leveraging AR to improve human-robot interactions.