The Impact of Motion Scaling and Haptic Guidance on Operators’ Workload and Performance in Teleoperation

The L-CAS team, in collaboration with intLab at the University of Lincoln, is pleased to announce the publication of our paper on “The Impact of Motion Scaling and Haptic Guidance on Operators’ Workload and Performance in Teleoperation“ at CHI’22. 

Soran Parsa, School of Computer Science, University of Lincoln, United Kingdom, sparsa@lincoln.ac.uk
Horia A. Maior, Horizon Digital Economy Research Institute, University of Nottingham, United Kingdom and School of Computer Science, University of Nottingham, United Kingdom, horiamaior@gmail.com
Alex Reeve Elliott Thumwood, School of Computer Science, University of Lincoln, United Kingdom, athumwood@lincoln.ac.uk
Max L Wilson, School of Computer Science, University of Nottingham, United Kingdom, max.wilson@nottingham.ac.uk
Marc Hanheide, Lincoln Centre for Autonomous Systems, University of Lincoln, United Kingdom, mhanheide@lincoln.ac.uk
Amir Ghalamzan Esfahani, Lincoln Institute for Agri-Food Technology, University of Lincoln, United Kingdom, aghalamzanesfahani@lincoln.ac.uk
 
The use of human operator managed robotics, especially for safety critical work, includes a shift from physically demanding to mentally challenging work, and new techniques for Human-Robot Interaction are being developed to make teleoperation easier and more accurate. This study evaluates the impact of combining two teleoperation support features (i) scaling the velocity mapping of leader-follower arms (motion scaling), and (ii) haptic-feedback guided shared control (haptic guidance). We used purposely difficult peg-in-the-hole tasks requiring high precision insertion and manipulation, and obstacle avoidance, and evaluated the impact of using individual and combined support features on a) task performance and b) operator workload. As expected, long distance tasks led to higher mental workload and lower performance than short distance tasks. Our results showed that motion scaling and haptic guidance impact workload and improve performance during more difficult tasks, and we discussed this in contrast to participants preference for using different teleoperation features.