Exploring camera viewpoint control models for a multi-tasking setting in teleoperation

Control of camera viewpoint plays a vital role in many teleoperation activities, as watching live video streams is still the fundamental way for operators to obtain situational awareness from remote environments. Motivated by a real-world industrial setting in mining teleoperation, we explore several possible solutions to resolve a common multi-tasking situation where an operator is required to control a robot and simultaneously perform remote camera operation. Conventional control interfaces are predominantly used in such teleoperation settings, but could overload an operator's hand-operation capability, and require frequent attention switches and thus could decrease productivity. We report on an empirical user study in a model multi-tasking teleoperation setting where the user has a main task which requires their attention. We compare three different camera viewpoint control models: (1) dual manual control, (2) natural interaction (combining eye gaze and head motion) and (3) autonomous tracking. The results indicate the advantages of using the natural interaction model, while the manual control model performed the worst.