Abstract
With the development of robotic technology, many tasks that used to be difficult, dangerous, or inaccessible for humans to perform can now be accomplished by specifically designed robots. However, most of the robots that are fully autonomous are not capable of completing a mission all by itself. Therefore, humans are often involved in remotely operating robots from a distance and guide them to accomplish these missions. Remote operation (or a teleoperation) generally involves multiple tasks such as visual search, robot navigation, spatial learning, and so on. However, it is still not well understood how well the human-robot team performs under different conditions, including which tasks should be assigned to the robots and which to the human operator. The present study examined the effect of active control of the robot on visual search and spatial learning in a teleoperation task. A robot was placed in a rectangular maze partitioned by walls to search for color and letter targets amongst distractors. A pair of observers watched simultaneously the online video of the scene taken by a camera mounted on the robot and pressed a key as soon as a target appeared. One of the observers was randomly assigned to command the movement of the robot (active driver), while the other was simply viewing (passive viewer). Both observers drew maps indicating the location of the targets after each exploration trial. Contrary to past research on active control, results showed an advantage for passive operators when participants had access to a map during exploration. However, when the map was removed during teleoperation, performance did not differ between the active and passive operators. These results suggest that fully automated robot navigation is beneficial only when maps of the to-be-explored space are available.
Meeting abstract presented at VSS 2012