The development of more advanced robotic technology gives a rise in possible applications. A field with great potential is the area of shared control. Here the human operator and the robot are both involved in solving the task at hand. This can happen at various degrees of autonomy, from where the robot has almost no decision power to fully autonomous solutions.
Often in shared control tasks, the responsibilities are split according to the individual abilities. The need to share control can also arise, if neither of the involved parts have access to all the information required to solve the task.
One such task is Urban-Search-and-Rescue with a mobile robot, where the operator only has access to a high level map of the building, while the robot can sense obstacles locally.
These obstacles pose a threat to the robot and need to be avoided. In this experiment we investigate whether using different degrees of autonomy during obstacle avoidance has an effect on task performance.
Participants control a TurtleBot3 in a Gazebo simulation of a house. Obstacles are invisible to the operator and need to be avoided to success- fully complete the task. We design and test three different conditions with increasing degree of robot autonomy, from teleoperation, over shared control to full autonomy.
The change in autonomy is through robot initiative, but operators have the authority to re- claim control. In the (semi-)autonomous conditions, we use a potential field method for reliable and efficient obstacle avoidance.
Audio feedback about changes in autonomy are provided to the participants. We measure how fast the participants complete the task and find no significant differences between the conditions.
For subjective measurements we use the QUEAD and NASA-TLX questionnaires and find only one significant partial result, favoring teleoperation. However, we observe a general trend, both in objective as well as subjective measurements, that favor the use of the teleoperation condition.
We conclude that participants might feel more comfortable having full control over the robot, but that further research is needed to investigate additional scenarios, autonomy modes, and ways to transfer control.