A New Semi-automatic Strategy for Teleoperating Mobile Manipulators Using a Haptic Device

When teleoperating wheeled mobile manipulator robots using haptic devices, users usually use rate control for navigation and position control for manipulation. Manually switching between those two modes (for example, via a push button) while relying on visual feedback is often challenging, as using a single camera significantly reduces depth perception, making contact-based tasks difficult due to reduced spatial awareness and depth cues. Adding proximity sensors or auditory cues for distance detection requires processing more data, raising both the cognitive load and associated costs. On the other hand, typical automatic hybrid switching schemes employ restoring forces in navigation mode to assist the human operator. However, performing contact-based tasks in navigation mode deteriorates transparency due to interference between environmental and restoring forces while impairing fine-manipulation capabilities. Therefore, to overcome these challenges, we propose a new hybrid scheme that detects interaction with the environment via a force sensor to facilitate the automatic transition from navigation to manipulation mode. We use constrained task-space controllers to address obstacle avoidance directly in the control law and enable safe interaction. We conducted an ethically approved study involving nine humans to assess the proposed scheme and used task completion time (TCT) and NASA Task Load Index (TLX) to assess human operator workload. Paired t-tests indicate a reduction of 23.3% (p = 0.0058) in TCT and mental demand by 57.1% (p = 0.0021), highlighting the advantages of the proposed semiautomatic switching scheme over the manual one.