Teleoperation of unmanned ground vehicles (UGVs), particularly for inspection of unstructured and unfamiliar environments still raises important challenges from the point of view of the operator interface. One of these challenges is caused by the fact that all information available to the operator is presented to the operator through a computer interface, providing only a partial view of the robot situation. The majority of existing interfaces provides information using visual, and, less frequently, sound channels. The lack of situation awareness (SA), caused by this partial view, may lead to an incorrect and inefficient response to the current UGV state, usually confusing and frustrating the human operator. For instance, the UGV may become stuck in debris while the operator struggles to move the robot, not understanding the cause of the UGV lack of motion. We address this problem by studying the use of haptic feedback to improve operator SA. More precisely, improving SA with respect to the traction state of the UGV, using a haptic tablet for both commanding the robot and conveying traction state to the user by haptic feedback. We report 1) a teleoperating interface, integrating a haptic tablet with an existing UGV teleoperation interface and 2) the experimental results of a user study designed to evaluate the advantage of this interface in the teleoperation of a UGV, in a search and rescue scenario. Statistically significant results were found supporting the hypothesis that using the haptic tablet elicits a reduction in the time that the UGV spends in states without traction.

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Adding human cognitive skills to planetary exploration through remote teleoperation can lead to more valuable scientific data acquisition. Still, even small amounts of latency can significantly affect real-time operations, often leading to compromised robot safety, goal overshoot, and high levels of cognitive workload. Thus, novel operational strategies are necessary to cope with these effects. This paper proposes three augmented teleoperation interfaces that allow the user to operate a robot subject to 3 seconds of latency:(1) Avatar-Aided Interface (AAI), a semi-autonomous approach based on a virtual element; (2) Predictive Interface (PI), an approach with direct control and predictive elements; and (3) Hybrid Interface (HI), where operators can easily switch between PI and AAI. We conducted a systematic within-subject experiment to evaluate the proposed interfaces in a realistic virtual environment with frequent traction losses. The user study compared AAI and PI to a Control Interface (CI), which did not display any augmented elements. The main results of this comparison showed that: (1) AAI led to a significant reduction in workload and a significant increase in usability and robot safety; (2) the use of the PI caused a significant increase in path length, indicating that operators overshoot their goals more often with this approach; (3) PI and AAI had lower reported effort; and (4) AAI is more flexible and effortless than PI and CI. Finally, during traction loss periods, PI and AAI had shortcomings that led to confusion from the operator, showing the need to integrate uncertainty measures in future interface design.