We compare four human–robot interaction configurations for semi-autonomous Unmanned Ground Vehicles (UGV) support in a simulated search-and-rescue task, organised along two orthogonal dimensions: Immersion (screen vs head-mounted display) and Viewpoint (exocentric/top-down vs egocentric/first-person). Thirty-six professional first responders and CBRN specialists completed the task using a 2D screen, a 3D screen, VR, or XR. 2D yielded the highest perceived explainability and self-rated performance; XR showed the lowest mean workload and a trend toward lower perceived difficulty (non-significant); VR increased negative effects (e.g., discomfort). Transparency significantly predicted trust, but with a counterintuitive negative coefficient, suggesting that uncalibrated disclosures can undermine perceived reliability. We report effect sizes and methodological details, discuss design implications for calibrating immersion and viewpoint to balance situational awareness, comfort, and trust in safety-critical operations.

In the field of medical first responder training, the choice of training modality is crucial for skill retention and real-world application. This study introduces the Green Manikin, an advanced Mixed Reality (MR) tool, conceptually combining the immersiveness of Virtual Reality (VR) with the tangibility of real-world training, and compares it against traditional real-world simulations and VR training. Our findings indicate that MR and real-world settings excel in Self and Social Presence, and in intention to use, offering heightened psychological presence suitable for complex training scenarios. Effort expectancy was highest in real-world environments, suggesting their ease of use for basic skill acquisition. This nuanced understanding allows for better tailoring of training modalities to specific educational objectives. Our research validates the utility of MR and offers a framework for selecting the most effective training environment for different learning outcomes in medical first responder training.

This video abstract discusses the details of the implementation of a Packbot operator control unit (OCU) using Apple's official iPhone SDK.

This paper presents the results from experiments validating the physics and environmental accuracy of a new robot simulation environment, the search and rescue game environment (SARGE), which is the foundation for series of robot-operator training games. An ATRV-Jr. outfitted with a SICK laser, GPS, and compass was used both in the real-world and in a simulated environment modeled after the real-world testing location in a simulated anomaly detection task. The ARTV-Jr., controlled by the Distributed Field Robotics Architecture, navigated through a series of waypoints in the environment. The simulated ATRV-Jr. matched the actions of the real ATRV-Jr. in both velocity and path similarity within 0.08 m/s and 0.7 m respectively.