GeoVision-Enabled Digital Twin for Hybrid Autonomous-Teleoperated Medical Responses
It addresses the need for improved situational awareness and decision-making in remote medical response systems for emergency care in challenging environments.
This paper introduces a Digital Twin architecture for hybrid autonomous-teleoperated medical response systems, leveraging GeoVision capabilities to integrate perception, adaptive navigation, and real-time synchronization for enhanced situational awareness. The framework provides remote users with an intuitive virtual representation of the platform and environment, improving decision-making in disaster-affected and infrastructure-limited settings.
Remote medical response systems are increasingly being deployed to support emergency care in disaster-affected and infrastructure-limited environments. Enabled by GeoVision capabilities, this paper presents a Digital Twin architecture for hybrid autonomous-teleoperated medical response systems. The proposed framework integrates perception and adaptive navigation with a Digital Twin, synchronized in real-time, that mirrors system states, environmental dynamics, patient conditions, and mission objectives. Unlike traditional ground control interfaces, the Digital Twin provides remote clinical and operational users with an intuitive, continuously updated virtual representation of the platform and its operational context, enabling enhanced situational awareness and informed decision-making.