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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Online-Zugang: | https://arxiv.org/abs/2604.13248 |
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| _version_ | 1866918447152103424 |
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| author | Kebria, Parham Sabri, Soheil Brattain, Laura J |
| author_facet | Kebria, Parham Sabri, Soheil Brattain, Laura J |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_13248 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | GeoVision-Enabled Digital Twin for Hybrid Autonomous-Teleoperated Medical Responses Kebria, Parham Sabri, Soheil Brattain, Laura J Robotics Artificial Intelligence 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. |
| title | GeoVision-Enabled Digital Twin for Hybrid Autonomous-Teleoperated Medical Responses |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2604.13248 |