Drones that Think on their Feet: Sudden Landing Decisions with Embodied AI
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866915805098147840 |
|---|---|
| author | Barbosa, Diego Ortiz Agrawal, Mohit Malegaonkar, Yash Burbano, Luis Andersson, Axel Dán, György Sandberg, Henrik Cardenas, Alvaro A. |
| author_facet | Barbosa, Diego Ortiz Agrawal, Mohit Malegaonkar, Yash Burbano, Luis Andersson, Axel Dán, György Sandberg, Henrik Cardenas, Alvaro A. |
| contents | Autonomous drones must often respond to sudden events, such as alarms, faults, or unexpected changes in their environment, that require immediate and adaptive decision-making. Traditional approaches rely on safety engineers hand-coding large sets of recovery rules, but this strategy cannot anticipate the vast range of real-world contingencies and quickly becomes incomplete. Recent advances in embodied AI, powered by large visual language models, provide commonsense reasoning to assess context and generate appropriate actions in real time. We demonstrate this capability in a simulated urban benchmark in the Unreal Engine, where drones dynamically interpret their surroundings and decide on sudden maneuvers for safe landings. Our results show that embodied AI makes possible a new class of adaptive recovery and decision-making pipelines that were previously infeasible to design by hand, advancing resilience and safety in autonomous aerial systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_00167 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Drones that Think on their Feet: Sudden Landing Decisions with Embodied AI Barbosa, Diego Ortiz Agrawal, Mohit Malegaonkar, Yash Burbano, Luis Andersson, Axel Dán, György Sandberg, Henrik Cardenas, Alvaro A. Artificial Intelligence Cryptography and Security Robotics Autonomous drones must often respond to sudden events, such as alarms, faults, or unexpected changes in their environment, that require immediate and adaptive decision-making. Traditional approaches rely on safety engineers hand-coding large sets of recovery rules, but this strategy cannot anticipate the vast range of real-world contingencies and quickly becomes incomplete. Recent advances in embodied AI, powered by large visual language models, provide commonsense reasoning to assess context and generate appropriate actions in real time. We demonstrate this capability in a simulated urban benchmark in the Unreal Engine, where drones dynamically interpret their surroundings and decide on sudden maneuvers for safe landings. Our results show that embodied AI makes possible a new class of adaptive recovery and decision-making pipelines that were previously infeasible to design by hand, advancing resilience and safety in autonomous aerial systems. |
| title | Drones that Think on their Feet: Sudden Landing Decisions with Embodied AI |
| topic | Artificial Intelligence Cryptography and Security Robotics |
| url | https://arxiv.org/abs/2510.00167 |