Cognitive Guardrails for Open-World Decision Making in Autonomous Drone Swarms
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909631584927744 |
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| author | Cleland-Huang, Jane Granadeno, Pedro Antonio Alarcon Bernal, Arturo Miguel Russell Hernandez, Demetrius Murphy, Michael Petterson, Maureen Scheirer, Walter |
| author_facet | Cleland-Huang, Jane Granadeno, Pedro Antonio Alarcon Bernal, Arturo Miguel Russell Hernandez, Demetrius Murphy, Michael Petterson, Maureen Scheirer, Walter |
| contents | Small Uncrewed Aerial Systems (sUAS) are increasingly deployed as autonomous swarms in search-and-rescue and other disaster-response scenarios. In these settings, they use computer vision (CV) to detect objects of interest and autonomously adapt their missions. However, traditional CV systems often struggle to recognize unfamiliar objects in open-world environments or to infer their relevance for mission planning. To address this, we incorporate large language models (LLMs) to reason about detected objects and their implications. While LLMs can offer valuable insights, they are also prone to hallucinations and may produce incorrect, misleading, or unsafe recommendations. To ensure safe and sensible decision-making under uncertainty, high-level decisions must be governed by cognitive guardrails. This article presents the design, simulation, and real-world integration of these guardrails for sUAS swarms in search-and-rescue missions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_23576 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cognitive Guardrails for Open-World Decision Making in Autonomous Drone Swarms Cleland-Huang, Jane Granadeno, Pedro Antonio Alarcon Bernal, Arturo Miguel Russell Hernandez, Demetrius Murphy, Michael Petterson, Maureen Scheirer, Walter Robotics Artificial Intelligence Human-Computer Interaction Small Uncrewed Aerial Systems (sUAS) are increasingly deployed as autonomous swarms in search-and-rescue and other disaster-response scenarios. In these settings, they use computer vision (CV) to detect objects of interest and autonomously adapt their missions. However, traditional CV systems often struggle to recognize unfamiliar objects in open-world environments or to infer their relevance for mission planning. To address this, we incorporate large language models (LLMs) to reason about detected objects and their implications. While LLMs can offer valuable insights, they are also prone to hallucinations and may produce incorrect, misleading, or unsafe recommendations. To ensure safe and sensible decision-making under uncertainty, high-level decisions must be governed by cognitive guardrails. This article presents the design, simulation, and real-world integration of these guardrails for sUAS swarms in search-and-rescue missions. |
| title | Cognitive Guardrails for Open-World Decision Making in Autonomous Drone Swarms |
| topic | Robotics Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2505.23576 |