Problem
Research questions and friction points this paper is trying to address.
agentic AI
safety-critical missions
multi-drone systems
operator trust
autonomy integration
Agentic AI for Safety-critical Multi-drone Systems: Challenges and Opportunities
🤖 AI Summary
本文探讨了在安全关键的多无人机系统中集成代理行为的问题，通过NAMUR和PERSIST项目，采用人本、参与式和迭代的研究方法来设计接口、监督机制及评估实践。
📝 Abstract
Multi-drone systems are increasingly positioned for safety-critical missions such as search and rescue (SAR) and critical infrastructure monitoring. Yet, real-world adoption remains constrained not only by autonomy performance, but by the difficulty of integrating agentic behavior into professional work: operators must understand, trust, and govern automation under uncertainty, time pressure, and accountability. This position paper synthesizes the ambitions and lessons from two ongoing efforts: NAMUR, which explores LLM-supported robot control in SAR and firefighting contexts, and PERSIST, which explores persistent drone operations for monitoring and security at critical infrastructure sites. We argue that agentic AI should be approached as a socio-technical design problem, where interfaces, oversight mechanisms, and evaluation practices are as critical as algorithms. We outline a human-centered, participatory, and iterative research approach aimed at uncovering stakeholder needs, shaping agent capabilities through successive prototypes, and producing transferable proof-of-concept systems and evaluation strategies for other safety-critical contexts.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Agentic AI
Socio-Technical Design
Human-Centered Research
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