A Systems Engineering Framework for Vision-Language-Enabled UAV Triage and Disaster Response
This paper proposes a systems engineering framework that embeds Vision Language Models (VLMs) as coordination agents in human-UAV disaster response loops, moving beyond decision support. Developed with Model-Based Systems Engineering (MBSE), the architecture integrates natural language interaction, mission-level task coordination, software-in-the-loop implementation, and Incident Command System-aligned communications. A preliminary human-factors study with seven participants found reduced perceived workload and high ratings for AI trust and communication clarity.
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[Submitted on 30 Jul 2026]
Title:A Systems Engineering Framework for Vision-Language-Enabled UAV Triage and Disaster Response
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Abstract:Recent advances in Vision Language Models (VLMs) have created new opportunities for disaster response, where responders must interpret large volumes of sensor data under time pressure. Current VLM applications include social media monitoring for situational awareness, generation of draft action plans, and translation of technical alerts into public-facing messages. While these efforts can accelerate information flow, they remain largely limited to decision-support roles. Such approaches can increase operator burden because humans must still translate outputs into coordinated actions across teams and robotic assets. This study explores the viability of embedding VLMs as coordination agents within the human-UAV loop. The proposed architecture integrates natural language interaction, mission-level task coordination, software-in-the-loop implementation, and communication aligned with the Incident Command System (ICS). Rather than functioning solely as advisory tools, VLMs facilitate communication between human operators, mission control logic, and UAV task execution. The framework was developed using a Model-Based Systems Engineering (MBSE) approach, with use case and block definition diagrams representing system roles, internal structure, and component interactions. Three key elements, the VLM Coordinator Agent, UAV Mission Control, and Task Allocator, were implemented within an integrated simulation and control environment. A preliminary human-factors evaluation with seven participants showed reduced perceived workload across mental demand, effort, and frustration, along with high ratings for AI trust and communication clarity. By integrating MBSE, software-in-the-loop testing, and human-factors evaluation, this work advances scalable human-autonomy teaming for high-stakes disaster response, with broader implications for aerospace autonomy and civil safety.
Comments: 10 pages, 8 figures. Author accepted manuscript of AIAA Paper 2026-4010, published in the AIAA AVIATION 2026 Forum
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
Cite as: arXiv:2607.27597 [cs.RO]
(or arXiv:2607.27597v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.27597
arXiv-issued DOI via DataCite (pending registration)
Journal reference: AIAA AVIATION 2026 Forum, AIAA Paper 2026-4010, 2026
Related DOI:
https://doi.org/10.2514/6.2026-4010
DOI(s) linking to related resources
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From: Swapnil Saha [view email] [v1] Thu, 30 Jul 2026 02:35:39 UTC (4,090 KB)
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