2026/06/04 by Swapnil Saha, Bhuvan Rajanasiriyur Jagadeesha, Karishma Patnaik +1
Computer Science · Engineering · #Air Traffic Management and Optimization #UAV Applications and Optimization #Wetland Management and Conservation #cs.AI #cs.RO #cs.SY #eess.SY
paper · pdf · doi:10.2514/6.2026-4010
published as AIAA AVIATION 2026 Forum, AIAA Paper 2026-4010, 2026 · 10 pages, 8 figures. Author accepted manuscript of AIAA Paper 2026-4010, published in the AIAA AVIATION 2026 Forum
openalex publication_date 2026/06/04 · openalex created_date 2026/06/05 · openalex updated_date 2026/07/29 · arxiv created 2026/07/30 · arxiv updated 2026/07/31
Recent advances in Vision Language Models (VLMs) have created new opportunities for disaster response, where responders must interpret large volumes of sensor data under critical time pressure. Current VLM applications in this domain include social media monitoring for situational awareness, generation of draft action plans, and translation of complex technical alerts into public facing messages. While these efforts demonstrate the potential of VLMs to accelerate information flow, they remain largely limited to decision-support roles. In practice, such approaches can increase operator burden, as 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, employing use case and block definition diagrams to represent system roles, internal structure, and component interactions. Three key components, 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.