Pacific (or Vancouver, Canada) time = September 24, 2026 7-8pm.

About the Talk:
Intelligent control of UAVs (Uncrewed Aerial Vehicles) swarms typically requires the swarm to navigate effectively while avoiding obstacles and achieving continuous coverage over multiple mission targets. Although traditional Multi-Agent Reinforcement Learning (MARL) approaches offer dynamic adaptability, they are hindered by the semantic gap in black-boxed communication and the rigidity of homogeneous role structures, resulting in poor generalization and limited task scalability. Recent advances in Large Language Model (LLM)-based control frameworks demonstrate strong semantic reasoning capabilities by leveraging extensive prior knowledge. Nevertheless, due to the lack of online learning and over-reliance on static priors, these works often struggle with effective exploration, leading to reduced individual potential and overall system performance. 

This session will feature Prof. Honggang Zhang from Macau University of Science and Technology, who will address these limitations by proposing RALLY: a role-adaptive navigation framework that leverages Large Language Models (LLMs) for autonomous, collaborative coordination among UAV swarms. The talk addresses key limitations of conventional multi-agent reinforcement learning by introducing LLM-driven semantic reasoning and adaptive role-switching. We believe this will be of great interest to anyone working in multi-agent systems, robotics, or AI-driven control frameworks.

Talk Title:
RALLY: Role-Adaptive LLM-Driven Yoked Navigation for Agentic UAV Swarms

Speaker:

Prof. Honggang Zhang
School of Computer Science and Engineering
Macau University of Science and Technology

Moderator:

Edward Au, Ph.D. (Editor-in-Chief, IEEE Open Journal of Vehicular Technology)