Research

Our multi-agent control research explores cooperative and coordinated behaviors among multiple autonomous robots and intelligent agents in diverse applications. We design distributed control strategies and optimization frameworks that enable efficient communication, consensus-building, and collective decision-making in dynamic environments. Our work encompasses formation control, cooperative manipulation, multi-robot task allocation, and swarm robotics. We address fundamental challenges in scalability, robustness to failures, and emergent collective behaviors for teams of heterogeneous agents.

We develop advanced control algorithms for autonomous and connected vehicles, focusing on optimal trajectory planning, adaptive cruise control, and vehicle stability systems. Our research integrates machine learning techniques with model-based control approaches to achieve safe, efficient, and comfortable autonomous driving in complex traffic environments. We address challenges in real-time decision-making, uncertainty handling, and robust performance under varying road and weather conditions.

We investigate how autonomous systems can better understand, predict, and collaborate with human operators and other road users. Our research focuses on human behavior modeling, intent recognition, and adaptive interaction strategies that enhance safety and user experience. We develop frameworks for shared control, trust calibration, and intuitive interfaces that facilitate seamless collaboration between humans and autonomous systems in transportation and robotics applications.