Papers
1
Total Citations
31
H-Index
1
About
Kaili Wu is a leading researcher in the field of multi-agent systems and autonomous control, with a primary focus on the coordination and intelligence of unmanned aerial vehicles (UAVs). Their most notable contribution is the development of optimal formation tracking control for multi-UAV systems using reinforcement learning, a breakthrough that addresses critical challenges in dynamic, real-time decision-making for swarms of drones. This work, published in 2023 and already garnering 31 citations, demonstrates Wu’s ability to bridge theoretical control theory with practical, scalable solutions for autonomous flight. By integrating reinforcement learning into formation tracking, Wu has advanced the efficiency and adaptability of UAV operations in complex environments, such as surveillance, search-and-rescue, and collaborative mapping. Their research stands out for its rigorous mathematical framework and potential for real-world deployment, offering a pathway toward fully autonomous, self-organizing aerial fleets. As a rising scholar, Kaili Wu’s work is shaping the next generation of intelligent control systems, making them a key figure to watch in the evolution of multi-robot coordination.
Research Focus
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Top Papers
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