Chaoxu Mu
Papers
7
Total Citations
135
H-Index
5
About
Chaoxu Mu is a leading researcher in safe reinforcement learning, adaptive optimal control, and multi-robot coordination, with a focus on developing intelligent control strategies for autonomous systems operating in complex, dynamic environments. Her most-cited work (71 citations) introduces a novel composite obstacle avoidance method that integrates barrier functions into cost functions, ensuring system safety through forward invariance while enabling adaptive motion planning. This approach has significant implications for autonomous vehicles and robotics. Mu has also pioneered robust adaptive critic control with network-based event-triggered formulations (31 citations), addressing real-time efficiency in networked systems. Her recent contributions include deep reinforcement learning frameworks for multi-station multi-robot task assignment (10 citations) and cooperative control for air-ground systems that handle environmental disturbances like road bumps and gust winds (9 citations). Notably, her work on conditional disturbance negation reveals that disturbances can sometimes be harnessed to improve control performance—a counterintuitive insight with broad applications. With over 135 total citations and publications spanning from 2017 to 2025, Mu’s research bridges theoretical rigor and practical deployment, making her a key figure in next-generation autonomous control.
Research Focus
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