Mingxiang Wang

Peking University, Xi'an University of Technology

Papers

Total Citations

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About

Mingxiang Wang is a leading researcher in autonomous mobile robotics, with a primary focus on consensus control, path planning, and obstacle avoidance for unmanned ground and wheeled mobile robots. His most cited work addresses the challenging problem of consensus control for nonholonomic wheeled mobile robots under input saturation constraints, where he introduced a specified-time observer that enables robots to estimate a leader’s linear and angular velocities using only local configuration data—a significant contribution to multi-robot coordination in constrained environments. In a second highly influential paper, Wang tackled slow convergence and collision risks in unknown complex environments by developing an improved Q-Learning algorithm integrated with a flower pollination algorithm, achieving optimized path planning with enhanced obstacle avoidance. With over 25 citations across his top papers, Wang’s work bridges theoretical control theory and practical reinforcement learning, offering scalable solutions for real-world autonomous navigation. His research is particularly notable for its direct applicability to field robotics, where input saturation and dynamic obstacles pose critical challenges. Wang’s contributions continue to shape the development of safer, more efficient autonomous systems.