Papers
19
Total Citations
442
H-Index
11
About
Dan Wang is a prominent researcher specializing in autonomous marine and mobile robotics, with particular expertise in cooperative control, path following, and reinforcement learning-based motion control. His work has made significant contributions to the guidance and control of autonomous surface vehicles (ASVs) and multi-agent robotic systems, addressing real-world challenges such as model uncertainties, environmental disturbances from wind and waves, and complex communication constraints. Wang's most influential contribution, "Anti-disturbance Coordinated Path-following Control of Robotic Autonomous Surface Vehicles" (2019, 86 citations), established robust frameworks for networked underactuated ASVs operating under adverse ocean conditions. His research consistently bridges theoretical rigor with experimental validation, a hallmark evident across his portfolio. His 2022 work integrating model-based deep reinforcement learning for unmanned surface vehicles reflects his forward-looking embrace of data-driven approaches, while his investigations into multi-agent flocking and containment maneuvering demonstrate broad expertise in cooperative robotics. With over 380 cumulative citations across his top works, Wang's research meaningfully advances autonomous maritime navigation, multi-robot coordination, and adaptive control systems. His ability to combine classical control theory with modern machine learning techniques positions him as an important bridge-builder in the evolving field of intelligent autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 2Path-Guided Containment Maneuvering of Mobile Robots: Theory and Experiments61 citations · 2020
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- 4Algorithms and Experiments on Flocking of Multiagents in a Bounded Space41 citations · 2013
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