Papers
1
Total Citations
19
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1
About
Mingsheng Fu is a researcher specializing in reinforcement learning and autonomous robotics, with a particular focus on enabling intelligent navigation in dynamic environments. His most-cited work, "Reinforcement Learning for Mobile Robot Obstacle Avoidance Under Dynamic Environments" (2018), has garnered 19 citations, establishing a foundational approach for training robots to adaptively avoid obstacles in real-time, unpredictable settings. This contribution addresses a critical challenge in mobile robotics—moving beyond static, pre-programmed paths to systems that learn and react to changing surroundings. Fu’s research integrates reinforcement learning algorithms with sensor-based decision-making, offering a scalable framework for safer and more efficient autonomous navigation. His work is notable for bridging theoretical advances in machine learning with practical robotic applications, making it valuable for researchers in both AI and robotics. By demonstrating how robots can learn collision-free behaviors through trial and error, Fu has contributed to the broader goal of deploying autonomous systems in complex, human-centric environments, such as warehouses, hospitals, and urban spaces. His ongoing efforts continue to push the boundaries of adaptive robot intelligence.
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Top Papers
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