Shuren Mao
Papers
1
Total Citations
32
H-Index
1
About
Shuren Mao is a researcher at the forefront of intelligent robotics and autonomous navigation, with a primary focus on enhancing the decision-making capabilities of mobile robots in complex indoor environments. His most-cited work, "Improved Path Planning for Indoor Patrol Robot Based on Deep Reinforcement Learning" (2022, 32 citations), addresses critical limitations in traditional deep reinforcement learning—namely, poor exploration ability and slow convergence—by integrating Pan/Tilt/Zoom (PTZ) image information into the navigation algorithm. This innovation significantly improves the efficiency and reliability of patrol robots operating along specified indoor routes. Mao’s contributions lie at the intersection of computer vision and reinforcement learning, offering practical solutions for real-world robotic tasks such as security patrol and autonomous inspection. His research has garnered attention for its potential to advance adaptive path planning in constrained settings, with his 2022 paper serving as a key reference for subsequent studies in deep RL-based navigation. Through this work, Mao demonstrates a clear commitment to bridging theoretical algorithms with deployable robotic systems.
Research Focus
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Top Papers
- 1