Papers
6
Total Citations
310
H-Index
6
About
Chengmin Zhou is a leading researcher in intelligent robotics, specializing in motion planning, reinforcement learning, and sensor-based obstacle avoidance. His seminal 2021 review, "A review of motion planning algorithms for intelligent robots," has garnered 234 citations, establishing a comprehensive taxonomy of traditional algorithms, classical machine learning, and reinforcement learning approaches. Zhou's major contributions include developing an attention-based advantage actor-critic algorithm with prioritized experience replay (2022, 21 citations), which significantly improves robotic navigation in dense, dynamic indoor environments by encoding complex features and handling unpredictable obstacles. His 2023 work on representation learning and reinforcement learning (16 citations) further advances dynamic motion planning systems, addressing limitations of classical algorithms in high-density scenarios. Zhou also pioneered a novel obstacle avoidance scheme combining CNN-based deep learning with LiDAR image processing (2018, 16 citations), and designed an autonomous robotic car using ROS (2020, 15 citations). His research bridges theoretical frameworks with practical implementations, offering robust solutions for autonomous vehicles and assistive robotics. With over 300 total citations, Zhou's work is essential reading for researchers tackling real-world robotic navigation challenges.
Research Focus
Key Achievements
Top Papers
- 1A review of motion planning algorithms for intelligent robots234 citations · 2021
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- 6A review of motion planning algorithms for intelligent robotics8 citations · 2021