Yinliang Chen
Papers
1
Total Citations
24
H-Index
1
About
Yinliang Chen is a robotics researcher whose work focuses on intelligent path planning and autonomous navigation in complex environments. His most significant contribution is the development of an improved Deep Deterministic Policy Gradient (DDPG) algorithm enhanced with Sequential Linear Path Planning (SLP), designed specifically for mobile robots operating in large-scale, dynamic settings. This innovative approach addresses the critical challenge of real-time obstacle avoidance while maintaining efficient, collision-free trajectories—a problem that has long limited the deployment of autonomous systems in unpredictable real-world conditions. With 24 citations to date, his 2023 study has already garnered attention from the robotics and reinforcement learning communities for its practical effectiveness. Chen’s work bridges the gap between deep reinforcement learning and real-world robotic applications, offering a scalable solution that outperforms traditional path planning methods in both simulation and physical environments. His research holds particular promise for applications in warehouse automation, autonomous delivery, and search-and-rescue operations, where robots must navigate among moving obstacles without human intervention.
Research Focus
Key Achievements
Top Papers
- 1