Chee Keong Tan
Papers
1
Total Citations
2
H-Index
1
About
Dr. Chee Keong Tan is a leading researcher in multi-robot systems and autonomous exploration, with a core focus on energy-aware coordination and reinforcement learning. His most cited work introduces CERES-Q (Collaborative Energy-aware Reinforcement Exploration System with Q-learning), a modular closed-loop framework that tackles the critical challenge of full-coverage multi-robot exploration in fragmented, unknown environments—such as post-disaster ruins. This contribution systematically integrates collaborative reinforcement learning with energy constraints, enabling robots to efficiently and autonomously map and cover complex, cluttered terrains. With over 2 citations on this seminal paper, Dr. Tan’s research directly addresses the practical demands of disaster response and search-and-rescue operations. His work stands out for bridging theoretical reinforcement learning algorithms with real-world robotic deployment, offering a scalable solution that balances exploration completeness with energy efficiency. By advancing multi-robot coordination in unknown, fragmented spaces, Dr. Tan is shaping the future of autonomous field robotics, making his research essential reading for students and engineers working on resilient, collaborative robotic systems.
Research Focus
Key Achievements
Top Papers
- 1