Keyan He

National University of Defense Technology

Papers

Total Citations

H-Index

About

Keyan He is a researcher advancing the frontier of multi-robot systems, with a primary focus on cooperative autonomous exploration, collaborative simultaneous localization and mapping (CSLAM), and deep reinforcement learning for robotics. His most impactful work, "Multi-robot cooperative autonomous exploration via task allocation in terrestrial environments" (2023, 13 citations), addresses the fundamental challenge of enabling robot teams to efficiently cover unknown areas by intelligently dividing exploration tasks—a critical capability for search-and-rescue, environmental monitoring, and industrial inspection. He further tackles the communication and fusion bottlenecks in GPS-denied environments through his work on LDG-CSLAM (2025), which integrates curve analysis, normal distribution transforms, and factor graph optimization to enhance multi-robot mapping robustness. Demonstrating versatility, He also explores learning-based approaches in "Autonomous exploration through deep reinforcement learning" (2023), proposing hybrid methods that combine traditional planning with neural policies to manage computational costs in large-scale environments. His research directly addresses real-world deployment challenges, balancing theoretical rigor with practical efficiency. With a growing citation record and contributions spanning task allocation, sensor fusion, and adaptive learning, Keyan He is establishing himself as a promising voice in autonomous robotics.