Yongbo Chen
Papers
7
Total Citations
216
H-Index
6
About
Yongbo Chen is a robotics researcher whose work centers on autonomous navigation, simultaneous localization and mapping (SLAM), and multi-robot systems. His most significant contribution lies in advancing **active SLAM** — the challenge of enabling robots to autonomously plan trajectories that simultaneously minimize localization uncertainty, achieve area coverage, and avoid obstacles. His 2020 paper, "Active SLAM for Mobile Robots With Area Coverage and Obstacle Avoidance," has accumulated 118 citations, establishing him as a notable voice in this specialized field. Chen's research consistently leverages model predictive control (MPC), graph topology, and convex optimization to develop computationally efficient SLAM frameworks applicable to both 2D and 3D environments. His work on cooperative multi-robot active SLAM introduces clever strategies — such as broadcasting pose-graph weaknesses — to enable low-cost coordination between agents. He has also made practical contributions through submap-based navigation systems demonstrated on real hardware platforms like the Fetch robot, bridging theoretical advances with real-world deployment. With a portfolio spanning anchor selection strategies, submodular optimization, and large-scale indoor navigation, Chen's research addresses scalability and practicality in robotic mapping, making his work valuable to students and practitioners working at the intersection of probabilistic robotics and autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1Active SLAM for Mobile Robots With Area Coverage and Obstacle Avoidance118 citations · 2020
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- 6Submap-Based Indoor Navigation System for the Fetch Robot11 citations · 2020
- 7Indoor Navigation System Using the Fetch Robot6 citations · 2019