Max Robotics — Global Service Robot Directory + US Certification

About

Baifan Chen is a leading researcher in mobile robotics, specializing in autonomous navigation, simultaneous localization and mapping (SLAM), and perception for humanoid and wheeled robots. Chen’s major contributions span adaptive locomotion and robust multi-sensor fusion, with pioneering work on Matsuoka’s central pattern generator (CPG) for adaptive walking in humanoid robots—a highly cited study (48 citations) that introduced desired rhythmic signals for stable gait generation. In perception, Chen developed loop closure detection using multi-scale deep feature fusion (31 citations) and advanced place recognition with the AANet framework, which employs semi-hard positive sample mining for hierarchical localization. Chen’s impact is further evidenced by innovations in heterogeneous map fusion, enabling asynchronous monocular vision and lidar integration (24 citations), and the Marked-LIEO system (10 citations), which fuses visual markers, LiDAR, IMU, and encoder data for precise odometry in challenging indoor corridors. Notable achievements include real-time dynamic obstacle detection with laser radar and hybrid data association for SLAM, alongside recent work combining soft actor-critic reinforcement learning with potential fields for global path planning. With over 150 total citations, Chen’s research continues to shape autonomous navigation, offering practical solutions for complex, real-world environments.