Papers
5
Total Citations
76
H-Index
4
About
Fan Bu is a robotics and autonomous systems researcher whose work spans pedestrian detection, safe trajectory planning, and human-robot interaction. His most recognized contribution, the Pedestrian Planar LiDAR Pose (PPLP) Network (2019), addressed a critical cost barrier in autonomous driving by fusing data from affordable planar LiDAR sensors with monocular camera imagery to achieve reliable oriented pedestrian detection — earning 29 citations and offering a practical alternative to expensive 3D LiDAR-dependent systems. Bu has also made meaningful strides in real-time safe motion planning for mobile robots, developing receding-horizon trajectory frameworks that bridge the persistent tension between computational speed and provable safety guarantees, work that has accumulated over 30 citations across related publications. His theoretical contributions extend to fault-tolerant autonomy, where he explored provably not-at-fault control strategies enabling robots to navigate unpredictable environments with limited environmental information. Earlier in his career, Bu investigated multimodal gesture recognition by fusing IMU and sEMG sensor data through multiple kernel learning, demonstrating a broad interest in intuitive human-robot interfaces. Together, his research reflects a consistent commitment to making autonomous systems safer, more perceptive, and more accessible in real-world deployment.
Research Focus
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Top Papers
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