Papers
5
Total Citations
26
H-Index
3
About
Kang An is a robotics researcher whose work spans biomechanically inspired locomotion, indoor localization, and autonomous systems. Best known for his contributions to biped robot control, An developed a rhythmic-reflex hybrid adaptive walking algorithm — his most-cited work with 12 citations — that draws on central pattern generation principles to coordinate complex multi-degree-of-freedom robotic motion, achieving stable and adaptive bipedal locomotion. Complementing this, his dynamic optimization studies of upper body parameters have deepened understanding of walking efficiency in biped robot design, informing how mass and limb geometry influence mechanical performance. Beyond legged robotics, An has made meaningful contributions to indoor positioning, proposing a vertically uniform alternating magnetic field approach to address the persistent multipath challenges of wireless localization for humans and mobile service robots, earning 6 citations. His more recent work demonstrates a broadening research portfolio: a cuckoo search–BP neural network method for collaborative robot joint parameter identification addresses real-world accuracy demands in human-robot interaction, while an ROS-based autonomous underwater vehicle control system extends his expertise into marine robotics. Across these domains, An consistently bridges theoretical modeling with practical experimental validation, making his research valuable to both designers and engineers in the robotics community.
Research Focus
Key Achievements
Top Papers
- 1Rhythmic-Reflex Hybrid Adaptive Walking Control of Biped Robot12 citations · 2018
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- 5Design and Experiments on AUV Control System based on ROS2 citations · 2024