Papers
5
Total Citations
54
H-Index
4
About
Xiangquan Tan is a robotics researcher whose work spans biologically inspired locomotion, autonomous navigation, and multi-sensor fusion for mobile robots. His most cited paper, "Biologically Inspired Complete Coverage Path Planning Algorithm Based on Q-Learning" (2023, 30 citations), addresses a critical challenge in robotics: enabling mobile robots to traverse every reachable point in an environment efficiently. By integrating Q-learning with biologically inspired neural networks, Tan’s algorithm overcomes issues like local optima and low coverage ratios, advancing the field of autonomous exploration. His earlier contributions include self-positioning systems for indoor robots, combining wheel odometry, inertial measurement units, and ultra-wideband technology to achieve reliable navigation without GPS (2021, 10 citations). Tan also developed an omnidirectional indoor mobile robot system using multi-sensor fusion (2021, 5 citations) and designed a frog-like hopping robot with optimized kinematics and vibration reduction (2012, 5 and 4 citations). With over 50 total citations across his publications, Tan’s work demonstrates a consistent focus on bridging biological principles and engineering solutions, making him a notable figure in robotics research.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5