Bowen Xing

Shanghai Ocean University, Shanghai Jiao Tong University

Papers

Total Citations

H-Index

About

Bowen Xing is a robotics researcher focused on advancing autonomous navigation and path planning for mobile and search-and-rescue robots. His core research areas include complete coverage path planning, multi-sensor fusion for map building, and reinforcement learning-based navigation algorithms. Xing’s most cited work proposes an Artificial Bee Colony optimization method to enhance the efficiency of search-and-rescue robots operating in complex environments, addressing the critical challenge of low path planning efficiency. He further improved map-building accuracy by optimizing the Cartographer algorithm with voxel and radius filtering to fuse multi-source sensor data, tackling issues of positional inaccuracy and delay. More recently, Xing has explored deep reinforcement learning, developing an improved Deep Q-Network (DQN) algorithm for complete coverage path planning. With over 12 citations across his top papers, Xing’s contributions are particularly relevant to researchers and engineers working on autonomous robotics in unstructured or hazardous settings. His work demonstrates a systematic progression from bio-inspired optimization to modern learning-based approaches, making him a notable emerging voice in the field of mobile robot autonomy.