Xisheng Feng

About

Xisheng Feng is a leading researcher at the intersection of underwater robotics, brain-computer interfaces (BCIs), and intelligent control systems. His work primarily focuses on advancing autonomous underwater vehicles (AUVs) and multi-robot systems, with significant contributions to cooperative search strategies, sonar-based perception, and reinforcement learning for dynamic control. Feng’s most cited paper, "An Analysis of Deep Learning Models in SSVEP-Based BCI: A Survey" (2023, 48 citations), highlights his expertise in neural signal processing and human-robot interaction, offering a comprehensive review of deep learning applications for steady-state visual evoked potentials. He has also pioneered novel approaches in underwater robotics, including cooperative area search using target prediction (26 citations) and scan registration for mechanical imaging sonar using Kullback–Leibler divergence (13 citations). His work on model-free recurrent reinforcement learning for AUV horizontal control (12 citations) and tiltrotor position tracking (4 citations) demonstrates his commitment to robust, adaptive control in challenging environments. Additionally, Feng has explored trans-domain robotics, such as air-water tiltrotors, and developed a fast hand-eye calibration method using stereo cameras (3 citations). With over 100 total citations, his research is shaping the future of autonomous systems and human-robot collaboration.

Research Focus

Key Achievements

Top Papers

1. 1
   An Analysis of Deep Learning Models in SSVEP-Based BCI: A Survey

   48 citations · 2023
2. 2
   Research on cooperative area search of multiple underwater robots based on the prediction of initial target information

   26 citations · 2019
3. 3
   Scan registration for underwater mechanical scanning imaging sonar using symmetrical Kullback–Leibler divergence

   13 citations · 2019
4. 4
   Model-Free Recurrent Reinforcement Learning for AUV Horizontal Control

   12 citations · 2018
5. 5
   Tiltrotors Position Tracking Controller Design Using Deep Reinforcement Learning

   4 citations · 2020
6. 6
   Memory-Based Reinforcement Learning for Trans-Domain Tiltrotor Robot Control

   3 citations · 2020
7. 7
   A fast and straightforward hand-eye calibration method using stereo camera

   3 citations · 2022
8. 8
   Research on Synergy Pursuit Strategy of Multiple Underwater Robots

   3 citations · 2019
9. 9
   A Novel Cooperative Pursuit Strategy in Multiple Underwater robots

   2 citations · 2019

Key Collaborators

Contact & Links