Shengli Zhou

University of Connecticut, University of Washington

Papers

Total Citations

H-Index

About

Shengli Zhou is a pioneering researcher at the intersection of autonomous robotics, environmental remediation, and wireless communication systems. His work primarily focuses on developing intelligent algorithms for multi-robot coordination in hazardous and unknown environments, with particular emphasis on oil spill response and underwater exploration. Zhou's most influential contribution is his 2013 paper on adaptive oil spill cleaning by autonomous vehicles under partial information (30 citations), which addresses the critical challenge of dynamic current conditions in environmental disaster response. He has also made significant advances in robotic learning through his 2006 work on probabilistic gaze imitation and saliency learning (28 citations), establishing Bayesian frameworks for human-robot interaction. His research extends to cooperative coverage algorithms for autonomous underwater vehicles in unknown environments (13 citations), enabling time-critical operations with minimal human supervision. Most recently, Zhou has contributed to industrial wireless communications with his 2022 work on RT-WiFi implementation on software-defined radio (7 citations), demonstrating the potential for high-speed, low-latency wireless technologies in mobile industrial applications. His interdisciplinary approach combines theoretical rigor with practical implementation, making substantial contributions to both environmental robotics and industrial automation.