Rachel Zheng

Papers

Total Citations

H-Index

About

Rachel Zheng is a rising leader in multi-robot systems and autonomous search and rescue (SaR), whose work bridges natural language processing with robotic planning. Her research focuses on developing intelligent coordination strategies that enable robot fleets to operate efficiently in unknown, hazardous environments. Zheng’s most influential contribution, “Exploiting Natural Language for Efficient Risk-Aware Multi-Robot SaR Planning” (2021, 15 citations), pioneered the use of natural language commands from human commanders to enhance scene understanding and risk perception, allowing robots to prioritize dangerous zones during victim search. This work uniquely integrates linguistic cues with visual data, improving mission safety and efficiency. In her subsequent study, “Graph-based Simultaneous Coverage and Exploration Planning for Fast Multi-robot Search” (2023, 3 citations), she introduced a novel graph-based algorithm that solves the dual challenge of mapping unknown spaces while systematically covering areas for victims—a critical advancement for time-sensitive disasters. Zheng’s contributions are notable for their practical impact on real-world SaR operations, reducing search times through parallelized robot coordination. Her interdisciplinary approach, combining natural language understanding with multi-agent planning, positions her at the forefront of human-robot teaming in emergency response.