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About

Rong Zheng is a leading researcher at the intersection of robotics, mobile sensing, and artificial intelligence, with a primary focus on developing intelligent, autonomous systems for environmental data collection. Her major contributions lie in pioneering the use of reinforcement learning and deep reinforcement learning for informative path planning and multi-robot coordination, enabling mobile robots to efficiently gather large-scale spatial data—such as air quality, temperature, and humidity—under strict energy and travel constraints. Her most cited work, "Informative Path Planning for Mobile Sensing with Reinforcement Learning" (2020, 39 citations), demonstrates how robots can autonomously maximize data utility, a critical advancement for applications in smart cities and environmental monitoring. She has further extended this to multi-robot systems (2021, 25 citations), accelerating data collection over vast areas. Zheng has also made notable strides in robust multiple blind sound source localization (2021, 15 citations), enhancing robotic navigation and indoor localization capabilities. Her earlier work on obstacle discovery in distributed active sensor networks (2009) laid foundational principles for collaborative sensing. With over 90 total citations, Zheng’s research is shaping the future of autonomous, energy-efficient sensing systems.