Chanelle Lee

Bristol Robotics Laboratory, University of the West of England

Papers

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About

Chanelle Lee is a researcher in swarm robotics, specializing in collective decision-making algorithms that enable robot swarms to reach consensus in uncertain environments. Her work focuses on the best-of-n problem, where a group of robots must collectively select the best option among several alternatives. Lee’s major contribution is the development and refinement of negative updating—a mechanism that allows robots to penalize poor options rather than simply reinforcing good ones—combined with opinion pooling to improve decision accuracy. Her 2018 paper, "Negative Updating Combined with Opinion Pooling in the Best-of-n Problem in Swarm Robotics," has garnered 16 citations, while her 2021 follow-up, "Negative Updating Applied to the Best-of-n Problem with Noisy Qualities," earned 15 citations. These works demonstrate how negative updating enhances robustness against noisy sensor data, a critical challenge for real-world deployment. Lee’s research bridges theoretical frameworks and practical applications, offering scalable solutions for robot swarms operating in dynamic, unpredictable environments. Her achievements highlight her as a rising contributor to swarm intelligence, with potential impacts on autonomous exploration, environmental monitoring, and distributed sensing.