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About

Nanxun Duo is a leading researcher in multi-robot systems, specializing in formation control, navigation, and distributed intelligence. Their work addresses critical challenges in coordinating robot swarms under real-world constraints, such as noise, time delays, and limited communication. Duo’s highly cited 2019 paper on consensus algorithms for multi-robot formation control (20 citations) provides a robust framework for maintaining stable formations in uncertain environments, while their 2018 study on UWB-based distance measurement (14 citations) introduces a state-switching strategy that reduces network traffic, a key innovation for scalable systems. In navigation, Duo has pioneered mapless approaches using deep reinforcement learning, including a 2019 paper on continuous-action navigation (8 citations) and a 2018 Q-learning-based method (7 citations) that enables collision-free, end-to-end control from lidar inputs—eliminating the need for pre-mapped environments. With over 49 total citations, Duo’s contributions are foundational for autonomous robotics, particularly in logistics, search-and-rescue, and industrial automation. Their work bridges theoretical consensus algorithms and practical, model-free navigation, making them a key figure in advancing resilient, intelligent multi-robot teams.