Hau Chan

Papers

Total Citations

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About

Hau Chan is a researcher whose work centers on the intersection of artificial intelligence, robotics, and algorithmic optimization, with a particular focus on multi-robot task allocation (MRTA). His research addresses one of the most foundational challenges in modern robotics: efficiently coordinating multiple robots to accomplish complex, real-world tasks such as search and rescue operations and area exploration. Chan's most notable contribution examines the Single-Task robots and Multi-Robot tasks Instantaneous Assignment (ST-MR-IA) framework, where he rigorously investigates the computational complexity of task allocation problems and develops approximation algorithms to make these problems tractable at scale. This work is especially significant given the practical stakes involved — effective task allocation is critical when robotic systems must operate reliably in dynamic, high-pressure environments where human lives may depend on efficient coordination. His 2021 paper on MRTA complexity and approximation has accumulated 14 citations, reflecting growing interest in this area within the robotics and AI communities. By bridging theoretical computer science with applied robotics, Chan's research provides both mathematical foundations and practical tools for the next generation of autonomous multi-robot systems, making his work valuable to engineers and researchers designing real-world robotic deployments.