Papers
13
Total Citations
171
H-Index
7
About
Shengkang Chen is a leading researcher in multi-robot systems, specializing in heterogeneous robot teams, task allocation, and autonomous exploration of extreme environments. His most impactful work stems from his integral role in Team CSIRO Data61’s success at the DARPA Subterranean Challenge, where he helped develop a groundbreaking solution that tied for the top score. This work, detailed in a 2022 paper with 97 citations, demonstrated how heterogeneous ground and air platforms with unified perception and autonomy can master the treacherous, GPS-denied underground world. Chen’s contributions extend to dynamic task allocation algorithms—including hybrid and game-theoretical approaches—that balance robot autonomy with human expertise through multi-modal user interfaces. His research, with papers accumulating over 160 citations, has set new standards for coordinating diverse robotic teams in unstructured environments. Notably, Chen has also explored the ethical dimensions of robotics, comparing human and large language model judgments on robot deception. His work is essential reading for anyone interested in the frontier of field robotics, multi-agent coordination, and the practical deployment of autonomous systems in disaster response and subterranean exploration.
Research Focus
Key Achievements
Top Papers
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- 5Hybrid SUSD-Based Task Allocation for Heterogeneous Multi-Robot Teams9 citations · 2023
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- 10Counter-Misdirection in Behavior-based Multi-robot Teams3 citations · 2021