Papers
10
Total Citations
219
H-Index
8
About
Guanqiang Gao is a computational intelligence and robotics researcher whose work sits at the intersection of multi-robot systems, motion planning, and evolutionary optimization. His most influential contributions center on the Multi-Point Dynamic Aggregation (MPDA) problem — a task coordination framework he helped formalize, modeling scenarios where teams of heterogeneous robots must collaboratively complete geographically distributed, time-varying missions such as post-disaster relief, bushfire suppression, and medical resource deployment. His 2021 paper introducing Adaptive Coordination Ant Colony Optimization for this domain has accumulated 57 citations, reflecting strong community uptake. Beyond aggregation problems, Gao has made substantial contributions to multi-robot coverage motion planning, proposing the auction-based Spanning Tree Coverage (A-STC) algorithm and distributed cooperative multi-area coverage strategies that together account for over 70 additional citations. His algorithmic toolkit spans genetic programming, memetic algorithms, estimation of distribution algorithms, and multi-objective evolutionary frameworks, demonstrating both breadth and methodological rigor. With a research trajectory progressing from foundational task models (2016) to sophisticated multi-objective formulations (2022), Gao has established himself as a productive voice in autonomous multi-robot coordination, offering solutions with clear real-world relevance across emergency response and environmental monitoring applications.
Research Focus
Key Achievements
Top Papers
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- 3Distributed multi-robot motion planning for cooperative multi-area coverage31 citations · 2017
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