Papers
12
Total Citations
178
H-Index
6
About
Fuqin Deng is a robotics and autonomous systems researcher whose work sits at the intersection of multi-robot coordination, deep reinforcement learning, and intelligent perception. His research addresses some of the most challenging problems in modern robotics, including multi-robot simultaneous localization and mapping (MR-SLAM), heterogeneous robot collaboration, and autonomous path planning in dynamic environments. Deng's most influential contribution, "Semantic Histogram Based Graph Matching for Real-Time Multi-Robot Global Localization in Large Scale Environment" (2021, 78 citations), introduced a novel approach to tackling viewpoint-invariant localization across multiple robots — a fundamental bottleneck in scalable robotic deployments. His work on deep multiagent reinforcement learning, particularly through asymmetric self-play for heterogeneous robot catching systems (35 citations) and the AB-Mapper path planning algorithm, demonstrates a sustained commitment to advancing intelligent multi-agent decision-making. He also developed MultiRoboLearn, an open-source framework bridging reinforcement learning research with practical multi-robot applications. Beyond coordination and navigation, Deng has extended his expertise into agricultural robotics, with recent work on banana bunch weight estimation using RGB-D imaging. Collectively, his publications reflect a researcher steadily building impactful, application-driven contributions across core domains of autonomous robotics.
Research Focus
Key Achievements
Top Papers
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- 3Complete Coverage Path Planning for Intelligent Sweeping Robot17 citations · 2021
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- 9Abnormal Occupancy Grid Map Recognition using Attention Network4 citations · 2022
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