Papers
7
Total Citations
93
H-Index
5
About
Xiangyuan Jiang is a leading researcher in autonomous robotics, specializing in source exploration, plume tracking, and multi-robot coordination. His groundbreaking work on "Plume Front Tracking in Unknown Environments by Estimation and Control" (31 citations) introduced a control-theoretic paradigm for robots to monitor and track hazardous environmental plumes, such as oil spills, without prior knowledge of physical parameters—a critical advancement for disaster response. Jiang further advanced autonomous search with his 2019 study on "Source Exploration for an Under-Actuated System" (22 citations), which tackled the challenge of locating unknown signal sources using limited measurements, overcoming the lack of a priori distribution data. In multi-robot systems, Jiang developed the Lin–Kernighan–Helsgaun Guided Evolutionary Algorithm for multi-objective task allocation (12 citations), optimizing energy consumption and completion time simultaneously. His recent work integrates graph deep reinforcement learning with graph normalization for scalable task allocation (2024, 5 citations), enabling efficient solutions for large-scale robotic teams. Jiang also contributed to wearable sensor networks through beetle antennae search strategies (2020, 12 citations) and indoor robot positioning using ArUco arrays (2023, 2 citations). With over 90 total citations, his research bridges control theory, estimation, and machine learning, driving practical advances in environmental monitoring, industrial automation, and autonomous navigation.
Research Focus
Key Achievements
Top Papers
- 1Plume Front Tracking in Unknown Environments by Estimation and Control31 citations · 2018
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