Jinlin Chen
Papers
9
Total Citations
68
H-Index
5
About
Jinlin Chen is a leading researcher in multi-robot systems, with a focus on cooperative pattern formation, distributed collision avoidance, and scalable reinforcement learning. His work addresses fundamental challenges in coordinating large-scale robotic teams, particularly in partially observable and dynamic environments. Chen’s 2019 paper “Pattern-RL” introduced a deep reinforcement learning approach for autonomous pattern formation, enabling robots to self-organize into circles, lines, or meshes—critical for military, search-and-rescue, and surveillance applications. His 2016 work on programming large-scale multi-robot systems with timing constraints advanced middleware design for reliable task execution. More recently, Chen developed efficient distributed collision avoidance algorithms for heterogeneous robots (2023) and a hierarchical deep reinforcement learning framework for multi-robot cooperation under partial observability (2021). He also created GraphWare and ManiWare, easy-to-use middleware platforms that simplify multi-robot and manipulator team coordination. With over 60 citations across his most-cited works, Chen’s contributions are shaping the future of autonomous multi-robot collaboration, from inspection robots to warehouse logistics.
Research Focus
Key Achievements
Top Papers
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- 2Programming Large-Scale Multi-Robot System with Timing Constraints15 citations · 2016
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- 9ManiWare: An Easy-to-Use Middleware for Cooperative Manipulator Teams3 citations · 2023