Junbin Wen

Guangdong University of Technology, Rensselaer Polytechnic Institute

Papers

Total Citations

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About

Junbin Wen is a robotics researcher whose work focuses on advancing multi-robot systems and coverage path planning, with a particular emphasis on optimizing efficiency in real-world applications. His most impactful contribution is the development of **TMSTC*** (Turn-minimizing Multirobot Spanning Tree Coverage Star), a novel path planning algorithm introduced in his 2023 paper, which has already garnered **52 citations**. This algorithm addresses a critical challenge in multi-robot coverage—minimizing the number of turns robots make while covering large areas—significantly improving energy efficiency and mission speed for tasks like search-and-rescue, agriculture, and industrial inspection. Wen’s earlier work, including his 2002 study on **simulating cooperating robot manipulators on a mobile platform**, laid foundational insights into the complex dynamics of closed-chain robotic systems, where multiple arms interact with a shared object and a moving base. Though less cited, this research demonstrated his long-standing interest in collaborative robotics and dynamic modeling. Wen’s contributions are particularly notable for bridging theoretical algorithm design with practical, scalable solutions for multi-robot teams, making his work essential reading for students and researchers in autonomous robotics and optimization.