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About

Shang-Wei Lin is a robotics and autonomous systems researcher whose work centers on motion planning, multi-robot coordination, and robust control. His most significant contributions lie in developing distributed and real-time algorithms that enable multiple robots to navigate complex, unstructured environments while avoiding collisions and deadlocks — longstanding challenges in robotics that become exponentially harder as robot swarms scale in size. Lin's most cited work, "A Real-Time and Fully Distributed Approach to Motion Planning for Multirobot Systems" (2017, 49 citations), established a foundational framework for decentralized trajectory generation, a contribution further extended in his 2018 paper on robust distributed control (41 citations) and his 2019 study addressing higher-order deadlocks in multi-robot systems (32 citations). Together, these three works form a cohesive research thread that has meaningfully advanced the field of multi-agent robotics. More recently, Lin has embraced machine learning techniques, applying deep reinforcement learning and causal reasoning to improve robot navigation under adversarial conditions and sensor attacks. His 2023 work bridging discrete-event systems with model predictive control reflects a maturing synthesis of formal methods and continuous control theory. With over 140 cumulative citations, Lin's research offers both theoretical rigor and practical relevance for the next generation of autonomous robotic systems.