Shouguang Wang

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About

Shouguang Wang is a leading researcher in intelligent automation and multirobot systems, with key contributions spanning multiobjective optimization, maritime robotics, and deadlock control in manufacturing. His work on "Learning-Inspired Immune Algorithm for Multiobjective-Optimized Multirobot Maritime Patrolling" (2023, 14 citations) addresses the critical challenge of patrolling path planning for multiple robots equipped with diverse sensors, modeling it as a multiobjective optimization problem to enhance maritime safety. This innovative approach has garnered attention for its practical implications in autonomous surveillance. In parallel, Wang's research on "Designing Liveness-Enforcing Supervisors for Manufacturing Systems by Using Maximally Good Step Graphs of Petri Nets" (2024, 6 citations) tackles the state-space explosion problem in deadlock control—a fundamental issue in automated manufacturing. By introducing maximally good step graphs (MGSG), a partial-order technique, he provides a more scalable solution for designing liveness-enforcing supervisors. Wang's work bridges theoretical advances in Petri nets and optimization algorithms with real-world applications in robotics and manufacturing, demonstrating significant impact in both academic and industrial contexts. His research continues to shape the development of efficient, intelligent systems for complex operational environments.