Yixuan Wang

Anhui Polytechnic University, University of Shanghai for Science and Technology, Northwestern University

Papers

Total Citations

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About

Yixuan Wang is a robotics and autonomous systems researcher whose work spans intelligent path planning, robust robot control, and the safety verification of neural network-enabled systems. His most recognized contribution, a Monte Carlo-based improvement to ant colony optimization for welding robot path planning (2023, 20 citations), addresses a fundamental inefficiency in classical swarm intelligence algorithms — demonstrating how smarter initialization strategies can significantly boost productivity in industrial welding applications. Complementing this, his work on Super-Twisting Nonsingular Terminal Sliding Mode control (2022, 8 citations) advances compliant and robust robot-environment interaction, a critical challenge in contact-rich manipulation tasks. More recently, Wang has turned his attention to the reliability of AI-driven autonomy, investigating runtime safety verification and the principled design of neural network controllers operating under uncertainty (2023–2024). This emerging body of work reflects a broader commitment to ensuring that learned policies meet rigorous safety guarantees before real-world deployment. Across these diverse yet interconnected domains, Wang's research positions him as a thoughtful contributor bridging classical control theory, bio-inspired optimization, and modern machine learning safety — an increasingly vital intersection for the future of intelligent robotics.