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About

Zhijun Yang is a multidisciplinary researcher whose work spans robotics, bio-inspired optimization algorithms, and smart materials engineering. His early contributions focused on human-machine interfaces and autonomous systems, most notably his widely cited 2018 paper on EMG-controlled mobile robotics, which demonstrated how electromyography signals could be harnessed to direct a ROS-based robot platform — a contribution that has earned 38 citations and influenced subsequent work in assistive and rehabilitation robotics. Building on this foundation, Yang has made significant strides in metaheuristic optimization, developing enhanced algorithms such as the orthogonal quasi-opposition-based dynamic salp swarm algorithm (26 citations) and a chaos-based adaptive equilibrium optimizer (2023), both designed to tackle complex global optimization challenges with greater efficiency and reliability. His 2022 work on deep reinforcement learning for robot path planning further underscores his commitment to intelligent autonomous systems. More recently, Yang has pivoted toward advanced soft materials, publishing pioneering research on liquid crystal elastomers with thermally gated covalent adaptability and novel non-radical 3D printing methods — opening new frontiers in soft robotics and artificial muscles. Collectively accumulating over 110 citations, Yang's career reflects a rare breadth of innovation connecting embodied robotics, computational intelligence, and next-generation materials science.