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About

Qinxiang Xia is a researcher at the forefront of intelligent robotics and advanced manufacturing, whose work bridges deep reinforcement learning with practical motion control. His primary research areas include trajectory planning for robotic arms, path-following control for mobile robots, and deformation mechanisms in metal forming processes. Xia’s most impactful contribution is a multi-objective optimal trajectory planning method for six-axis robotic arms using deep reinforcement learning, published in 2023 and cited 48 times—his highest-cited work. This study innovatively integrates multiple robot motion characteristics into a reinforcement learning framework, enabling more efficient and adaptive robotic operations. He also developed a novel deep reinforcement learning-based control strategy for nonholonomic wheeled mobile robots, achieving simultaneous path-following and obstacle avoidance, which has garnered 16 citations. More recently, Xia has explored the current-assisted flow spinning process for difficult-to-deform metals, analyzing macroscopic and microscopic deformation mechanisms in cup-shaped parts. His work demonstrates a unique ability to apply cutting-edge AI techniques to real-world engineering challenges, from factory automation to materials processing. With a growing citation record and contributions spanning both theoretical algorithms and practical manufacturing, Xia is establishing himself as a versatile researcher at the intersection of robotics and materials science.