Tianjiao Song

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About

Tianjiao Song is a researcher specializing in robotics, trajectory optimization, and intelligent optimization algorithms, with a particular focus on improving the efficiency and precision of industrial robotic systems. Song's work addresses critical challenges in industrial automation, including minimizing vibration, reducing cycle times, and enhancing the operational performance of serial manipulators used in manufacturing environments such as CNC machine tools and welding applications. Song's most notable contribution, "Serial Manipulator Time-Jerk Optimal Trajectory Planning Based on Hybrid IWOA-PSO Algorithm" (2022), has garnered 59 citations, demonstrating significant influence in the robotics and automation community. This work introduced a hybrid optimization approach combining an Improved Whale Optimization Algorithm (IWOA) with Particle Swarm Optimization (PSO) to achieve superior time-jerk trajectory planning, directly addressing the dual objectives of speed and smoothness in robotic motion. A complementary study further extended this methodology to six-axis welding robots, employing fifth-order B-spline interpolation for refined path construction. Song's research sits at the intersection of computational intelligence and industrial robotics, offering practical algorithmic solutions that enhance real-world manufacturing productivity. Their contributions are particularly valuable for engineers and researchers seeking to bridge advanced metaheuristic optimization techniques with applied robotic systems.