Papers
2
Total Citations
133
H-Index
2
About
Runxiang Yu is a leading researcher at the intersection of robotics and artificial intelligence, with a primary focus on motion planning, robot calibration, and deep reinforcement learning (DRL). His most influential work, a 2021 systematic review on DRL-based motion planning for mobile robots, has garnered 131 citations, establishing a foundational resource for researchers navigating unstructured environments. Yu’s review critically synthesizes how DRL enables robots to autonomously adapt to complex, dynamic settings—a key driver for intelligent automation in logistics, manufacturing, and service robotics. In his more recent 2023 study, Yu addresses the critical challenge of industrial robot accuracy, introducing a novel calibration method that combines high-order Hermite polynomials with a sparrow search algorithm-optimized backpropagation (SSA-BP) neural network. This work targets high-stakes applications like aerospace drilling and precision assembly, where absolute positioning accuracy is paramount. By bridging theoretical DRL frameworks with practical calibration solutions, Yu’s contributions advance both the adaptability and precision of robotic systems, making his research essential for students and engineers developing next-generation autonomous robots.
Research Focus
Key Achievements
Top Papers
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