Meng Zhang

Chongqing University of Posts and Telecommunications

Papers

Total Citations

H-Index

About

Meng Zhang is a researcher whose work lies at the intersection of robotics, path planning, and intelligent control systems. Their most notable contribution is the development of an improved Rapidly-exploring Random Tree (RRT) algorithm for robot arm path planning, published in 2021. By fusing the artificial potential field method with the traditional RRT approach, Zhang addressed critical limitations such as high randomness, excessive space complexity, and suboptimal path generation. This hybrid technique has garnered 10 citations, reflecting its practical value in enhancing robotic manipulation efficiency. Zhang’s research is particularly relevant to autonomous systems and industrial automation, where precise and computationally efficient motion planning is essential. Their work demonstrates a keen ability to combine classical algorithms with novel optimization strategies, offering a more reliable solution for real-world robotic applications. As a researcher focused on advancing intelligent robotics, Zhang continues to explore ways to reduce computational overhead while improving path quality, making their contributions a valuable reference for students and engineers working on robotic arm control and autonomous navigation.