Rongjun Zhu

Xi'an University of Science and Technology, Jiangsu Police Officer College

Papers

Total Citations

H-Index

About

Rongjun Zhu’s research focuses on human-like motion planning for robotic systems, particularly anthropomorphic arms, and the integration of embedded processors for real-time health monitoring. His most notable contribution is the development of the Dual Fast Marching Tree (DFMT) algorithm, which combines constrained fast marching tree planning in Cartesian space with human-like fast marching tree planning in self-motion space. This work, published in 2020 with 22 citations, addresses task constraints while enabling more natural, human-like movements for robotic arms, advancing applications in assistive robotics and human-robot interaction. Zhu’s approach bridges computational efficiency and biomechanical realism, offering a novel framework for motion planning under complex constraints. Additionally, his work on real-time monitoring of athlete musculoskeletal health using embedded processors (2 citations) explores the intersection of robotics and sports science, though this paper was later retracted. Despite this, Zhu’s primary impact remains in motion planning, where his DFMT algorithm provides a practical solution for achieving fluid, task-aware robotic motion, contributing to the broader field of autonomous systems and human-centered robotics.