Max Robotics — Global Service Robot Directory + US Certification

About

Zhongjie Long is a robotics researcher whose work spans the critical intersection of industrial automation and safe human-robot interaction. His primary research areas include kinematic calibration for industrial manipulators, vision-based robot perception, and mobile robot navigation in dynamic environments. Long has made significant contributions to improving the accuracy of six-degree-of-freedom serial robots, developing hybrid calibration models that minimize linearization errors—a key challenge in industrial settings where rotational errors and long link lengths degrade performance. He also pioneered a vision-based, real-time calibration method using deep learning and dimension-reduced models, enabling non-model-based multitarget pose measurement with monocular cameras. In mobile robotics, Long has advanced safe path planning in human-shared spaces, introducing a multi-policy rapidly-exploring random tree controller and a reinforcement learning-based double-layer controller that handle stochastic human movement. His 2024 and 2025 papers have collectively garnered over 30 citations, with his most cited work on accurate relative measurement of multitarget poses (9 citations) highlighting his impact. Long’s innovative structural design of a multi-directional foot mobile robot further demonstrates his versatility, addressing complex terrain navigation challenges. His work is essential reading for researchers in industrial robotics, autonomous navigation, and human-robot collaboration.