Max Robotics — Global Service Robot Directory + US Certification

About

Junjie Guo is a pioneering researcher in mobile robotics and human-robot interaction, whose work has laid critical groundwork for autonomous navigation and intuitive control systems. His primary research areas include obstacle avoidance algorithms, path tracking for automated guided vehicles (AGVs), and gesture-based human-computer interaction (HCI). Guo’s most influential contribution is his innovative improvement to the artificial potential field (APF) method for mobile robot obstacle avoidance, which addressed the long-standing problems of local traps and oscillatory behavior. By proposing a new force function, his 2007 study (29 citations) provided a more reliable and efficient navigation solution, directly enhancing the safety and autonomy of mobile robots in dynamic environments. He further advanced AGV technology through multi-step predictive control for precise path tracking, demonstrated on the custom-built XAUT.AGV100. In HCI, Guo introduced novel hand shape recognition using artificial neural networks, enabling more intuitive, low-effort robot commands. His recent exploration of reconfigurable kirigami-inspired origami structures (2025) signals a bold expansion into soft robotics and adaptive materials. With a career spanning foundational navigation algorithms to cutting-edge deformable structures, Guo’s work continues to influence both industrial automation and interactive robotics.