Max Robotics — Global Service Robot Directory + US Certification

About

Dr. Tsung-Han Chang is a leading researcher in autonomous robotics and intelligent manufacturing systems, whose work bridges the gap between theoretical control algorithms and real-world robotic applications. His most influential work, "Measuring the success possibility of implementing advanced manufacturing technology by utilizing the consistent fuzzy preference relations" (2008, 38 citations), established a robust decision-making framework for industrial automation adoption. In mobile robotics, Dr. Chang developed a pioneering obstacle avoidance method combining an improved dynamic window approach with artificial potential fields for HyperOmni Vision-equipped wheeled robots (2019, 21 citations), directly addressing the Federation of International Robot-soccer Association's RoboSot challenge rules. His recent breakthrough in multi-agent path-finding introduces a hybrid centralized training and decentralized execution neural network architecture with deep reinforcement learning (2024, 5 citations), significantly reducing collision risks during physical robot training. Dr. Chang also designed and implemented a fully autonomous service robot integrating human-robot interaction, ROS-based path planning, and cyber-physical systems (2022, 3 citations). His research uniquely combines fuzzy logic, computer vision, and multi-agent reinforcement learning to create safer, more efficient autonomous systems for manufacturing and service environments.