Max Robotics — Global Service Robot Directory + US Certification

About

Shiguang Wen’s research lies at the intersection of robotics, computer vision, and human-robot interaction, with a focus on enabling machines to perceive, plan, and move intelligently. His most cited work, “Improvement of Mask-RCNN Object Segmentation Algorithm” (20 citations), advances deep learning for precise visual recognition, a cornerstone for autonomous systems. Wen’s contributions to bipedal locomotion are equally significant: he pioneered gait planning for humanoid robots like Nao using the linear inverted pendulum model (7 citations) and developed neural network-based on-line gait generators (2 citations), both essential for stable, human-like walking. His early work on LEGO-based robotic education (11 citations) demonstrates a commitment to accessible learning tools, while his real-time 3D temperature field reconstruction system (6 citations) showcases applied innovation for search-and-rescue and industrial diagnostics. Wen has also explored amputee-prosthesis coordination (3 citations) and dynamic stiffness estimation from sEMG signals (2 citations), bridging robotics with biomedical engineering. With over 60 total citations across a decade of work, his research consistently addresses practical challenges—from manipulator trajectory optimization (4 citations) to monocular camera-based navigation (3 citations)—making him a versatile contributor to modern robotics and intelligent systems.