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About

Glenn Saunders is a robotics researcher whose work sits at the intersection of industrial automation, advanced manufacturing, and intelligent control systems. His research focuses on enabling industrial robots to perform complex, high-precision manufacturing tasks — from force-controlled surface finishing to metal additive manufacturing — areas where traditional automation has historically struggled. Saunders' most influential contribution, "Robotic Deep Rolling With Iterative Learning Motion and Force Control" (2020, 31 citations), demonstrated how standard industrial robots could be adapted for demanding force-sensitive processes through intelligent learning algorithms. His subsequent work on electroadhesion-based fabric handling (16 citations) tackled one of manufacturing automation's most persistent challenges: reliably grasping soft, deformable materials for garment production. Across his portfolio, Saunders consistently addresses real-world deployment barriers — developing plug-and-play multi-vendor software architectures, sensor-guided assembly systems for large composite structures, and open-source frameworks for Wire Arc Additive Manufacturing (WAAM). His dual-robot motion tracking and multi-robot scan-and-print research further push the boundaries of speed and geometric precision in robotic fabrication. With over 90 cumulative citations and growing contributions to both hardware innovation and software infrastructure, Saunders represents a practical, systems-level approach to making intelligent robotic manufacturing accessible and scalable across industries.