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About

Musa Jouaneh is a robotics and automation researcher whose career spans over three decades of influential contributions to robot motion planning, sensing systems, and intelligent disassembly. His early landmark work on coordinated motion between robots and positioning tables — published in a two-part series in 1990 and accumulating over 100 combined citations — established foundational strategies for optimizing tool-workpiece coordination in manufacturing processes such as welding, sealing, and laser cutting, resolving kinematic redundancy to maximize workspace efficiency and speed. Jouaneh's more recent research has pivoted toward sustainable manufacturing, focusing on automated robotic disassembly for electronics recycling. His work developing vision-and-force-sensing systems for laptop screw removal (56 citations) and integrating the Soar cognitive architecture to enable adaptive learning across different device models reflects a sophisticated, systems-level approach to robotic intelligence. His 2021 contribution applying deep learning computer vision to fastener detection (45 citations) demonstrates his ability to incorporate cutting-edge AI techniques into practical robotic applications. Beyond his technical contributions, Jouaneh has shown a commitment to engineering education, creating an autonomous mini-golf robot as an engaging demonstration platform for students. Across sensing, motion planning, and machine learning, his work reflects a career dedicated to making robots smarter, more adaptive, and more practically useful.