Khalil Zbiss

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About

Khalil Zbiss is a researcher advancing the frontiers of collaborative industrial robotics, with a primary focus on automating complex manufacturing processes such as car painting. His work addresses critical challenges in multi-robot coordination, trajectory generation, and optimal system configuration. Zbiss’s most impactful contribution, "Automatic Collision-Free Trajectory Generation for Collaborative Robotic Car-Painting" (2022), has garnered 24 citations and tackles the intricate problem of enabling heterogeneous industrial manipulators to work together seamlessly. By leveraging CAD models of vehicles, his algorithm generates safe, efficient paths for multiple robotic arms, ensuring complete paint coverage without collisions. Building on this, his 2024 paper on "Automatic Optimal Robotic Base Placement" (4 citations) solves the foundational problem of where to position robots on the factory floor or ceiling to maximize coverage and efficiency. Together, these works form a comprehensive framework for designing flexible, automated painting cells. Zbiss’s research is pivotal for industries seeking to transition from manual to fully autonomous painting processes, promising significant gains in precision, speed, and safety. His contributions are shaping the future of collaborative manufacturing.