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About

Anders Lager is a researcher focused on advancing the autonomy and reliability of industrial robots in dynamic, human-centric environments. His work centers on task planning, risk-aware decision-making, and the integration of IoT and fog analytics to enable real-time responsiveness. Lager’s key contributions include developing a task modelling formalism that allows mobile robots to autonomously plan and adapt their actions amidst uncertainty, and introducing “Task Roadmaps” to significantly speed up runtime replanning when unexpected events occur. He has also pioneered a risk-aware planning framework for collaborative mobile robots that accounts for uncertain task durations due to human presence, directly addressing the challenge of human-robot collaboration. With over 16 citations across his top papers, Lager’s research is gaining traction for its practical impact on modern manufacturing and logistics. His notable work includes a 2020 study on IoT and fog analytics for industrial robot applications, which explores how edge computing can enhance inferencing speed and connectivity. Lager’s contributions are essential for creating safer, more efficient, and truly adaptive robotic systems in the factories of the future.