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About

Rickard Karlsson is a researcher specializing in Bayesian state estimation, sensor fusion, and the modeling and control of flexible industrial robots. His work addresses a fundamental challenge in modern robotics: standard industrial robot control systems rely primarily on motor-side measurements, which are insufficient for achieving precise trajectory tracking when structural flexibility introduces significant positional errors at the tool end. Karlsson's major contributions lie in developing recursive Bayesian methods — including particle filters and related probabilistic frameworks — to fuse data from multiple sensors, such as motor encoders and end-effector accelerometers, thereby dramatically improving arm-side position accuracy. His 2012 paper on Bayesian state estimation of a flexible industrial robot stands as his most influential work, accumulating 41 citations and demonstrating the practical viability of probabilistic sensor fusion in demanding industrial environments. His earlier 2005 studies, each garnering around 17–20 citations, laid important groundwork by establishing both the modeling principles and multi-sensor estimation architectures that underpin his later research. Collectively, Karlsson's publications have shaped how researchers and engineers approach the problem of flexible manipulator control, offering principled, probabilistic alternatives to purely model-based approaches and contributing meaningful tools to the robotics and control engineering communities.