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About

Sirpa Launis is a leading researcher in the field of robotic manipulation, specializing in the control and path planning of heavy-duty, long-reach manipulators for complex industrial environments. Her work focuses on solving critical challenges in real-time obstacle avoidance and self-collision avoidance for redundant robotic systems, as demonstrated in her most-cited papers from 2017, which have collectively garnered 13 citations. Launis has made significant contributions to vision-aided positioning, developing novel methods for tool center point (TCP) pose estimation and camera-to-kinematic model calibration, particularly for mining manipulators operating in unknown environments. Her 2020 paper on redundancy-based visual TCP pose estimation and her 2022 work on probabilistic camera calibration have advanced the precision and reliability of long-reach manipulators, addressing the nonlinearities inherent in non-rigid structures. Her 2023 paper on vision-aided precise positioning using local calibration further enhances positioning accuracy for heavy-duty manipulators. Launis’s research bridges the gap between theoretical robotics and practical applications, offering robust solutions for automation in challenging settings. Her work is essential reading for students and researchers interested in real-time collision avoidance, visual servoing, and calibration techniques for large-scale robotic systems.