Industrial Robot
Industrial robots are increasingly central to manufacturing, with research focusing on improving their safety, efficiency, and human-robot collaboration. Current efforts concentrate on developing more intuitive and adaptable robot control systems, including human-like kinematic models and real-time adaptive systems that respond to operator physiological signals, often employing machine learning algorithms like Bayesian optimization and deep reinforcement learning for improved performance and safety. These advancements aim to enhance productivity, worker comfort and safety, and the overall integration of robots into diverse industrial settings.
Papers
Uniform vs. Lognormal Kinematics in Robots: Perceptual Preferences for Robotic Movements
Jose J. Quintana, Miguel A. Ferrer, Moises Diaz, Jose J. Feo, Adam Wolniakowski, Konstantsin Miatliuk
Visual Servoing Based on 3D Features: Design and Implementation for Robotic Insertion Tasks
Antonio Rosales, Tapio Heikkilä, Markku Suomalainen