Structural displacement compensation of a gigantic manipulator via deep learning

Structures of robotic systems that handle extremely heavy loads undergo static displacement. The ITER blanket remote handling system, which handles 4-ton objects, has displacements of up to 100 mm at the end effector. We propose a novel method that combines deep learning with a physics-based virtual reality system to compensate for displacement. Our deep learning model was trained by using data obtained from both the virtual reality system and physical measurement data of end effector positions. By using a prototype of the ITER blanket remote handling system, we experimentally show that our method successfully reduces the displacement at the end effector to a maximum error of 5.7 mm and a median error of 1.2 mm. We conclude that our approach provides an effective contribution to ensuring the feasibility and safety of the remote maintenance procedures that are to be performed within the ITER project.