Automated Painting: Object Modeling, Trajectory Learning and Adaptation
Gabriele Tiboni (Ph.D. Student)
Spray painting is a task commonly performed by robots in industry. Automating this process offers advantages on the consistency of the results and repeatability over even the most skilled human operator, with the further benefit of limiting human exposure to hazardous environments. The goal of this thesis is to reduce human intervention in the learning process of the robotic arm motion path. The tip of the spray gun should follow the trajectory that produces the highest painting quality with uniform material distribution and minimum wasted paint. We will focus on solving this optimization process decomposing it in three parts: (1) modeling the object to be painted (2) learn the trajectory from pre-defined ground truth paths or imitation via a painting quality based reward (3) adapting the model to new scenarios (from CAD simulators to reality), new objects and new painting modalities (eg protection/esthetic painting, coating).
|Primary Host:||Tatiana Tommasi (Politecnico di Torino)|
|Exchange Host:||Jan Peters (Technical University of Darmstadt & Max Planck Institute for Intelligent Systems)|
|PhD Duration:||01 November 2021 - 31 October 2024|
|Exchange Duration:||01 January 2023 - 01 July 2023 - Ongoing|