A Survey on Autonomous Offline Path Generation for Robot-Assisted Spraying Applications

Author:

Weber Alexander Miguel1ORCID,Gambao Ernesto1ORCID,Brunete Alberto1ORCID

Affiliation:

1. Centre for Automation and Robotics (CAR UPM-CSIC), Universidad Politécnica de Madrid, 28040 Madrid, Spain

Abstract

Robot-assisted spraying is a widespread manufacturing process for coating a multitude of mechanical components in an efficient and cost-effective way. However, process preparation is very time-consuming and relies heavily on the expertise of the robot programmer for generating the appropriate robot trajectory. For this reason, industry and academia investigate the possibility of supporting the end-user in the process by the use of appropriate algorithms. Mostly partial concepts can be found in the literature instead of a solution that solves this task end-to-end. This survey paper provides a summary of previous research in this field, listing the frameworks developed with the intention of fully automating the coating processes. First, the main inputs required for the trajectory calculation are described. The path-generating algorithm and its subprocesses are then classified and compared with alternative approaches. Finally, the required information for the executable output program is described, as well as the validation tools to keep track of program performance. The paper comes to the conclusion that there is a demand for an autonomous robot-assisted spraying system, and with a call-for-action for the implementation of the holistic framework.

Funder

Madrid Robotics Digital Innovation Hub

Programas de Actividades I+D en la Comunidad de Madrid

Structural Funds of the EU

Publisher

MDPI AG

Subject

Control and Optimization,Control and Systems Engineering

Reference59 articles.

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2. Fauchais, P.L., Heberlein, J.V., and Boulos, M.I. (2014). Thermal Spray Fundamentals, Springer.

3. Recognition and Pose Estimation of Auto Parts for an Autonomous Spray Painting Robot;Lin;IEEE Trans. Ind. Inform.,2019

4. Incremental approach for trajectory generation of spray painting robot;Andulkar;Ind. Robot.,2015

5. Posada, J.R., Meissner, A., Hentz, G., and D’Agostino, N. (2020, January 17–18). Machine learning approaches for offline-programming optimization in robotic painting. Proceedings of the 52nd International Symposium on Robotics, ISR 2020, Munich, Germany.

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