Bilatu
18-tik 1-10 emaitza erakusten
Learning and generalising object extraction skill for contact-rich disassembly tasks: an introductory study
(Springer, 2021)
Remanufacturing automation must be designed to be flexible and robust enough to overcome the uncertainties, conditions of the products, and complexities in the planning and operation of the processes. Machine learning ...
Evaluación de la experiencia de uso de un entorno robótico industrial en realidad virtualEvaluation of the user experience of an industrial robotic environment in virtual reality
(AEIPRO, 2022)
Industry 4.0 is leading to a whole new level of process automation, thus redefining the role of humans, and altering existing jobs in yet unknown ways. Although the number of robots in the manufacturing industry has been ...
A review on reinforcement learning for contact-rich robotic manipulation tasks
(Elsevier, 2023)
Research and application of reinforcement learning in robotics for contact-rich manipulation tasks have exploded in recent years. Its ability to cope with unstructured environments and accomplish hard-to-engineer behaviors ...
Visual Odometry in Challenging Environments: An Urban Underground Railway Scenario Case
(IEEE, 2022)
Localization is one of the most critical tasks for an autonomous vehicle, as position information is required to understand its surroundings and move accordingly. Visual Odometry (VO) has shown promising results in the ...
FlexRQC: Model for a Flexible Robot-Driven Quality Control Station
(Elsevier Ltd., 2020)
As the investment on a dedicated quality control stations is not desirable for limited production batches. In general, those systems result in very optimised systems and the lack of flexibility since they are designed for ...
Novel automated interactive reinforcement learning framework with a constraint-based supervisor for procedural tasks
(Elsevier, 2025)
Learning to perform procedural motion or manipulation tasks in unstructured or uncertain environments poses significant challenges for intelligent agents. Although reinforcement learning algorithms have demonstrated positive ...
Deflectometric data segmentation for surface inspection: a fully convolutional neural network approach
(SPIE, 2020)
The purpose of this paper is to explore the use of fully convolutional neural networks (FCN) to perform a semantic segmentation of deflectometric recordings for quality control of reflective surfaces. The proposed method ...
Monocular visual odometry for underground railway scenarios
(SPIE, 2021)
In this paper, the application of monocular Visual Odometry (VO) solutions for underground train stopping
operation are explored. In order to analyze if the application of monocular VO solutions in challenging environments
as ...
Implementation analysis for a hybrid particle filter on an FPGA based smart camera
(Scitepress, 2010)
Design and development of embedded devices which perform computer vision related task presents many challenges, many of which stem from attempting to fit the complexity of many higher level vision algorithms into the ...
Application of Computer Vision and Deep Learning in the railway domain for autonomous train stop operation
(IEEE, 2020)
The purpose of this paper is to present the results of the analysis of the application of Deep Learning in the railway domain with a particular focus on a train stop operation. The paper proposes an approach consisting of ...
