Search Results - deep learning in robotics and automation

Edge-Deployed Deep Learning for Automated Quality Control in Industrial Assembly: A Case Study in Real-Time Defect Detection

Authors: Ashourpour, Milad Azizpour, Ghazaleh

Source: Procedia CIRP. :735-740

Subject Terms: Assembly Line, Deep Learning, Defect Detection, Robotic Automation, YOLOv8, Computer vision, Cost effectiveness, Deep neural networks, Defects, Error detection, Inspection, Process control, Robotic assembly, Case-studies, Convolutional neural network, Cost-effective solutions, Industrial assemblies, Real- time, Economic and social effects

File Description: print

Access URL: https://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-69346
https://doi.org/10.1016/j.procir.2025.02.180

Fast and Robust Visuomotor Riemannian Flow Matching Policy

Authors: Ding, Haoran Jaquier, Noémie Peters, Jan et al.

Source: IEEE Transactions on robotics. 41:5327-5343

Subject Terms: Deep learning in robotics and automation, learning and adaptive systems, learning from demonstrations, riemannian flow matching, visuomotor policies

File Description: electronic

Access URL: https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-369864
https://kth.diva-portal.org/smash/get/diva2:1998239/FULLTEXT01.pdf

Advancing robotic systems with Ensemble and Modular Deep Learning: research idea and framework.

Authors: Artiushenko, Viktor Reider, Richard Reggelin, Tobias et al.

Source: Procedia Computer Science. 2025, Vol. 253, p2557-2566. 10p.

Subject Terms: Ensemble learning, Artificial intelligence, Modular construction, Robotics, Automation, Deep learning

New solutions to control robotic environments: quality control in food packaging

Authors: Banús Paradell, Núria

Contributors: University/Department: Universitat de Girona. Departament d'Informàtica, Matemàtica Aplicada i Estadística (2013-)

Thesis Advisors: nbanusparadell@gmail.com, Boada, Imma, Xiberta, Pau

Source: TDX (Tesis Doctorals en Xarxa)

Subject Terms: Indústria alimentària, Processament d'aliments, Industria alimentaria, Procesado de alimentos, Food processing industry, Xarxes neuronals convolucionals, Redes neuronales convolucionales, Convolutional neural networks, Automatització de la indústria, Automatización de la industria, Industry automation, Visió per computador, Visión por computador, Machine vision, Intel·ligència artificial, Inteligencia artificial, Artificial intelligence, Computer vision for automation, Aprenentatge profund, Aprendizaje profundo, Deep learning, Control de qualitat d'envasos d'aliments, Control de calidad de envases de alimentos, Packaging quality control

File Description: application/pdf

Access URL: http://hdl.handle.net/10803/673469

Object segmentation dataset generation framework for robotic bin-picking: Multi-metric analysis between results trained with real and synthetic data.

Authors: Cordeiro, Artur Rocha, Luís Freitas Boaventura-Cunha, José et al.

Source: Computers & Industrial Engineering. Jul2025, Vol. 205, pN.PAG-N.PAG. 1p.

Subject Terms: *AUTOMATION, *ROBOTICS, *INDUSTRIAL applications, *EXPERTISE, DEEP learning

Real-Time Robotic Grasp Stability Detection via DynConv-MIL: A Lightweight Deep Learning Approach.

Authors: Wang, Jingnan Qin, Pengjie Wang, Yaling et al.

Source: Procedia Computer Science. 2025, Vol. 271, p114-119. 6p.

Subject Terms: Tactile sensors, Deep learning, Real-time computing, Machine learning, Mechanical efficiency, Industrial robots

Deep Bayesian-Assisted Keypoint Detection for Pose Estimation in Assembly Automation.

Authors: Shi, Debo Rahimpour, Alireza Ghafourian, Amin et al.

Source: Diversity. 23(13)

Subject Terms: AI, assembly automation, convolutional neural networks, deep learning, keypoint detection, manufacturing automation, pose estimation, robot manipulation, robotics, Bayes Theorem, Automation, Machine Learning

File Description: application/pdf

Access URL: https://escholarship.org/uc/item/8ms7w3p1
https://escholarship.org/content/qt8ms7w3p1/qt8ms7w3p1.pdf

Deep Learning Approaches to Grasp Synthesis: A Review

Authors: Newbury, Rhys Gu, Morris Chumbley, Lachlan et al.

Source: IEEE Transactions on robotics. 39(5):3994-4015

Subject Terms: deep learning in robotics and automation, Dexterous manipulation, grasping, perception for grasping and manipulation

File Description: print

Access URL: https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-349565

AI AND ROBOTIC PROCESS AUTOMATION IN FINTECH: ANALYZING THE SHIFT TOWARDS DIGITIZED CUSTOMER SERVICES AND OPERATIONAL EFFICIENCY.

Authors: MAH, Pascal Muam MUZAM, John PEŁECH-PILICHOWSKI, Tomasz et al.

Source: Scientific Papers of Silesian University of Technology. Organization & Management / Zeszyty Naukowe Politechniki Slaskiej. Seria Organizacji i Zarzadzanie. 2023, Issue 187, p399-417. 19p.

Subject Terms: *ARTIFICIAL intelligence, *FINANCIAL technology, *CUSTOMER services, ROBOTIC process automation, COGNITIVE computing, TRANSFORMATIVE learning

Towards Intelligent Automation (IA): Literature Review on the Evolution of Robotic Process Automation (RPA), its Challenges, and Future Trends.

Authors: Siderska, Julia Aunimo, Lili Süße, Thomas et al.

Source: Engineering Management in Production & Services. Dec2023, Vol. 15 Issue 4, p90-103. 14p.

Subject Terms: *ARTIFICIAL intelligence, *NATURAL language processing, DEEP learning

Editorial: Agricultural innovation in the age of climate change: a 4.0 approach

Authors: Angelo Cardellicchio Vito Renò Carmela Rosaria Guadagno et al.

Source: Frontiers in Plant Science, Vol 16 (2025)

Subject Terms: precision agriculture, sustainability, deep learning, artificial intelligence, robotics & automation in agriculture, Plant culture, SB1-1110

File Description: electronic resource

Relation: https://www.frontiersin.org/articles/10.3389/fpls.2025.1715677/full; https://doaj.org/toc/1664-462X

Access URL: https://doaj.org/article/e1812f2bdc9f44008944e8f94d6f0db2

Fused RGB and IR image based deep learning detection of dried laver bugak for robotic automation systems

Authors: Tae Hyong Kim Ah-Na Kim

Source: Scientific Reports, Vol 15, Iss 1, Pp 1-16 (2025)

Subject Terms: Image fusion, Deep learning, YOLO, Robot automation, Dried laver Bugak, Medicine, Science

File Description: electronic resource

Relation: https://doaj.org/toc/2045-2322

Access URL: https://doaj.org/article/cab1b5c752c64e42a5d9adc9ade886f9

Real-Time and Fully Automated Robotic Stacking System with Deep Learning-Based Visual Perception

Authors: Ali Sait Ozer Ilkay Cinar

Source: Sensors, Vol 25, Iss 22, p 6960 (2025)

Subject Terms: computer vision, industrial automation, programmable logic controller integration, real-time object detection, robotic stacking, smart manufacturing, Chemical technology, TP1-1185

File Description: electronic resource

Relation: https://www.mdpi.com/1424-8220/25/22/6960; https://doaj.org/toc/1424-8220

Access URL: https://doaj.org/article/64b45307582b4bdbbcb4a72215c32b11

Targeted weed management of Palmer amaranth using robotics and deep learning (YOLOv7)

Authors: Amlan Balabantaray Shaswati Behera CheeTown Liew et al.

Source: Frontiers in Robotics and AI, Vol 11 (2024)

Subject Terms: weed management, robotics and automation in agriculture, machine vision, artificial intelligence, targeted spraying, real time, Mechanical engineering and machinery, TJ1-1570, Electronic computers. Computer science, QA75.5-76.95

File Description: electronic resource

Relation: https://www.frontiersin.org/articles/10.3389/frobt.2024.1441371/full; https://doaj.org/toc/2296-9144

Access URL: https://doaj.org/article/a61c0585109f42bcaf3fd6b59947fbe1

Integration of Deep Learning Vision Systems in Collaborative Robotics for Real-Time Applications

Authors: Nuno Terras Filipe Pereira António Ramos Silva et al.

Source: Applied Sciences, Vol 15, Iss 3, p 1336 (2025)

Subject Terms: collaborative robots, computer vision, YOLO, deep learning, real-time object detection, industrial automation, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

File Description: electronic resource

Relation: https://www.mdpi.com/2076-3417/15/3/1336; https://doaj.org/toc/2076-3417

Access URL: https://doaj.org/article/b36144d2dc1444e59d146911c7833bfd

A Unification Between Deep-Learning Vision, Compartmental Dynamical Thermodynamics, and Robotic Manipulation for a Circular Economy

Authors: Federico Zocco Wassim M. Haddad Andrea Corti et al.

Source: IEEE Access, Vol 12, Pp 173502-173516 (2024)

Subject Terms: Automation for circularity, circular flow design, circular supply chain, thermodynamical material networks, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

File Description: electronic resource

Relation: https://ieeexplore.ieee.org/document/10746487/; https://doaj.org/toc/2169-3536

Access URL: https://doaj.org/article/baa31399de4e4729a29a0ed53b1f4bcc

ARTIFICIAL INTELLIGENCE IN ROBOTICS AND AUTOMATION FOR MECHANICAL APPLICATIONS: A REVIEW

Authors: Heena Jain Nitin Swarnkar

Deep reinforcement learning hands-on : apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more / Maxim Lapan.

Authors: Lapan, Maxim. Autor Packt Publishing. Wydawca pbl

Contributors: Lapan, Maxim. Autor Packt Publishing. Wydawca pbl

Source: Bibliografia przy rozdziałach. Indeks.

Subject Terms: Deep learning

Other Title: Deep reinforcement learning hands-on

Access URL: http://katalog.nukat.edu.pl/lib/item?id=chamo:5196939&theme=nukat

Integrating machine learning, optical sensors, and robotics for advanced food quality assessment and food processing

Authors: In-Hwan Lee Luyao Ma

Source: Food Innovation and Advances, Vol 4, Iss 1, Pp 65-72 (2025)

Subject Terms: food quality, artificial intelligence, optical sensors, deep learning, robotics, automation, Food processing and manufacture, TP368-456, Biotechnology, TP248.13-248.65

File Description: electronic resource

Relation: https://doaj.org/toc/2836-774X

Access URL: https://doaj.org/article/4be0c6a82f7a4ca88e3b2de3d996f108

The voraus-AD Dataset for Anomaly Detection in Robot Applications

Authors: Brockmann, Jan Thies Rudolph, Marco Rosenhahn, Bodo et al.

Source: IEEE Transactions on robotics. 40:438-451

Subject Terms: Dataset for anomaly detection (AD), deep learning in robotics and automation, failure detection and recovery, probability and statistical models

File Description: print

Access URL: https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-201696