Kabsch Marker Estimation Algorithm—A Multi-Robot Marker-Based Localization Algorithm Within the Industry 4.0 Context DOI Creative Commons
João Braun, José Lima, Ana I. Pereira

и другие.

IEEE Access, Год журнала: 2024, Номер 12, С. 68711 - 68729

Опубликована: Янв. 1, 2024

This paper introduces the Kabsch Marker Estimation Algorithm (KMEA), a new, robust multi-marker localization method designed for Autonomous Mobile Robots (AMRs) within Industry 4.0 (I4.0) settings. By integrating Algorithm, our approach significantly enhances robustness by aligning detected fiducial markers with their known positions. Unlike conventional methods that rely on limited subset of visible markers, KMEA uses all available without requiring camera's extrinsic parameters, thereby improving robustness. The algorithm was validated in an I4.0 automated warehouse mockup, four-stage methodology compared to previously established marker estimation reference. On one hand, results have demonstrated KMEA's similar performance standard controlled scenarios, millimetric precision across set error metrics and mean relative (MRE) less than 1%. other KMEA, when faced challenging test scenarios outliers, showed superior baseline algorithm, where it maintained centimetric scale metrics, whereas suffered extreme degradation. emphasized average reduced from 86.9% 92% Parts III IV methodology, respectively. These were achieved using low-cost hardware, indicating possibility even greater accuracy advanced equipment. details algorithm's development, theoretical framework, comparative advantages over existing methods, discusses results, concludes comments regarding its potential industrial commercial applications scalability reliability.

Язык: Английский

A Robotic Framework for the Robot@Factory 4.0 Competition DOI
Ricardo B. Sousa, Cláudia Rocha, João G. Martins

и другие.

Опубликована: Май 2, 2024

Robotic competitions stand as platforms to propel the forefront of robotics research while nurturing STEM education, serving hubs both applied and scientific innovation. In Portugal, Portuguese Robotics Open (FNR) is an event with several robotic competitions, including Robot@Factory 4.0 competition. This competition presents example deploying autonomous robots on a factory shop floor. Although literature has works proposing frameworks for original version competition, none them proposes system framework that hardware, firmware, software complete achieve navigation. paper modular open-access, enabling future participants use improve it in editions. work culmination all knowledge acquired by winning 2022 2023 editions

Язык: Английский

Процитировано

1
Kabsch Marker Estimation Algorithm—A Multi-Robot Marker-Based Localization Algorithm Within the Industry 4.0 Context DOI Creative Commons
João Braun, José Lima, Ana I. Pereira

и другие.

IEEE Access, Год журнала: 2024, Номер 12, С. 68711 - 68729

Опубликована: Янв. 1, 2024

This paper introduces the Kabsch Marker Estimation Algorithm (KMEA), a new, robust multi-marker localization method designed for Autonomous Mobile Robots (AMRs) within Industry 4.0 (I4.0) settings. By integrating Algorithm, our approach significantly enhances robustness by aligning detected fiducial markers with their known positions. Unlike conventional methods that rely on limited subset of visible markers, KMEA uses all available without requiring camera's extrinsic parameters, thereby improving robustness. The algorithm was validated in an I4.0 automated warehouse mockup, four-stage methodology compared to previously established marker estimation reference. On one hand, results have demonstrated KMEA's similar performance standard controlled scenarios, millimetric precision across set error metrics and mean relative (MRE) less than 1%. other KMEA, when faced challenging test scenarios outliers, showed superior baseline algorithm, where it maintained centimetric scale metrics, whereas suffered extreme degradation. emphasized average reduced from 86.9% 92% Parts III IV methodology, respectively. These were achieved using low-cost hardware, indicating possibility even greater accuracy advanced equipment. details algorithm's development, theoretical framework, comparative advantages over existing methods, discusses results, concludes comments regarding its potential industrial commercial applications scalability reliability.

Язык: Английский

Процитировано

0