Haptic-based touch detection for collaborative robots in welding applications

Tannous, Michael; Miraglia, Marco; Inglese, Francesco; Giorgini, Loris; Ricciardi, Filippo; Pelliccia, Riccardo; Milazzo, Mario; Stefanini, Cesare

doi:10.1016/j.rcim.2020.101952

Cited by 35 publications

(11 citation statements)

References 16 publications

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“…Vision and distance sensors were made widely popular by game console manufacturers since distance and object detection were key elements for the player's interaction with their games (Han et al, 2013). Touch (Tannous et al, 2020) is focused on muscle-like force detection and skin-like sensing; they both make it possible for robots to adjust their actions based on the input in the mission to avoid harm. Audition (Nakadai and Okuno, 2020) allows robots to detect/locate workers based on sound and opens possibilities for communication.…”

Section: Relying On Datamentioning

confidence: 99%

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Transferring artificial intelligence practices between collaborative robotics and autonomous driving

Zorman

Žlahtič

Stradovnik

et al. 2022

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PurposeCollaborative robotics and autonomous driving are fairly new disciplines, still with a long way to go to achieve goals, set by the research community, manufacturers and users. For technologies like collaborative robotics and autonomous driving, which focus on closing the gap between humans and machines, the physical, psychological and emotional needs of human individuals becoming increasingly important in order to ensure effective and safe human–machine interaction. The authors' goal was to conceptualize ways to combine experience from both fields and transfer artificial intelligence knowledge from one to another. By identifying transferable meta-knowledge, the authors will increase quality of artificial intelligence applications and raise safety and contextual awareness for users and environment in both fields.Design/methodology/approachFirst, the authors presented autonomous driving and collaborative robotics and autonomous driving and collaborative robotics' connection to artificial intelligence. The authors continued with advantages and challenges of both fields and identified potential topics for transferrable practices. Topics were divided into three time slots according to expected research timeline.FindingsThe identified research opportunities seem manageable in the presented timeline. The authors' expectation was that autonomous driving and collaborative robotics will start moving closer in the following years and even merging in some areas like driverless and humanless transport and logistics.Originality/valueThe authors' findings confirm the latest trends in autonomous driving and collaborative robotics and expand them into new research and collaboration opportunities for the next few years. The authors' research proposal focuses on those that should have the most positive impact to safety, complement, optimize and evolve human capabilities and increase productivity in line with social expectations. Transferring meta-knowledge between fields will increase progress and, in some cases, cut some shortcuts in achieving the aforementioned goals.

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Section: Relying On Datamentioning

confidence: 99%

“…, 2013). Touch (Tannous et al. , 2020) is focused on muscle-like force detection and skin-like sensing; they both make it possible for robots to adjust their actions based on the input in the mission to avoid harm.…”

Section: Introductionmentioning

confidence: 99%

Transferring artificial intelligence practices between collaborative robotics and autonomous driving

Zorman

Žlahtič

Stradovnik

et al. 2022

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“…Industrial AI presents a tremendous opportunity to empower human-robot collaboration and provide support to current human-centric tasks on the shop-floor, be it by improving the welfare and safety of operators or by making their tasks easier and more efficient. Therefore, current opportunities for the application of Industrial AI in this domain include workforce training and task support [20], [23], collaborative robotics [19], [21] and ergonomics [34], [74].…”

Section: ) Human-robot Collaboration and Ergonomicsmentioning

confidence: 99%

Industrial Artificial Intelligence in Industry 4.0 - Systematic Review, Challenges and Outlook

et al. 2020

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“…Traditionally, vision-based systems exploits fiducial markers as references for robotics applications such as augmented reality (AR) [8,11], computer vision applications [12][13][14][15], human-robot interaction [16,17], and real-time systems like Structure from Motion (SM) and Simultaneous Localization and Mapping (SLAM) [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

MechaTag: A Mechanical Fiducial Marker and the Detection Algorithm

Digiacomo

Bologna

Inglese

et al. 2021

J Intell Robot Syst

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Fiducial markers are fundamental components of many computer vision systems that help, through their unique features (e.g., shape, color), a fast localization of spatial objects in unstructured scenarios. They find applications in many scientific and industrial fields, such as augmented reality, human-robot interaction, and robot navigation. In order to overcome the limitations of traditional paper-printed fiducial markers (i.e. deformability of the paper surface, incompatibility with industrial and harsh environments, complexity of the shape to reproduce directly on the piece), we aim at exploiting existing, or additionally fabricated, structural features on rigid bodies (e.g., holes), developing a fiducial mechanical marker system called MechaTag. Our system, endowed with a dedicated algorithm, is able to minimize recognition errors and to improve repeatability also in case of ill boundary conditions (e.g., partial illumination). We assess MechaTag in a pilot study, achieving a robustness of fiducial marker recognition above 95% in different environment conditions and position configurations. The pilot study was conducted by guiding a robotic platform in different poses in order to experiment with a wide range of working conditions. Our results make MechaTag a reliable fiducial marker system for a wide range of robotic applications in harsh industrial environments without losing accuracy of recognition due to the shape and material.

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Haptic-based touch detection for collaborative robots in welding applications

Cited by 35 publications

References 16 publications

Transferring artificial intelligence practices between collaborative robotics and autonomous driving

Transferring artificial intelligence practices between collaborative robotics and autonomous driving

Industrial Artificial Intelligence in Industry 4.0 - Systematic Review, Challenges and Outlook

MechaTag: A Mechanical Fiducial Marker and the Detection Algorithm

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