Radio Frequency Passive Tagging System Enabling Object Recognition and Alignment by Robotic Hands

Robotic hands require reliable and precise sensing systems to achieve accurate object recognition and manipulation, particularly in environments where vision- or capacitive-based approaches face limitations such as poor lighting, dust, reflective surfaces, or non-metallic materials. This paper presents a novel radiofrequency (RF) pre-touch sensing system that enables robust localization and orientation estimation of objects prior to grasping. The system integrates a compact coplanar waveguide (CPW) probe with fully passive chipless RF resonator tags fabricated using a patented flexible and stretchable conductive ink through additive manufacturing. This approach provides a low-cost, durable, and highly adaptable solution that operates effectively across diverse object geometries and environmental conditions. The experimental results demonstrate that the proposed RF sensor maintains stable performance under varying distances, orientations, and inter-tag spacings, showing robustness where traditional methods may fail. By combining compact design, cost-effectiveness, and reliable near-field sensing independent of an object or lighting, this work establishes RF sensing as a practical and scalable alternative to optical and capacitive systems. The proposed method advances robotic perception by offering enhanced precision, resilience, and integration potential for industrial automation, warehouse handling, and collaborative robotics.

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Publication Details

Journal
Electronics
Published
2025-08-25
DOI
https://doi.org/10.3390/electronics14173381
Citations
1
Primary Topic
Robotics and Automated Systems
Type
article
Field-Weighted Citation Impact
1.21

Funders

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article

Radio Frequency Passive Tagging System Enabling Object Recognition and Alignment by Robotic Hands

Armin Gharibi, André F. Silva, Filippo Costa, Simone Genovesi
1 citations
Electronics
Robotics and Automated Systems
1.21
article

Radio Frequency Passive Tagging System Enabling Object Recognition and Alignment by Robotic Hands

Armin Gharibi, André F. Silva, Filippo Costa, Simone Genovesi
article en
1 citations

Abstract

Robotic hands require reliable and precise sensing systems to achieve accurate object recognition and manipulation, particularly in environments where vision- or capacitive-based approaches face limitations such as poor lighting, dust, reflective surfaces, or non-metallic materials. This paper presents a novel radiofrequency (RF) pre-touch sensing system that enables robust localization and orientation estimation of objects prior to grasping. The system integrates a compact coplanar waveguide (CPW) probe with fully passive chipless RF resonator tags fabricated using a patented flexible and stretchable conductive ink through additive manufacturing. This approach provides a low-cost, durable, and highly adaptable solution that operates effectively across diverse object geometries and environmental conditions. The experimental results demonstrate that the proposed RF sensor maintains stable performance under varying distances, orientations, and inter-tag spacings, showing robustness where traditional methods may fail. By combining compact design, cost-effectiveness, and reliable near-field sensing independent of an object or lighting, this work establishes RF sensing as a practical and scalable alternative to optical and capacitive systems. The proposed method advances robotic perception by offering enhanced precision, resilience, and integration potential for industrial automation, warehouse handling, and collaborative robotics.

ElectronicsVol. 14(17)
University of Pisa (IT), University of Coimbra (PT)
Dipartimenti di Eccellenza, Ministero dell’Istruzione, dell’Università e della Ricerca, Universidade de Coimbra, Regione Toscana
Openalex Percentile: Top 18%
Robotics and Automated Systems
1.21
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Radio Frequency Passive Tagging System Enabling Object Recognition and Alignment by Robotic Hands — Armin Gharibi, André F. Silva, et al. · Electronics (2025) | TGRS Research Map | TGRS