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A 3D Printed Soft Robotic Hand with Embedded Soft Sensors for Direct Transition between Hand Gestures and Improved Grasping Quality and Diversity

Journal Article


Abstract


  • In this study, a three-dimensional (3D) printed soft robotic hand with embedded soft sensors, intended for prosthetic applications is designed and developed to efficiently operate with new-generation myoelectric control systems, e.g., pattern recognition control and simultaneous proportional control. The mechanical structure of the whole hand ('ACES-V2') is fabricated as a monolithic structure using a low-cost and open-source 3D printer. It minimizes the post-processing required for the addition of the embedded sensors in the hand. These are significant benefits for the robotic hand that features low cost, low weight (313 grams), and anthropomorphic appearance. With the soft position sensors added to the fingers, the fingers' positions can be monitored to avoid self-collision of the hand. Besides, it allows a robotic prosthetic hand to eliminate the conventional way of returning to the neutral full open position when switching from one type of gesture to another. This makes the transition between the hand gestures much faster, more efficient, and more intuitive as well. Further, initial contact detection of each finger is achieved for the preshaping of multi-finger grasps, e.g., tripod grip and power grasps, to improve the stability and quality of the grasps. Combinations of different gestures allow the hand to perform multi-stage grasps to seize and carry multiple objects simultaneously. It can potentially augment the hand's dexterity and grasping diversity. Providing direct transition between the hand gestures and improved grasping quality and diversity are the primary contributions of this study.

Publication Date


  • 2022

Citation


  • Zhou, H., Tawk, C., & Alici, G. (2022). A 3D Printed Soft Robotic Hand with Embedded Soft Sensors for Direct Transition between Hand Gestures and Improved Grasping Quality and Diversity. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 30, 550-558. doi:10.1109/TNSRE.2022.3156116

Scopus Eid


  • 2-s2.0-85125714233

Start Page


  • 550

End Page


  • 558

Volume


  • 30

Abstract


  • In this study, a three-dimensional (3D) printed soft robotic hand with embedded soft sensors, intended for prosthetic applications is designed and developed to efficiently operate with new-generation myoelectric control systems, e.g., pattern recognition control and simultaneous proportional control. The mechanical structure of the whole hand ('ACES-V2') is fabricated as a monolithic structure using a low-cost and open-source 3D printer. It minimizes the post-processing required for the addition of the embedded sensors in the hand. These are significant benefits for the robotic hand that features low cost, low weight (313 grams), and anthropomorphic appearance. With the soft position sensors added to the fingers, the fingers' positions can be monitored to avoid self-collision of the hand. Besides, it allows a robotic prosthetic hand to eliminate the conventional way of returning to the neutral full open position when switching from one type of gesture to another. This makes the transition between the hand gestures much faster, more efficient, and more intuitive as well. Further, initial contact detection of each finger is achieved for the preshaping of multi-finger grasps, e.g., tripod grip and power grasps, to improve the stability and quality of the grasps. Combinations of different gestures allow the hand to perform multi-stage grasps to seize and carry multiple objects simultaneously. It can potentially augment the hand's dexterity and grasping diversity. Providing direct transition between the hand gestures and improved grasping quality and diversity are the primary contributions of this study.

Publication Date


  • 2022

Citation


  • Zhou, H., Tawk, C., & Alici, G. (2022). A 3D Printed Soft Robotic Hand with Embedded Soft Sensors for Direct Transition between Hand Gestures and Improved Grasping Quality and Diversity. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 30, 550-558. doi:10.1109/TNSRE.2022.3156116

Scopus Eid


  • 2-s2.0-85125714233

Start Page


  • 550

End Page


  • 558

Volume


  • 30