TY - GEN
T1 - SonicFinger
T2 - 2023 IEEE International Conference on Robotics and Automation, ICRA 2023
AU - Rupavatharam, Siddharth
AU - Escobedo, Caleb
AU - Lee, Daewon
AU - Prepscius, Colin
AU - Jackel, Larry
AU - Howard, Richard
AU - Isler, Volkan
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Robot end effectors with proximity detection and contact sensing capabilities can reactively position the gripper to align objects and ensure successful grasps. In this paper, we introduce SonicFinger, an acoustic aura based sensing system capable of full-surface pre-touch and contact sensing. A single piezoelectric transducer embedded within a novel 3D printed finger is excited using a monotone to create an acoustic aura encompassing the finger; this enables pre-touch sensing and gripper alignment, while changes in finger-transducer acoustic coupling indicate contact. SonicFinger is low-cost, compact, and easy to manufacture and assemble. Sensing capabilities are evaluated using a set of objects with various physical properties such as optical reflectivity, dielectric constants, mechanical properties, and acoustic absorption. A dataset with over 8,000 proximity and contact events is collected. Our system shows a pre-touch detection true positive rate (TPR) of 92.4% and a true negative rate (TNR) of 95.3%. Contact detection experiments show a TPR of 93.7% and a TNR of 98.7%. Furthermore, pretouch detection information from Sonic Finger is used to adjust the robot grippers pose to align a target object at the center of both fingers.
AB - Robot end effectors with proximity detection and contact sensing capabilities can reactively position the gripper to align objects and ensure successful grasps. In this paper, we introduce SonicFinger, an acoustic aura based sensing system capable of full-surface pre-touch and contact sensing. A single piezoelectric transducer embedded within a novel 3D printed finger is excited using a monotone to create an acoustic aura encompassing the finger; this enables pre-touch sensing and gripper alignment, while changes in finger-transducer acoustic coupling indicate contact. SonicFinger is low-cost, compact, and easy to manufacture and assemble. Sensing capabilities are evaluated using a set of objects with various physical properties such as optical reflectivity, dielectric constants, mechanical properties, and acoustic absorption. A dataset with over 8,000 proximity and contact events is collected. Our system shows a pre-touch detection true positive rate (TPR) of 92.4% and a true negative rate (TNR) of 95.3%. Contact detection experiments show a TPR of 93.7% and a TNR of 98.7%. Furthermore, pretouch detection information from Sonic Finger is used to adjust the robot grippers pose to align a target object at the center of both fingers.
UR - https://www.scopus.com/pages/publications/85168712694
UR - https://www.scopus.com/inward/citedby.url?scp=85168712694&partnerID=8YFLogxK
U2 - 10.1109/ICRA48891.2023.10161074
DO - 10.1109/ICRA48891.2023.10161074
M3 - Conference contribution
AN - SCOPUS:85168712694
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 12556
EP - 12562
BT - Proceedings - ICRA 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 29 May 2023 through 2 June 2023
ER -