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Precise grasping is required for robotic hands to perform useful functions. Current robotic grasping is limited by the inability to precisely set grasping conditions before physical contact, often resulting in crushing or slipping of the object. Integrated pretouch sensors that detect object parameters at near-distance are highly useful for addressing this issue. This paper reports the first miniaturized and low-cost optoacoustic (OA) pretouch sensor integrated into the fingertip of a human-sized bionic robotic hand. The OA pretouch sensor performs distance ranging and material/structure classifications based on OA signals excited on the object surface by laser pulses. The sensor-to-object distance is derived from the time delay, and the object material/structure is determined from the frequency spectra using a machine-learning based classifier. The high sensitivity of the OA pretouch sensor allows clean OA signals to be captured from single laser pulses and eliminates the need of signal averaging, allowing data-acquisition in real time during continuous finger motion. The simplified and compact design is cost effective and enables seamless integration of the OA pretouch sensors onto distal portion of a bionic robot finger. Experimental characterization showed a lateral resolution of 0.5 mm and ranging accuracy within 0.3 mm. Machine learning performed with a 100% accuracy in household material/structure classification and 90.4% accuracy in fruit firmness classification. These results confirm that OA pretouch sensors are viable for integration and improving the grasping of robot hands.
Bao et al. (Mon,) studied this question.