🤖 AI Summary
This work addresses the lack of embeddable miniature multi-axis force/torque sensors in tweezer-like tools, which hinders real-time force feedback and failure detection during delicate manipulation. The authors propose PinFT—a compact five-axis capacitive force/torque sensor designed for integration into both tips of a pair of tweezers. Leveraging a three-layer PCB stack, segmented plated-through-hole electrodes, and a silicone dielectric layer, PinFT transduces five-degree-of-freedom loads via 3D displacement of a central stainless-steel pin, with measurement accuracy enhanced through high-order polynomial calibration. Validated on 3D-printed tweezers and a parallel-jaw gripper platform, the system achieves mean absolute errors of 0.23 N in force and 2.5 mN·m in torque (R² > 0.97), successfully enabling tasks such as sub-millimeter component handling, simulated hair plucking, and soft material tearing while reliably detecting slip and ejection failures to significantly enhance micromanipulation perception.
📝 Abstract
We present PinFT, a miniature five-axis capacitive force/torque sensor designed for direct tip-level integration into tweezer-like tools. The sensor employs a compact three-PCB stack with segmented plated through-hole electrodes and a silicone elastomer dielectric, enabling five-degree-of-freedom force and torque sensing ($F_x$, $F_y$, $F_z$, $T_x$, $T_y$) through displacement of a central 2\,mm-diameter stainless steel pin. The fabricated prototype was calibrated using a higher-order polynomial mapping, yielding mean absolute errors of approximately 0.23\,N for forces and 2.5\,mN$\cdot$m for torques, with coefficients of determination ($R^2$) exceeding 0.97 across all axes. To demonstrate practical utility, a 3D-printed tweezer integrating PinFT sensors at both tips was mounted on a parallel-jaw gripper and evaluated across three representative manipulation tasks: grasping a sub-millimeter SMD capacitor, pulling a simulated hair from a silicone substrate, and tearing a compliant silicone specimen. In all cases, per-tip force sensing reliably captured characteristic force signatures that distinguish successful manipulation from failure events -- including slip and object ejection -- using gradient-based features derived from internal grasp force and net interaction force. These results demonstrate that direct, per-tip force sensing enables standard parallel-jaw grippers to monitor and interpret fine manipulation tasks performed through a handheld tweezer.