Design and Evaluation of a Soft Robotic Actuator with Non-Intrusive Vision-Based Bending Measurement

This paper presents the design and evaluation of a novel soft pneumatic actuator featuring two independent bending chambers, enabling independent joint actuation and localization for rehabilitation purposes. The actuator's dual-chamber configuration provides flexibility for applications requiring customized bending profiles. To measure the bending angle of the finger joints in real time, a camera-based system is employed, utilizing a deep learning detection model to localize the joints and estimate their bending angles. This approach provides a non-intrusive, sensor-free alternative to hardware-based measurement methods, reducing complexity and wiring typically associated with wearable devices. Experimental results demonstrate the effectiveness of the proposed actuator in achieving bending angles of 105 degrees for the metacarpophalangeal (MCP) joint and 95 degrees for the proximal interphalangeal (PIP) joint, as well as a gripping force of 9.3 N. The vision system also captures bending angles with a precision of 98%, indicating potential applications in fields such as rehabilitation and human-robot interaction.