abstract
- Soft origami robots offer unique advantages in flexibility and adaptability. However, their precise inverse kinematics modeling remains a challenge due to highly nonlinear kinematic behavior caused by actuator backlash and material compliance. This work proposes an approach based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) to approximate the inverse kinematics of a Yoshimura origami-based continuum robot. A camera vision system is used to acquire robot motion data, using the pinhole model and Zhang¿s method for camera calibration. Reliable data (x, y end-effector coordinates) for kinematic learning were obtained using the calibrated system. The ANFIS system converged after 150 training epochs and was experimentally validated using quantitative error metrics, including repeatability (0.009-0.190 cm) and accuracy (0.052-0.355 cm), despite nonlinearities associated with TPU-95A hyperelastic behavior. Unlike conventional model-based approaches, the proposed ANFIS learning framework provides an experimentally validated data-driven solution to the inverse kinematics problem of tendon-driven continuum robots and demonstrates robustness within the validated operating region under tendon backlash and material compliance.