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Cable-driven soft robots hold significant potential for surgical and industrial applications, yet their performance and maneuverability can be further enhanced through design optimization. By optimizing the design, factors such as bending angles, manipulator deformation, and overall functionality can be directly influenced, leading to improved interaction with the environment and more accurate task performance. This article presents a physics-based design optimization approach for cable-driven soft robotic manipulators, aiming to enhance bending performance through structural design enhancements. Four design criteria, namely, cross-sectional shape, material, gap shape, and gap size, are considered in the optimization process. Given the inherent nonlinearity of soft materials, finite element modeling techniques are employed to analyze the effects of modifying each design parameter on displacement and bending angle. The manipulator’s design is evaluated using ABAQUS/CAE, and an analysis of variance test is conducted to identify significant performance differences among the design parameters. The results reveal that material variation has the most substantial impact, followed by gap shape and gap size. Based on subsequent parameter optimization, Dragon Skin 10 is determined to be the optimal material for bending motion, while a trapezoidal gap shape is preferred. In addition, a genetic algorithm is utilized to select a maximum gap size of 8.87 mm. These findings provide valuable insights into key design principles for cable-driven soft manipulators, aiming to enhance flexibility and reduce actuation forces. By establishing a fundamental understanding of the relationship between morphology and motion capability, this methodology demonstrates an effective simulation-driven optimization approach that incorporates the nonlinear elastic behavior of materials to improve performance. Overall, this work establishes a framework for optimizing cable-driven architectures to suit various applications in the field of soft robotics.
Khalil et al. (Fri,) studied this question.