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In soft robotics, the most used actuators are soft pneumatic actuators because of their simplicity, cost-effectiveness, and safety. However, pneumatic actuation is also disadvantageous because of the strong non-linearities associated with using a compressible fluid. The identification of analytical models is often complex, and finite element analyses are preferred to evaluate deformation and tension states, which are computationally onerous. Alternatively, artificial intelligence algorithms can be used to follow model-free and data-driven approaches to avoid modeling complexity. In this work, however, the response surface methodology was adopted to identify a predictive model of the bending angle for soft pneumatic joints through geometric and functional parameters. The factorial plan was scheduled based on the design of the experiment, minimizing the number of tests needed and saving materials and time. Finally, a bio-inspired application of the identified model is proposed by designing the soft joints and making an actuator that replicates the movements of the scorpion’s tail in the attack position. The model was validated with two external reinforcements to achieve the same final deformation at different feeding pressures. The average absolute errors between predicted and experimental bending angles for I and II reinforcement allowed the identified model to be verified. |
