Optimization of Soft Actuator Geometry and Material Modeling Using Metaheuristic Algorithms

Abstract

The geometry of soft actuators significantly impacts their performance, including force generation, range of motion, and adaptability. Optimizing actuator geometry and material properties under specific constraints is crucial for achieving desired performance. This paper presents an optimization workflow employing metaheuristic algorithms in synergy with SolidWorks and Sorotoki, a newly developed MATLAB toolkit for soft robotics. The workflow optimizes actuator geometry to maximize bending while minimizing actuating pressure. A metaheuristic algorithm iteratively modifies the actuator’s design in SolidWorks, according to finite element analysis conducted using Sorotoki. To ensure accurate simulations, a uniaxial tensile test is performed on Thermoplastic Polyurethane (TPU), with curve fitting based on metaheuristic algorithms for precise hyperelastic modeling. The Ogden and Yeoh models are compared, with results indicating the Ogden model best represents TPU behavior. Four metaheuristic algorithms—Particle Swarm Optimization (PSO), Genetic Algorithm, Simulated Annealing, and Moth Flame Optimization (MFO)—are evaluated. PSO outperforms others in material modeling, while MFO yields the most effective actuator geometry. This workflow enables the design of more efficient and adaptable soft actuators for applications in robotics, prosthetics, and biomedical devices.