Soft robotic manipulators, unlike their rigid-linked counterparts, deform continuously along their lengths similar to elephant trunks and octopus arms. Their excellent dexterity enables them to navigate through unstructured and cluttered environments and handle fragile objects using whole arm manipulation. Soft robotic manipulator design involves the specification of air muscle actuators and the number, length and configuration of sections that maximize dexterity and load capacity for a given maximum actuation pressure. This paper uses nonlinear models of the actuators and arm structure to optimally design soft robotic manipulators. The manipulator model is based on Cosserat rod theory, accounts for large curvatures, extensions, and shear strains, and is coupled to nonlinear Mooney-Rivlin actuator model. Given a dexterity constraint for each section, a genetic algorithm-based optimizer maximizes the arm load capacity by varying the actuator and section dimensions. The method generates design rules that simplify the optimization process. These rules are then applied to the design of pneumatically and hydraulically actuated soft robotic manipulators, using 100 psi and 1000 psi maximum pressure, respectively.

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