Soft robots are intrinsically compliant, which makes them suitable for interaction with delicate objects and living beings. The vast design space and the complex dynamic behavior of the elastic body of the robots make designing them by hand challenging, often requiring a large number of iterations. It is thus advantageous to design soft robots using a computational design approach that integrates simulation feedback. Since locomotion is an essential component in many robotic tasks, this paper presents the computational design synthesis of soft, virtual, locomotion robots. Methods used in previous work give little insight into and control over the computational design synthesis process. The generated solutions are also highly irregular and very different to hand-designed solutions. Also, the problem requirements are solely modeled in the objective function. Here, designs are generated using a spatial grammar with a rule set that is deduced from known locomotion principles. Spatial grammars make it possible to define the type of morphologies that are generated. The aim is to generate gaits based on different locomotion principles, e.g. walking, hopping and crawling. By combining a spatial grammar with simulated annealing, the solution space is searched for locomotive designs. The designs are simulated using a mass-spring model with stable self-collision so that all generated designs can be evaluated. The resulting virtual designs exhibit a large variety of expected and unexpected gaits. The grammar is analyzed to understand the generation process and assess the performance. The main contribution of this research is modeling of some of the results in the spatial grammar rather than the objective function. Thus, the process is guided towards a class of designs with extremities for locomotion, without having to define the class explicitly. Further, the simulation approach is new and results in a stable method that accounts for self-collision.

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