Evolving Soft Locomotion in Aquatic and Terrestrial Environments: Effects of Material Properties and Environmental Transitions

Abstract

Designing soft robots poses considerable challenges; automated design approaches may be particularly appealing in this field, as they promise to optimize complex multimaterial machines with very little or no human intervention. Evolutionary soft robotics is concerned with the application of optimization algorithms inspired by natural evolution to let soft robots (both their morphologies and controllers) spontaneously evolve within physically realistic simulated environments, figuring out how to satisfy a set of objectives defined by human designers. In this article, a powerful evolutionary system is put in place to perform a broad investigation on the free-form evolution of simulated walking and swimming soft robots in different environments. Three sets of experiments are reported, tackling different aspects of the evolution of soft locomotion. The first two explore the effects of different material properties on the evolution of terrestrial and aquatic soft locomotion: particularly, we show how different materials lead to the evolution of different morphologies, behaviors, and energy-performance trade-offs. It is found that within our simplified physics world, stiffer robots evolve more sophisticated and effective gaits and morphologies on land, while softer ones tend to perform better in water. The third set of experiments starts investigating the effect and potential benefits of major environmental transitions (land↔water) during evolution. Results provide interesting morphological exaptation phenomena and point out a potential asymmetry between land→water and water→land transitions: while the first type of transition appears to be detrimental, the second one seems to have some beneficial effects.

Comparing Direct and Indirect Representations for Environment-Specific Robot Component Design

“…Notably Cheney et al [5] evolved CPPNs in their VoxCAD environment and generated life-like organisms in the form of soft robots. Further progression of this work has seen CPPNs and VoxCAD applied to terrestrial, aquatic and tight space environments as well as the evolutionary development of plants [25]- [28]. We look to remove CPPN evolved robots from simulated environments and allow for instantiation in the real world whilst also increasing the size and resolution of the design space.…”

How To Help People Float

“…Before novices even enter the pool hall, autonomic sensors can deliver subconscious warnings of potential dangers ahead, through sound, odours, or appearance of 'dirt.' With submergence in water the energy demand for homeostatic regulation increases through loss of heat conductance and increased resistance to movement (Corucci, Cheney, Giorgio-Serchi, Bongard, & Laschi, 2018;Childress, 1981). This cuts the amount of available energy left over for independent thoughts and self-directed actions and explains why immersion is often followed by an increase in appetite to replace the extra energy lost from an elevated rate of metabolism (Matsui, Ishikawa, Ito, Okamoto, Inoue, Lee, Fujikawa, Ichitani, Kawanaka & Soya 2012).…”

Design, Modeling and Testing of a Flagellum-inspired Soft Underwater Propeller Exploiting Passive Elasticity

“…Among the numerous themes in which soft technologies have quickly branched, underwater robotics stands out [7], [8], on one hand, for its added complexity [9], [10] and, on the other hand, for offering an especially suitable context where to test these new kind of machines [11], [12]. Indeed, the close correspondence between the density of the aquatic medium and that of common rubberlike materials of which most soft robots are made of enables underwater soft systems to operate unconstrained by the need for a supportive rigid structure, as it occurs in terrestrial environments [13]. This has positive implications on the broadness of the design space, which has stimulated the development of a vast number of soft bioinspired underwater robots [14], [15], [16], [17].…”