Intelligence emerges from complexity

When looking at soft robots, people may immediately see movable rubbery structures and be astounded by such devices, which recall octopus tentacles, earthworms and other organisms. But already at a second sight, some might question the effectiveness of such compliant structures when compared to well-known hard robots, which revolutionized automation and manufacturing industries, and start wondering where the main weaknesses lie. A careful observer may notice that controlling soft robots with the same procedures developed for hard robots is not as clear and immediate as it could seem, since the compliant mechanism at the core of soft actuators is a complex phenomenon, whose science is way more challenging than the one used to describe rigid body mechanisms. Soft robotics motion relies on deformation rather than rotation or translation of connected links, thus the number of degrees of freedom is theoretically infinite. Moreover, the motion needs to be generated by large strain in order to be functional and large strain theories or so-called hyperelastic models, which describe these phenomena, are highly nonlinear. A standard software-operated control scheme that needs to be applied to a soft robot should be able to deal with infinite degrees of freedom and nonlinear equations, not to mention the need of flexible electronics and sensors. The same careful observer at this point should assume that the implementation of software intelligence in soft robotics may not be the ideal solution. How to overcome this obstacle? An extremely successful approach is to transform weaknesses into strengths: if nonlinearities are detrimental to software intelligence, the same nonlinearities are fundamental to create a new paradigm of intelligence based on the peculiar characteristics of the hardware structures and materials. In other words, it is possible, thanks to nonlinearities, to embed control schemes directly coded in the hardware of a soft robot. Recent publications have been proved this concept, where interconnected elastic inflatable actuators are set in motion sequentially using a single fluidic inlet, harnessing particular features emerged by the complex behavior of soft materials. To get an idea of the science behind these phenomena, it is helpful to find a common situation that people can experience. Let’s consider the aforementioned elastic inflatable actuators as an updated version of party balloons: anyone who has tried to inflate a party balloon should have noticed how much more force is required at the beginning compared to the rest of the inflation. This behavior is caused by the nonlinear relationship between the pressure inside the balloon and its volume. Inflatable structures play an important role in soft robots where multiple actuators are used to generate locomotion or to grasp objects. Classically, the scientific community used to look at instabilities as sources of troubles and focus on reducing these nonlinear features, whereas they can be harnessed to boost soft robots performances, by creating hardware-encoded sequencing. As such, our research envisages the nonlinear response of commonly used actuators shapes, while also investigating the impact of the different types of rubber. By connecting multiple inflatable actuators, it is possible to design complex robotic systems that can be controlled using a single fluidic input. So far, several supply tubes are needed to control an inflation sequence, but, with the approach presented here, it will be possible to get the same sequence with only a single input. This advantage, ça va sans dire, is essential for further miniaturization of soft robotics, paving the way to a new class of bio-MEMS and microsurgery tools.

Edoardo Milana


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