Smart Robotic Surfaces - Multi-scale Actuation of Topographies for Adaptive Adhesion

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When an octopus holds an object, it both grips (macroscopically) by wrapping its whole tentacle, but also locally adheres with sucker rings, before reversing that adhesion for release. Or consider the fly that uses reversible adhesion of its feet to land, hold and then quickly detach from surfaces. A wide range of organisms have mechanisms to reversibly change interfacial strength of adhesion, through dynamic, local structural change (of feet, skin, claws), for climbing, gripping/release, frictional control and locomotion. There are few examples of engineered materials with switchable adhesion. But any technological system using contact adhesion, such as robots, conveyors, gloves and medical devices, could be radically changed by the ability to reversibly change the interfacial adhesion strength (or friction) of their surfaces. Conveyors in manufacturing could better manipulate objects, Velcro could be switched on/off, switchable foot pads could enable climbing robots, a `smart' glove could actively tune its grip, or a new generation of medical devices could attach and release from soft tissue in surgery. This proposal aims to develop materials with switchable control and dynamic optimization of adhesion strength through the actuation (movement and positioning) of micrometer and millimeter scale surface topographies (microposts). We have developed `dynamic micropost arrays' (DMA) to control mechanical adhesion to surfaces, using air pressure (pneumatics) or water pressure (hydraulics). The pressure change can deform the microposts to make them expand out or retract in and grip surfaces as a result. Our approach enables this micropost movement to be controlled digitally, to make them `robotic surfaces', as their movement and properties can be programmed. We also plan to control them through sensory feedback, to `feel' their successful or failed adhesion to a surface. We aim to better understand mechanisms of mechanical adhesion through this local control. Sensing will allow an adhesive DMA layer to optimize its overall adhesion strength, and actively respond to an adhesive failure (slipping). Machine learning algorithms will be tested to respond to pressure sensing information. This approach to dynamic adhesion has not been demonstrated before. Potential applications of this approach could be very significant for technologies in robotics and medicine, and we will explore its application to skin adhesion in particular.