Dielectric elastomer (DE) actuators are popularly referred to as artificial muscles because their impressive actuation strain and speed, low density, compliant nature, and silent operation capture many of the desirable physical properties of muscle. Unlike conventional robots and machines, whose mechanisms and drive systems rapidly become very complex as the number of degrees of freedom increases, groups of DE artificial muscles have the potential to generate rich motions combining many translational and rotational degrees of freedom. These artificial muscle systems can mimic the agonist-antagonist approach found in nature, so that active expansion of one artificial muscle is taken up by passive contraction in the other. They can also vary their stiffness. In addition, they have the ability to produce electricity from movement. But departing from the high stiffness paradigm of electromagnetic motors and gearboxes leads to new control challenges, and for soft machines to be truly dexterous like their biological analogues, they need precise control. Humans control their limbs using sensory feedback from strain sensitive cells embedded in muscle. In DE actuators, deformation is inextricably linked to changes in electrical parameters that include capacitance and resistance, so the state of strain can be inferred by sensing these changes, enabling the closed loop control that is critical for a soft machine. But the increased information processing required for a soft machine can impose a substantial burden on a central controller. The natural solution is to distribute control within the mechanism itself. The octopus arm is an example of a soft actuator with a virtually infinite number of degrees of freedom (DOF). The arm utilizes neural ganglia to process sensory data at the local “arm” level and perform complex tasks. Recent advances in soft electronics such as the piezoresistive dielectric elastomer switch (DES) have the potential to be fully integrated with actuators and sensors. With the DE switch, we can produce logic gates, oscillators, and a memory element, the building blocks for a soft computer, thus bringing us closer to emulating smart living structures like the octopus arm. The goal of future research is to develop fully soft machines that exploit smart actuation networks to gain capabilities formerly reserved to nature, and open new vistas in mechanical engineering.

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Self sensing Dielectric Elastomer Actuator (DEA) artificial muscles will enable the creation of soft, lightweight robots with animal-like capabilities. We demonstrate a fast, accurate, and economic self sensing algorithm that enables an arbitrary voltage oscillation to be used to sense DEA capacitance during actuation in a manner that is robust to significant changes in electrode resistance and leakage current. Not only we can use this algorithm to emulate the proprioceptive feedback found in natural muscle but also we can use it for the online characterisation and analysis of DEA behavior.

Because of their large output strain, dielectric elastomer actuators (DEAs) have been proposed for tunable optics applications such as tunable gratings. However, the inherent viscoelastic drift of these actuators is an important drawback and closed-loop operation of DEAs is a prerequisite for any accurate real-world application. In this paper, we show how capacitive self-sensing can be used to drive a DEA in closed-loop without the need for any external sensor. The method has been demonstrated on a DEA tunable grating based on a VHB acrylic and silicone membrane. The results show that the widely used VHB presents a time-dependent drift between the capacitance of the electrodes and their strain. The silicone-based grating does not exhibit such a drift, and its strain can be stabilized by regulating the capacitance of the device to a constant value. We also report on an new fabrication method for thin deformable gratings based on replication on a water-soluble master and a 27% change in the grating period has been obtained on a VHB-based device. Abstract. Because of their large output strain, dielectric elastomer actuators (DEAs) have been proposed for tunable optics applications such as tunable gratings. However, the inherent viscoelastic drift of these actuators is an important drawback and closed-loop operation of DEAs is a prerequisite for any accurate real-world application. In this paper, we show how capacitive self-sensing can be used to drive a DEA in closed-loop without the need for any external sensor. The method has been demonstrated on a DEA tunable grating based on a VHB acrylic and silicone membrane. The results show that the widely used VHB presents a time-dependent drift between the capacitance of the electrodes and their strain. The silicone-based grating does not exhibit such a drift, and its strain can be stabilized by regulating the capacitance of the device to a constant value. We also report on an new fabrication method for thin deformable gratings based on replication on a water-soluble master and 27% change in the grating period has been obtained on a VHB-based device. Reference

Dielectric breakdown often leads to catastrophic failure in Dielectric Elastomer Actuator(s) (DEA). The resultant damage to the dielectric membrane renders the DEA useless for future actuation, and in extreme cases the sudden discharge of energy during breakdown can present a serious fire risk. The breakdown strength of DEA however is heavily dependent on the presence of microscopic defects in the membrane giving its overall breakdown strength inherent variability. The practical consequence is that DEA normally have to be operated far below their maximum performance in order to achieve consistent reliability.Predicting when DEA are about to suffer breakdown based on feedback will enable significant increase in effective DEA performance without sacrificing reliability. It has been previously suggested that changes in the leakage current can be a harbinger of dielectric breakdown; leakage current exhibits a sharp increase during breakdown. In this paper the relationship between electric field and leakage current is investigated for simple VHB4905-based DEA. Particular emphasis is placed on the behaviour of leakage current leading up to and during breakdown conditions. For a sample size of nine expanding dot DEA, the DEA that failed at electric fields below the maximum tested exhibited noticeably higher nominal power dissipation and a higher frequency of partial discharge events than the DEA that did not breakdown during testing. This effect could easily be seen at electric fields well below that at which the worst performing DEA failed.

We present a soft, bearing-free artificial muscle motor that cannot only turn a shaft but also grip and reposition it through a flexible gear. The bearing-free operation provides a foundation for low complexity soft machines, with multiple degree-of-freedom actuation, that can act simultaneously as motors and manipulators. The mechanism also enables an artificial muscle controlled gear change. Future work will include self-sensing feedback for precision, multidegree-of-freedom operation.

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Electrostatic motors—first used by Benjamin Franklin to rotisserie a turkey—are making a comeback in the form of high energy density dielectric elastomer artificial muscles. We present a self-commutated artificial muscle motor that uses dielectric elastomer switches in the place of bulky external electronics. The motor simply requires a DC input voltage to rotate a shaft (0.73 Nm/kg, 0.24 Hz) and is a step away from hard metallic electromagnetic motors towards a soft, light, and printable future.