Chemically Driven Oscillating Soft Pneumatic Actuation.

Pneumatic actuators are widely studied in soft robotics as they are facile, low cost, scalable, and robust and exhibit compliance similar to many systems found in nature. The challenge is to harness high energy density chemical and biochemical reactions that can generate sufficient pneumatic pressure to actuate soft systems in a controlled and ecologically compatible manner. This investigation evaluates the potential of chemical reactions as both positive and negative pressure sources for use in soft robotic pneumatic actuators. Considering the pneumatic actuation demands, the chemical mechanisms of the pressure sources, and the safety of the system, several gas evolution/consumption reactions are evaluated and compared. Furthermore, the novel coupling of both gas evolution and gas consumption reactions is discussed and evaluated for the design of oscillating systems, driven by the complementary evolution and consumption of carbon dioxide. Control over the speed of gas generation and consumption is achieved by adjusting the initial ratios of feed materials. Coupling the appropriate reactions with pneumatic soft-matter actuators has delivered autonomous cyclic actuation. The reversibility of these systems is demonstrated in a range of displacement experiments, and practical application is shown through a soft gripper that can move, pick up, and let go of objects. Our approach presents a significant step toward more autonomous, versatile soft robots driven by chemo-pneumatic actuators.

A calming hug: Design and validation of a tactile aid to ease anxiety.

Anxiety disorders affect approximately one third of people during their lifetimes and are the ninth leading cause of global disability. Current treatments focus on therapy and pharmacological interventions. However, therapy is costly and pharmacological interventions often have undesirable side-effects. Healthy people also regularly suffer periods of anxiety. Therefore, a non-pharmacological, intuitive, home intervention would be complementary to other treatments and beneficial for non-clinical groups. Existing at-home anxiety aids, such as guided meditations, typically employ visual and/or audio stimuli to guide the user into a calmer state. However, the tactile sense has the potential to be a more natural modality to target in an anxiety-calming device. The tactile domain is relatively under-explored, but we suggest that there are manifold physiological and affective qualities of touch that lend it to the task. In this study we demonstrate that haptic technology can offer an enjoyable, effective and widely accessible alternative for easing state anxiety. We describe a novel huggable haptic interface that pneumatically simulates slow breathing. We discuss the development of this interface through a focus group evaluating five prototypes with embedded behaviours ('breathing', 'purring', 'heartbeat' and 'illumination'). Ratings indicated that the 'breathing' prototype was most pleasant to interact with and participants described this prototype as 'calming' and 'soothing', reminding them of a person breathing. This prototype was developed into an ergonomic huggable cushion containing a pneumatic chamber powered by an external pump allowing the cushion to 'breathe'. A mixed-design experiment (n = 129) inducing anxiety through a group mathematics test found that the device was effective at reducing pre-test anxiety compared to a control (no intervention) condition and that this reduction in anxiety was indistinguishable from that of a guided meditation. Our findings highlight the efficacy of this interface, demonstrating that haptic technologies can be effective at easing anxiety. We suggest that the field should be explored in more depth to capture the nuances of different modalities in relation to specific situations and trait characteristics.

A multizone cerebellar chip for bioinspired adaptive robot control and sensorimotor processing.

The cerebellum is a neural structure essential for learning, which is connected via multiple zones to many different regions of the brain, and is thought to improve human performance in a large range of sensory, motor and even cognitive processing tasks. An intriguing possibility for the control of complex robotic systems would be to develop an artificial cerebellar chip with multiple zones that could be similarly connected to a variety of subsystems to optimize performance. The novel aim of this paper, therefore, is to propose and investigate a multizone cerebellar chip applied to a range of tasks in robot adaptive control and sensorimotor processing. The multizone cerebellar chip was evaluated using a custom robotic platform consisting of an array of tactile sensors driven by dielectric electroactive polymers mounted upon a standard industrial robot arm. The results demonstrate that the performance in each task was improved by the concurrent, stable learning in each cerebellar zone. This paper, therefore, provides the first empirical demonstration that a synthetic, multizone, cerebellar chip could be embodied within existing robotic systems to improve performance in a diverse range of tasks, much like the cerebellum in a biological system.

Light-Triggered Soft Artificial Muscles: Molecular-Level Amplification of Actuation Control Signals.

The principle of control signal amplification is found in all actuation systems, from engineered devices through to the operation of biological muscles. However, current engineering approaches require the use of hard and bulky external switches or valves, incompatible with both the properties of emerging soft artificial muscle technology and those of the bioinspired robotic systems they enable. To address this deficiency a biomimetic molecular-level approach is developed that employs light, with its excellent spatial and temporal control properties, to actuate soft, pH-responsive hydrogel artificial muscles. Although this actuation is triggered by light, it is largely powered by the resulting excitation and runaway chemical reaction of a light-sensitive acid autocatalytic solution in which the actuator is immersed. This process produces actuation strains of up to 45% and a three-fold chemical amplification of the controlling light-trigger, realising a new strategy for the creation of highly functional soft actuating systems.

Cerebellar-inspired algorithm for adaptive control of nonlinear dielectric elastomer-based artificial muscle.

Electroactive polymer actuators are important for soft robotics, but can be difficult to control because of compliance, creep and nonlinearities. Because biological control mechanisms have evolved to deal with such problems, we investigated whether a control scheme based on the cerebellum would be useful for controlling a nonlinear dielectric elastomer actuator, a class of artificial muscle. The cerebellum was represented by the adaptive filter model, and acted in parallel with a brainstem, an approximate inverse plant model. The recurrent connections between the two allowed for direct use of sensory error to adjust motor commands. Accurate tracking of a displacement command in the actuator's nonlinear range was achieved by either semi-linear basis functions in the cerebellar model or semi-linear functions in the brainstem corresponding to recruitment in biological muscle. In addition, allowing transfer of training between cerebellum and brainstem as has been observed in the vestibulo-ocular reflex prevented the steady increase in cerebellar output otherwise required to deal with creep. The extensibility and relative simplicity of the cerebellar-based adaptive-inverse control scheme suggests that it is a plausible candidate for controlling this type of actuator. Moreover, its performance highlights important features of biological control, particularly nonlinear basis functions, recruitment and transfer of training.

Biohybrid Control of General Linear Systems Using the Adaptive Filter Model of Cerebellum.

The adaptive filter model of the cerebellar microcircuit has been successfully applied to biological motor control problems, such as the vestibulo-ocular reflex (VOR), and to sensory processing problems, such as the adaptive cancelation of reafferent noise. It has also been successfully applied to problems in robotics, such as adaptive camera stabilization and sensor noise cancelation. In previous applications to inverse control problems, the algorithm was applied to the velocity control of a plant dominated by viscous and elastic elements. Naive application of the adaptive filter model to the displacement (as opposed to velocity) control of this plant results in unstable learning and control. To be more generally useful in engineering problems, it is essential to remove this restriction to enable the stable control of plants of any order. We address this problem here by developing a biohybrid model reference adaptive control (MRAC) scheme, which stabilizes the control algorithm for strictly proper plants. We evaluate the performance of this novel cerebellar-inspired algorithm with MRAC scheme in the experimental control of a dielectric electroactive polymer, a class of artificial muscle. The results show that the augmented cerebellar algorithm is able to accurately control the displacement response of the artificial muscle. The proposed solution not only greatly extends the practical applicability of the cerebellar-inspired algorithm, but may also shed light on cerebellar involvement in a wider range of biological control tasks.

On the edge: haptic discrimination of edge sharpness.

The increasing ubiquity of haptic displays (e.g., smart phones and tablets) necessitates a better understanding of the perceptual capabilities of the human haptic system. Haptic displays will soon be capable of locally deforming to create simple 3D shapes. This study investigated the sensitivity of our haptic system to a fundamental component of shapes: edges. A novel set of eight high quality shape stimuli with test edges that varied in sharpness were fabricated in a 3D printer. In a two alternative, forced choice task, blindfolded participants were presented with two of these shapes side by side (one the reference, the other selected randomly from the remaining set of seven) and after actively exploring the test edge of each shape with the tip of their index finger, reported which shape had the sharper edge. We used a model selection approach to fit optimal psychometric functions to performance data, and from these obtained just noticeable differences and Weber fractions. In Experiment 1, participants performed the task with four different references. With sharpness defined as the angle at which one surface meets the horizontal plane, the four JNDs closely followed Weber's Law, giving a Weber fraction of 0.11. Comparisons to previously reported Weber fractions from other haptic manipulations (e.g. amplitude of vibration) suggests we are sufficiently sensitive to changes in edge sharpness for this to be of potential utility in the design of future haptic displays. In Experiment 2, two groups of participants performed the task with a single reference but different exploration strategies; one was limited to a single touch, the other unconstrained and free to explore as they wished. As predicted, the JND in the free exploration condition was lower than that in the single touch condition, indicating exploration strategy affects sensitivity to edge sharpness.