Special issue on Bioinspired Soft Robotics

Soft robotic snakes promise significant advantages in achieving traveling curvature waves with a reduced number of active segments as well as allowing for safe and adaptive interaction with the environment and human users. However, current soft robot platforms suffer from a lack of accurate theoretical dynamic models and proprioceptive measurements, which impede advancements toward full autonomy. To address this gap, this paper details our recent results on the design, fabrication, and experimental evaluation of a new-generation pressure-operated soft robotic snake platform we call the WPI SRS, which employs custom magnetic sensors embedded in a flexible backbone to continuously monitor the curvature of each of its four bidirectional bending segments. In addition, we present a complete and accurate dynamic undulatory locomotion model that accounts for the propagation of frictional moments to describe linear and rotational motions of the SRS. Experimental studies indicate that on-board sensory measurements provide accurate real-time curvature feedback, and numerical simulations offer a level of abstraction for lateral undulation under ideal conditions.

We present a soft robotic skin that can recognize and localize texture using a distributed set of sensors and computational elements that are inspired by the Pacinian corpuscle, the fast adapting, uniformly spaced mechanoreceptor with a wide receptive field, which is responsive to vibrations due to rubbing or slip on the skin. Tactile sensing and texture recognition is important for controlled manipulation of objects and navigating in one's environment. Yet, providing robotic systems or prosthetic devices with such capability at high density and bandwidth remains challenging. Each sensor node in the presented skin is created by collocating computational elements with individual microphones. These nodes are networked in a lattice and embedded in EcoFlex rubber, forming an amorphous medium. Unlike existing skins consisting of passive sensor arrays that feed into a central computer, our approach allows for detecting, conditioning and processing of tactile signals in-skin, facilitating the use of high-bandwidth signals, such as vibration, and allowing nodes to respond only to signals of interest. Communication between nodes allows the skin to localize the source of a stimulus, such as rubbing or slip, in a decentralized manner. Signal processing by individual nodes allows the skin to estimate the material touched. Combining these two capabilities, the skin is able to convert high-bandwidth, spatiotemporal information into low-bandwidth, event-driven information. Unlike taxel-based sensing arrays, this amorphous approach greatly reduces the computational load on the central robotic system. We describe the design, analysis, construction, instrumentation and programming of the robotic skin. We demonstrate that a 2.8 square meter skin with 10 sensing nodes is capable of localizing stimulus to within 2 centimeters, and that an individual sensing node can identify 15 textures with an accuracy of 71%. Finally, we discuss how such a skin could be used for full-body sensing in existing robots, augment existing sensing modalities, and how this material may be useful in robotic grasping tasks.

The detailed mechanical design of a digital mask projection stereolithgraphy system is described for the 3D printing of soft actuators. A commercially available, photopolymerizable elastomeric material is identified and characterized in its liquid and solid form using rheological and tensile testing. Its capabilities for use in directly printing high degree of freedom (DOF), soft actuators is assessed. An outcome is the ∼40% strain to failure of the printed elastomer structures. Using the resulting material properties, numerical simulations of pleated actuator architectures are analyzed to reduce stress concentration and increase actuation amplitudes. Antagonistic pairs of pleated actuators are then fabricated and tested for four-DOF, tentacle-like motion. These antagonistic pairs are shown to sweep through their full range of motion (∼180°) with a period of less than 70 ms.

Pneumatic artificial muscles (PAMs) are actuators known for their high power to weight ratio, natural compliance and light weight. Due to these advantages, PAMs have been used for orthotic devices and robotic limbs. Small scale PAMs have the same advantages, as well as requiring greatly reduced volumes with potential application to prostheses and small scale robotics. The bladder of a PAM affects common actuator performance metrics, specifically: blocked force, free contraction, hysteresis, and dead-band pressure. This paper investigates the effect that bladder thickness has on static actuation performance of small scale PAMs. Miniature PAMs were fabricated with a range of bladder thicknesses to quantify the change in common actuator performance metrics specifically: blocked force, free contraction, and dead-band pressure. These PAMs were then experimentally characterized in quasi-static conditions, where results showed that increasing bladder wall thickness decreases blocked force and free contraction, while dead-band pressure increases. A nonlinear model was then applied to determine the structure of the stress–strain relationship that enables accurate modeling and the minimum number of terms. Two nonlinear models are compared and the identified parameters are analyzed to study the effect of the bladder thickness on the model.

The ionic polymer–metal composite (IPMC) is a soft material based actuator and sensor and has a promising potential in underwater application. This paper describes a hybrid biomimetic underwater vehicle that uses IPMCs as sensors. Propelled by the energy of waves, this underwater vehicle does not need an additional energy source. A physical model based on the hydrodynamics of the vehicle was developed, and simulations were conducted. Using the Poisson–Nernst–Planck system of equations, a physics model for the IPMC sensor was proposed. For this study, experimental apparatus was developed to conduct hydrodynamic experiments for both the underwater vehicle and the IPMC sensors. By comparing the experimental and theoretical results, the speed of the underwater vehicle and the output of the IPMC sensors were well predicted by the theoretical models. A maximum speed of 1.08 × 10⁻¹ m s⁻¹ was recorded experimentally at a wave frequency of 1.6 Hz. The peak output voltage of the IPMC sensor was 2.27 × 10⁻⁴ V, recorded at 0.8 Hz. It was found that the speed of the underwater vehicle increased as the wave frequency increased and the IPMC output decreased as the wave frequency increased. Further, the energy harvesting capabilities of the underwater vehicle hosting the IPMCs were tested. A maximum power of 9.50 × 10⁻¹⁰ W was recorded at 1.6 Hz.

Flexibility plays an important role in fish behavior by enabling high maneuverability for predator avoidance and swimming in turbulent flow. This paper presents a novel flexible fish robot equipped with distributed pressure sensors for flow sensing. The body of the robot is molded from soft, hyperelastic material, which provides flexibility. Its Joukowski-foil shape is conducive to modeling the fluid analytically. A quasi-steady potential-flow model is adopted for real-time flow estimation, whereas a discrete-time vortex-shedding flow model is used for higher-fidelity simulation. The dynamics for the flexible fish robot yield a reduced model for one-dimensional swimming. A recursive Bayesian filter assimilates pressure measurements to estimate flow speed, angle of attack, and foil camber. The closed-loop speed-control strategy combines an inverse-mapping feedforward controller based on an average model derived for periodic actuation of angle-of-attack and a proportional-integral feedback controller utilizing the estimated flow information. Simulation and experimental results are presented to show the effectiveness of the estimation and control strategy. The paper provides a systematic approach to distributed flow sensing for closed-loop speed control of a flexible fish robot by regulating the flapping amplitude.

This paper presents the stability analysis of the leading edge spar of a flapping wing unmanned air vehicle with a compliant spine inserted in it. The compliant spine is a mechanism that was designed to be flexible during the upstroke and stiff during the downstroke. Inserting a variable stiffness mechanism into the leading edge spar affects its structural stability. The model for the spar–spine system was formulated in terms of the well-known Mathieu's equation, in which the compliant spine was modeled as a torsional spring with a sinusoidal stiffness function. Experimental data was used to validate the model and results show agreement within 11%. The structural stability of the leading edge spar–spine system was determined analytically and graphically using a phase plane plot and Strutt diagrams. Lastly, a torsional viscous damper was added to the leading edge spar–spine model to investigate the effect of damping on stability. Results show that for the un-damped case, the leading edge spar–spine response was stable and bounded; however, there were areas of instability that appear for a range of spine upstroke and downstroke stiffnesses. Results also show that there exist a damping ratio between 0.2 and 0.5, for which the leading edge spar–spine system was stable for all values of spine upstroke and downstroke stiffnesses.

This article serves as an overview of the unique challenges and opportunities made possible by a soft, jellyfish inspired, underwater robot. We include a description of internal pressure modeling as it relates to propulsive performance, leading to a desired energy-minimizing volume flux program. Strategies for determining optimal actuator placement derived from biological body motions are presented. In addition a feedback mechanism inspired by the epidermal line sensory system of cephalopods is presented, whereby internal pressure distribution can be used to determine pertinent deformation parameters.

Robotic fish accomplish swimming by deforming their bodies or other fin-like appendages. As an emerging class of embedded computing system, robotic fish are anticipated to play an important role in environmental monitoring, inspection of underwater structures, tracking of hazardous wastes and oil spills, and the study of live fish behaviors. While integration of flexible materials (into the fins and/or body) holds the promise of improved swimming performance (in terms of both speed and maneuverability) for these robots, such components also introduce significant design challenges due to the complex material mechanics and hydrodynamic interactions. The problem is further exacerbated by the need for the robots to meet multiple objectives (e.g., both speed and energy efficiency). In this paper, we propose an evolutionary multiobjective optimization approach to the design and control of a robotic fish with a flexible caudal fin. Specifically, we use the NSGA-II algorithm to investigate morphological and control parameter values that optimize swimming speed and power usage. Several evolved fin designs are validated experimentally with a small robotic fish, where fins of different stiffness values and sizes are printed with a multi-material 3D printer. Experimental results confirm the effectiveness of the proposed design approach in balancing the two competing objectives.

Robots that can easily interact with humans and move through natural environments are becoming increasingly essential as assistive devices in the home, office and hospital. These machines need to be safe, effective, and easy to control. One strategy towards accomplishing these goals is to build the robots using soft and flexible materials to make them much more approachable and less likely to damage their environment. A major challenge is that comparatively little is known about how best to design, fabricate and control deformable machines. Here we describe the design, fabrication and control of a novel soft robotic platform (Softworms) as a modular device for research, education and public outreach. These robots are inspired by recent neuromechanical studies of crawling and climbing by larval moths and butterflies (Lepidoptera, caterpillars). Unlike most soft robots currently under development, the Softworms do not rely on pneumatic or fluidic actuators but are electrically powered and actuated using either shape-memory alloy microcoils or motor tendons, and they can be modified to accept other muscle-like actuators such as electroactive polymers. The technology is extremely versatile, and different designs can be quickly and cheaply fabricated by casting elastomeric polymers or by direct 3D printing. Softworms can crawl, inch or roll, and they are steerable and even climb steep inclines. Softworms can be made in any shape but here we describe modular and monolithic designs requiring little assembly. These modules can be combined to make multi-limbed devices. We also describe two approaches for controlling such highly deformable structures using either model-free state transition-reward matrices or distributed, mechanically coupled oscillators. In addition to their value as a research platform, these robots can be developed for use in environmental, medical and space applications where cheap, lightweight and shape-changing deformable robots will provide new performance capabilities.