Adding autonomy to materials science
Shape-memory alloys can alter their shape in response to a change in temperature. This can be thought of as a simple autonomous response, albeit one that is fully ...programmed at the time of fabrication. It is now possible to build materials or combinations of materials that can sense and respond to their local environment, in ways that might also include simple computations and communication. McEvoy and Correll review recent developments in the creation of autonomous materials. They look at how individual abilities are added to a material and the current limitations in the further development of “robotic materials.”
Science
, this issue
10.1126/science.1261689
BACKGROUND
The tight integration of sensing, actuation, and computation that biological systems exhibit to achieve shape and appearance changes (like the cuttlefish and birds in flight), adaptive load support (like the banyan tree), or tactile sensing at very high dynamic range (such as the human skin) has long served as inspiration for engineered systems. Artificial materials with such capabilities could enable airplane wings and vehicles with the ability to adapt their aerodynamic profile or camouflage in the environment, bridges and other civil structures that could detect and repair damages, or robotic skin and prosthetics with the ability to sense touch and subtle textures. The vision for such materials has been articulated repeatedly in science and fiction (“programmable matter”) and periodically has undergone a renaissance with the advent of new enabling technology such as fast digital electronics in the 1970s and microelectromechanical systems in the 1990s.
ADVANCES
Recent advances in manufacturing, combined with the miniaturization of electronics that has culminated in providing the power of a desktop computer of the 1990s on the head of a pin, is enabling a new class of “robotic” materials that transcend classical composite materials in functionality. Whereas state-of-the-art composites are increasingly integrating sensors and actuators at high densities, the availability of cheap and small microprocessors will allow these materials to function autonomously. Yet, this vision requires the tight integration of material science, computer science, and other related disciplines to make fundamental advances in distributed algorithms and manufacturing processes. Advances are currently being made in individual disciplines rather than system integration, which has become increasingly possible in recent years. For example, the composite materials community has made tremendous advances in composites that integrate sensing for nondestructive evaluation, and actuation (for example, for shape-changing airfoils), as well as their manufacturing. At the same time, computer science has created an entire field concerned with distributed algorithms to collect, process, and act upon vast collections of information in the field of sensor networks. Similarly, manufacturing has been revolutionized by advances in three-dimensional (3D) printing, as well as entirely new methods for creating complex structures from unfolding or stretching of patterned 2D composites. Finally, robotics and controls have made advances in controlling robots with multiple actuators, continuum dynamics, and large numbers of distributed sensors. Only a few systems have taken advantage of these advances, however, to create materials that tightly integrate sensing, actuation, computation, and communication in a way that allows them to be mass-produced cheaply and easily.
OUTLOOK
Robotic materials can enable smart composites that autonomously change their shape, stiffness, or physical appearance in a fully programmable way, extending the functionality of classical “smart materials.” If mass-produced economically and available as a commodity, robotic materials have the potential to add unprecedented functionality to everyday objects and surfaces, enabling a vast array of applications ranging from more efficient aircraft and vehicles, to sensorial robotics and prosthetics, to everyday objects like clothing and furniture. Realizing this vision requires not only a new level of interdisciplinary collaboration between the engineering disciplines and the sciences, but also a new model of interdisciplinary education that captures both the disciplinary breadth of robotic materials and the depth of individual disciplines.
(Top) Biological systems that tightly integrate sensing, actuation, computation, and communication and (bottom) the engineering applications that could be enabled by materials that take advantage of similar principles.
(From left) The cuttlefish (camouflage), an eagle’s wings (shape change), the banyan tree (adaptive load support), and human skin (tactile sensing).
CREDITS: CUTTLEFISH: N. HOBGOOD/WIKIMEDIA COMMONS; BALD EAGLE ALASKA: C. CHAPMAN/WIKIMEDIA COMMONS; BANYAN TREE: W. KNIGHT/WIKIMEDIA COMMONS; HUMAN SKIN: A. MCEVOY; MEN IN CAMOUFLAGE HUNTING GEAR: H. RYAN/U.S. FISH AND WILDLIFE SERVICE; 21ST CENTURY AEROSPACE VEHICLE: NASA; SYDNEY HARBOUR BRIDGE: I. BROWN/WIKIMEDIA COMMONS; CYBERHAND: PRENSILIA S.R.L/ PRENSILIA.COM
Tightly integrating sensing, actuation, and computation into composites could enable a new generation of truly smart material systems that can change their appearance and shape autonomously. Applications for such materials include airfoils that change their aerodynamic profile, vehicles with camouflage abilities, bridges that detect and repair damage, or robotic skins and prosthetics with a realistic sense of touch. Although integrating sensors and actuators into composites is becoming increasingly common, the opportunities afforded by embedded computation have only been marginally explored. Here, the key challenge is the gap between the continuous physics of materials and the discrete mathematics of computation. Bridging this gap requires a fundamental understanding of the constituents of such robotic materials and the distributed algorithms and controls that make these structures smart.
Soft material for soft actuators Miriyev, Aslan; Stack, Kenneth; Lipson, Hod
Nature communications,
09/2017, Volume:
8, Issue:
1
Journal Article
Peer reviewed
Open access
Inspired by natural muscle, a key challenge in soft robotics is to develop self-contained electrically driven soft actuators with high strain density. Various characteristics of existing ...technologies, such as the high voltages required to trigger electroactive polymers ( > 1KV), low strain ( < 10%) of shape memory alloys and the need for external compressors and pressure-regulating components for hydraulic or pneumatic fluidicelastomer actuators, limit their practicality for untethered applications. Here we show a single self-contained soft robust composite material that combines the elastic properties of a polymeric matrix and the extreme volume change accompanying liquid-vapor transition. The material combines a high strain (up to 900%) and correspondingly high stress (up to 1.3 MPa) with low density (0.84 g cm
). Along with its extremely low cost (about 3 cent per gram), simplicity of fabrication and environment-friendliness, these properties could enable new kinds of electrically driven entirely soft robots.The development of self-contained electrically driven soft actuators with high strain density is difficult. Here the authors show a single self-contained soft robust composite material that combines the elastic properties of a polymeric matrix and the extreme volume change accompanying liquid vapour transition.
There is ever-increasing interest yet grand challenge in developing programmable untethered soft robotics. Here we address this challenge by applying the asymmetric elastoplasticity of stacked ...graphene assembly (SGA) under tension and compression. We transfer the SGA onto a polyethylene (PE) film, the resulting SGA/PE bilayer exhibits swift morphing behavior in response to the variation of the surrounding temperature. With the applications of patterned SGA and/or localized tempering pretreatment, the initial configurations of such thermal-induced morphing systems can also be programmed as needed, resulting in diverse actuation systems with sophisticated three-dimensional structures. More importantly, unlike the normal bilayer actuators, our SGA/PE bilayer, after a constrained tempering process, will spontaneously curl into a roll, which can achieve rolling locomotion under infrared lighting, yielding an untethered light-driven motor. The asymmetric elastoplasticity of SGA endows the SGA-based bi-materials with great application promise in developing untethered soft robotics with high configurational programmability.
The combination of nonlinear spectrum and convolutional neural network (CNN) is efficient for fault diagnosis of nonlinear system. However, in traditional method, the nonlinear spectrum calculation ...was accomplished by identification algorithm outside the CNN, which reduced the diagnosis efficiency. To solve this problem, a novel CNN with the function of spectrum calculation and fault diagnosis is designed, in which the spectrum calculation network and the fault diagnosis network are connected in series. By extracting the optimized parameters of network, the nonlinear spectrum based on generalized frequency response function (GFRF) is obtained in the former network. Then, the GFRF spectrum is automatically put into the latter network for feature extraction and diagnosis. Hence, after determining the structure of the CNN, only by system input and output, the fault diagnosis can be realized, which avoids the complex process in traditional method. What's more, a new error cost function model is designed to guide the network parameters optimization in the direction of feature classification, which is conductive to improve the diagnosis accuracy. The proposed network model is applied to the heavy-duty industrial robot system, and the best performance is demonstrated by several experiments.
In the contemporary field of optimal trajectory planning for industrial robots, it is customary to construct trajectories through the manual predefinition of interpolation functions. Unfortunately, ...this method frequently overlooks the influence of the interpolation function itself on the optimization objectives, resulting in suboptimal outcomes. To remedy this limitation, an optimal trajectory planning method with coupled interpolation function selection is proposed, in which the total task time and the integral squared jerk are defined as optimization objectives. This method minimizes the optimization objectives while also factoring in the optimal interpolation function, and avoiding subjective interference. To address the aforementioned biobjective optimization problem better, an Improved MultiObjective Golden Eagle Optimizer is introduced. Population diversity and the ability to escape local optima are enhanced through the incorporation of Chaotic Mapping, Opposition‐Based Learning, Differential Evolution, and adaptive inertia weight strategy into the algorithm. The superiority of the algorithm is validated through a series of simulations on 17 benchmark functions. In the context of the robotic stirring operation within the automated block cast charging process, the proposed method is utilized to derive the time–jerk optimal trajectory. The results demonstrate the effectiveness of the proposed method.
Highly stretchable strain sensors based on conducting polymer hydrogel are rapidly emerging as a promising candidate toward diverse wearable skins and sensing devices for soft machines. However, due ...to the intrinsic limitations of low stretchability and large hysteresis, existing strain sensors cannot fully exploit their potential when used in wearable or robotic systems. Here, a conducting polymer hydrogel strain sensor exhibiting both ultimate strain (300%) and negligible hysteresis (<1.5%) is presented. This is achieved through a unique microphase semiseparated network design by compositing poly(3,4‐ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) nanofibers with poly(vinyl alcohol) (PVA) and facile fabrication by combining 3D printing and successive freeze‐thawing. The overall superior performances of the strain sensor including stretchability, linearity, cyclic stability, and robustness against mechanical twisting and pressing are systematically characterized. The integration and application of such strain sensor with electronic skins are further demonstrated to measure various physiological signals, identify hand gestures, enable a soft gripper for objection recognition, and remote control of an industrial robot. This work may offer both promising conducting polymer hydrogels with enhanced sensing functionalities and technical platforms toward stretchable electronic skins and intelligent robotic systems.
A conducting‐polymer hydrogel strain sensor is proposed with both high stretchability (300% strain) and ultralow hysteresis (<1.5%). The hydrogel‐based sensor harnesses a unique microphase semiseparated network to achieve enhanced sensing properties. The fabricated sensor can be applied as electronic skins to monitor physiological signals, enable a soft gripper for object recognition and remote control of an industrial robot.
This work presents the development of a 3-D passive scale tracker (3DPST) for industrial robot pose accuracy detection. A nonlinear optimization error parameter identification approach based on a ...geometric error model is given in order to acquire geometric errors of the instrument precisely and simply. The spatial measurement accuracy of 3DPST has greatly increased after new error parameters adjustment. Additionally, the robot pose errors are measured by 3DPST, and a laser tracker (LT) is employed as a reference tool for simultaneous comparison and verification. Moreover, as a result of the presence of Abbe offsets between the measurement points of the two instruments, orientation errors will affect positioning errors in the comparison procedure. Therefore, it is crucial to apply the Abbe principle while transferring the error values from the LT measurement points to the 3DPST measurement points. According to ISO 9283, the pose accuracy is measured using a cube in the robot workspace. The maximum measurement difference of positioning errors and orientation errors between the two instruments is Formula Omitted and 0.0723°, respectively. This demonstrates that the two instruments’ calibration capabilities are comparable, and 3DPST can complete the pose accuracy calibration of industrial robots.
By leveraging deep learning-based technologies, industrial artificial intelligence (IAI) has been applied to solve various industrial challenging problems in Industry 4.0. However, for privacy ...reasons, traditional centralized training may be unsuitable for sensitive data-driven industrial scenarios, such as healthcare and autopilot. Recently, federated learning has received widespread attention, since it enables participants to collaboratively learn a shared model without revealing their local data. However, studies have shown that, by exploiting the shared parameters adversaries can still compromise industrial applications such as auto-driving navigation systems, medical data in wearable devices, and industrial robots' decision making. In this article, to solve this problem, we propose an efficient and privacy-enhanced federated learning (PEFL) scheme for IAI. Compared with existing solutions, PEFL is noninteractive, and can prevent private data from being leaked even if multiple entities collude with each other. Moreover, extensive experiments with real-world data demonstrate the superiority of PEFL in terms of accuracy and efficiency.
Cyber-physical systems embed software into the physical world. They appear in a wide range of applications such as smart grids, robotics, and intelligent manufacturing. Cyber-physical systems have ...proved resistant to modeling due to their intrinsic complexity arising from the combination of physical and cyber components and the interaction between them. This study proposes a general framework for discovering cyber-physical systems directly from data. The framework involves the identification of physical systems as well as the inference of transition logics. It has been applied successfully to a number of real-world examples. The novel framework seeks to understand the underlying mechanism of cyber-physical systems as well as make predictions concerning their state trajectories based on the discovered models. Such information has been proven essential for the assessment of the performance of cyber-physical systems; it can potentially help debug in the implementation procedure and guide the redesign to achieve the required performance.
Abstract
Aiming at the problem of sensor-less high-precision manipulation, this paper designs and constructs a crankshaft-bearing assembly system based on vision-guided and attractive region in ...environment. A pre-analysis and evaluation of the attitude measurement accuracy method is proposed to describe the trusted region of the current assembly pose through vision. Then its high-dimensional attractive regions of environment constraints is constructed based on the mechanical constraints between the assembly object and the assembly position. Combine the complementary advantages of vision and mechanical information, a robot adjust its pose to a high precision manipulation pose. A platform of an industrial robot with a vision system is built and experiments on the crankshaft and bearing are successfully assembled. It is validated that using the proposed the vision-guided and attraction region in environment system, high precision assembly manipulation is realized.
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