Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)最新文献

Muscle Actuators with Flexures

Biohybrid robots require robust, reproducible, and predictable muscle actuators. Ritu Raman and co-workers have designed compliant mechanisms, termed “flexures”, that enhance the performance and decode the contractile dynamics of biological actuators with unprecedented accuracy and precision (see article number 2300834). The authors’ platform enables user-defined control of muscle contractile dynamics and fatigue behavior, enabling real-world applications of muscle as an actuator for adaptive machines. [Cover art by Ella Marushchenko.]

Multiscale Haptic Interactions

Most wearable haptic devices (top left) deliver haptic notifications to the body; perception of haptics is depicted as brain activity (pink region). In the multiscale paradigm (bottom right), a user actively interacts with the wearable device to perceive a greater amount of information, both via passive receipt of haptic notifications through the band (pink region) and via active exploration of the textile haptic band with fingertips (yellow region). Background letters and numbers represent information transmission to the user via the device. For more information on multiscale haptics, see article number 2300897 by Marcia K. O’Malley and co-workers.

Flexible Sheet-Based Fluidic Diodes

In article number 2300785, Vi T. Vo, Daniel J. Preston, and co-workers introduce a soft fluidic diode made from flexible thermoplastic and textile sheets using a layered fabrication approach. This cover image illustrates the diode as a foundational component in the development of sheet-based fluidic logic systems—as both modular and integrated circuits—to enable electronics-free control of soft devices and robots. Fluidic circuits incorporating the diode provide capabilities including Boolean operations, encoding, and rectification.

Stepwise Control of Multiple Magnetic Millirobots

In article number 2300483, Chia-Yuan Chen and co-workers report an advanced motion control strategy in which multiple magnetic millirobots were controlled externally to exhibit distinct modes of motion independently for coordinative and collaborative behaviors. The presented work can serve as an initial step towards enabling collective intelligence in small-scale robotic systems aiming to realize artificial swarm intelligence technologies for various micromanipulation tasks selectively.

Designing actuators that can modulate power, achieve high energy efficiency, and ensure safe collision remains a challenge, especially for dynamic energy robot systems (DERS) with high-performance requirements. Herein, a novel multi-configuration elastic actuator (MCEA) is proposed based on a controllable planetary differential mechanism (PDM) with one power port from three springs. These springs, positioned between the inner gear ring and the fixed housing shell, are regulated by a single servo motor through a ratchet–pawl mechanism. This setup enables the springs to absorb energy during collisions, reducing impact and subsequently releasing this energy to boost power output. The inner gear ring functions as a controllable one-way rotating element, acting either as an input or output for power. The MCEA's ability to manage power modulation and energy flow is demonstrated through experiments that highlight its potential for safe collision management, energy recycling, and power modulation. Experiment results indicate that the maximum output power of the MCEA in the proposed hybrid elastic actuation (HEA) mode is 8.05 times higher than that in the traditional actuation (TA) mode. A single-legged robot with a four-link mechanism is also built to validate the considerable performance in the application of legged robots, showing considerable adaptability and prospects for DERS.

Soft robots have been advancing rapidly, but their control is still limited by rigid control elements. Soft valves offer a solution to this problem by enabling soft robots to no longer rely on rigid control elements. They have become an emerging research topic in soft robotics. However, with a large number of publications on soft valves, it may be challenging for researchers to quickly grasp the advanced technology related to soft valves. To address this issue, this article summarizes the current state of development in soft valves. The design principles and applications of soft valves in terms of structures and materials are discussed, along with the modeling ideas for soft valves. Finally, the current challenges faced by soft valves are outlined, and potential solutions to these problems are proposed.

Incorporating authentic tactile interactions into virtual environments presents a notable challenge for the emerging development of soft robotic metamaterials. In this study, a vision-based approach is introduced to learning proprioceptive interactions by simultaneously reconstructing the shape and touch of a soft robotic metamaterial (SRM) during physical engagements. The SRM design is optimized to the size of a finger with enhanced adaptability in 3D interactions while incorporating a see-through viewing field inside, which can be visually captured by a miniature camera underneath to provide a rich set of image features for touch digitization. Employing constrained geometric optimization, the proprioceptive process with aggregated multi-handles is modeled. This approach facilitates real-time, precise, and realistic estimations of the finger's mesh deformation within a virtual environment. Herein, a data-driven learning model is also proposed to estimate touch positions, achieving reliable results with impressive R² scores of 0.9681, 0.9415, and 0.9541 along the x, y, and z axes. Furthermore, the robust performance of the proposed methods in touch-based human–cybernetic interfaces and human–robot collaborative grasping is demonstrated. In this study, the door is opened to future applications in touch-based digital twin interactions through vision-based soft proprioception.

This study employs computational algorithms to automatically identify and classify features in X-Ray fluorescence (XRF) microscopy images. Principal component analysis (PCA) and unsupervised machine learning algorithms, such as Gaussian mixture (GM) clustering, are implemented to label features on a collection of XRF maps of human atherosclerotic plaque samples. The investigation involves the hard X-Ray nanoprobe (NanoMAX) at MAX IV synchrotron radiation facility, utilizing scanning transmission X-Ray microscopy (STXM) and XRF techniques. The analysis covers regions of interest scanned by the beam with a step size of 200 nm, yielding XRF maps of elements like calcium, iron, and zinc. These maps reveal intricate structures unsuitable for manual labeling. However, they can be accurately classified in an automated fashion using GM. Prior to clustering, PCA is used to deal with repeated patterns and background areas. The resulting clusters are associated with different types of features, which can be identified as specific tissues confirmed by histology. Regions of high concentrations of phosphorus, sulfur, calcium, and iron are found in the samples. These regions are also observed in the STXM results as spots of low transmission that typically are associated with calcium deposits only.

Creating artificial general intelligence is the solution most often in the spotlight. It is also linked with the possibility—or fear—of machines gaining consciousness. Alternatively, developing domain-specific artificial intelligence is more reliable, energy-efficient, and ethically tractable, and raises mostly a problem of effective coordination between different systems and humans. Herein, it is argued that it will not require machines to be conscious and that simpler ways of sharing awareness are sufficient.

Nanomaterial properties and functionalities are influenced by their shape, size, and chemical composition. The importance of these parameters highlights the need for a statistically robust analysis of a large particle population, necessitating automation. This study introduces a neural network-empowered smart scan technique that achieves a relative increase in speed compared to traditional energy-dispersive X-ray spectroscopy (EDX) mapping. The main advantage is that it reduces the required dose, decreasing potential damage to the sample by avoiding unnecessary exposure. It holds potential use in other multimodal scanning transmission electron microscopy or scanning-based imaging approaches. In the first example, identifying particles in a matrix with a trained neural network reduces the acquisition time by two orders of magnitude. This acceleration enables a statistical compositional analysis of thousands of particles in less than 1 h. Similar improvements are observed for atomic resolution. The discrete positions of atoms identified by the trained network allow for selective EDX sampling at these centers, thereby identifying the atomic species of the column with much-reduced sampling. Consequently, a lower sampling dose is required, enabling mapping of more delicate materials with high lateral resolution and at a high statistical confidence interval. Even though manual training is still required, this approach greatly benefits repetitive quality control tasks.