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

Electroactive Polymer-Based Cardiac Assist Device

E-CAD is an implantable electroactive polymer-based cardiac assist device that supports heart function via biomimetic, nonblood-contacting compression. Wrapped around the heart, it enhances contraction, consumes under 0.3 W, and uses a 0.3 mm driveline to reduce infection and thrombotic risk. Its energy efficiency and driveline design may address limitations of conventional support systems, including bulky power leads and thrombotic risk. More details can be found in article number 10.1002/202500076 by Si-Hyuck Kang, Amy Kyungwon Han, and co-workers.

Treecreeper Drone

Taking inspiration from treecreepers, Haichuan Li, Shane Windsor, and Basaran Bahadir Kocer present in article number 2401101 a passively triggered aerial robot that can reliably perch on vertical tree trunks. The friction-based approach combines a microspine array with a tail-like support, then validated via dynamic analyses and flight experiments, ensuring stable performance across trunk diameters and bark textures, suited for prolonged forest monitoring tasks.

Electrochemical Memory Device

The image representing article number 2500416 by Jiyong Woo and co-workers showcases an ultrathin electrochemical memory device. By introducing an AlO_x liner, the device exhibits highly linear and symmetric current modulation along with fast and robust operation. This combination of features makes it a promising synaptic unit for next-generation neuromorphic computing systems.

Quadrupedal Mobile Manipulator

The image shows a quadrupedal mobile manipulator autonomously opening a door using the proposed method, illustrating its ability to perform complex and coordinated tasks in human environments. A novel trajectory generation and control framework that enhances end-effector tracking accuracy while expanding workspace and motion smoothness. This image also means the proposed method helps the quadrupedal mobile robot to open a new future. More details can be found in article 10.1002/aisy.202500242 by Jiawei Chen and co-workers.

Wave-based mechanisms inspired by traveling wave locomotion in animals have shown great potential use in robots to navigate unstructured environments. Herein, the dual actuator wave-like navigator (DAWN), a multisurface robot employing two actuated helical wave generators to produce continuous traveling waves on flexible link tracks enclosed in elastomer skins, is presented. These skins provide mechanical resilience, enhanced friction, and adaptability on uneven terrain. The robot demonstrates steering and controlled locomotion on flat surfaces, inclines, and declines. To characterize the robot, locomotion tests are performed on plywood, PMMA, and sand, achieving average linear speeds of 16.00, 15.76, and 1.63 mm s⁻¹, respectively. A key innovation is cyclic pneumatic actuation of the skins with actuation frequencies of 0.5 and 0.9 Hz, improving locomotion performance on sand to 2.22 and 2.70 mm s⁻¹. DAWN's capability to move on sand, grass, gravel, and wet soil is also demonstrated. Its modular design enables plug-and-play assembly of components including helical wave generators, flexible link tracks, and elastomer skins, allowing for easy maintenance, modification, and replacements. Potential applications include navigation in complex terrains for search and rescue, inspection, and environmental monitoring.

Traditional speech recognition methods rely on software-based feature extraction that introduces latency and high energy costs, making them unsuitable for low-power devices. A proof-of-concept demonstration is provided of a bioinspired tonotopic sensor for speech recognition that mimics the human cochlea, using a spiral-shaped elastic metamaterial. The measured modal response of the structure at different frequencies generates a spatially distributed signal, providing a spatiotemporal map of the input named “tonogram”. The device acts as an in-sensor physical reservoir computing system, working simultaneously as a sensor and as a computing unit, capable of extracting features of spoken words relevant to speech recognition. Results indicate that this can serve as a valid alternative to traditional software-based digital preprocessing, ensuring high accuracy in terms of classification, while reducing computational requirements. This work demonstrates the potential of bioinspired metamaterials for energy-efficient auditory sensing and, beyond speech recognition, for applications such as IoT devices and edge computing artificial intelligence systems.

Under restricted global positioning access, navigating micro aerial vehicles (MAVs) is particularly challenging. Therefore, the ability to autonomously estimate velocity and position based on onboard sensors becomes critical. While vision or radar-based approaches face limitations for MAVs due to payload constraints, poor lighting, or featureless environments, bio-inspired airflow sensing offers a promising alternative. Airflow interaction with MAVs provides continuous motion cues during flight, enabling airflow odometry—a feasible yet accuracy-limited solution. This article presents a novel skin-like airflow odometry system using distributed flexible thermal anemometers embedded in wingtips, allowing real-time motion estimation where conventional methods fail. Computational fluid dynamics is first employed to analyze the feasibility and sensitivity without changing their aerodynamic profile. A transformer-enhanced gated recurrent unit network then extracts high-precision airflow velocity, while model-based multisensor fusion reduces dead-reckoning drift and is validated through experiments. To the authors’ knowledge, this demonstrates the first airflow odometry system with positional accuracy surpassing previous research for MAV applications.

Biological materials heal, learn, and adapt thanks to the collective work of tiny agents acting at their microscale. Extensive research in robotics has tried to duplicate this scheme in a synthetic system, yet in their current centimeter-scale forms, the constituent robots are too large and too few, especially when compared to their biological inspiration. Here, this study shows a new type of high-stiffness, low-density material made entirely from robots of submillimeter dimensions. To bear load, these hundred-micrometer robots directly grow metal onto their bodies and bond together under the control of on-device microelectronics. The resulting aggregates achieve some of the lowest densities of any material and toughness/elastic moduli approaching the fundamental limits for metallic foams. Going beyond static properties, this study shows that robots can be used to actively repair the material microstructure, restoring stiffness and toughness following compressive fatigue. Broadly, these results clear the way for a new breed of programmable materials with bulk properties that can be rationally tuned over several orders of magnitude through the actions of robots too small to see with the naked eye.

Large-scale deep learning models are increasingly constrained by their immense energy consumption, which limits their scalability and applicability for edge intelligence. In-memory computing (IMC) offers a promising solution by addressing the von Neumann bottleneck inherent in traditional deep learning accelerators, significantly reducing energy consumption. However, the analog nature of IMC introduces hardware nonidealities that degrade model performance and reliability. This article presents a novel approach to directly train physical models of IMC, formulated as ordinary differential equation (ODE)-based physical neural networks (PNNs). To enable the training of large-scale networks, a technique called differentiable spike-time discretization is proposed, which reduces the computational cost of ODE-based PNNs by up to 20 times in speed and 100 times in memory. Such large-scale networks enhance learning performance by exploiting hardware nonidealities on the CIFAR-10 dataset. The proposed bottom-up methodology is validated through post-layout SPICE simulations on the IMC circuit with nonideal characteristics using the sky130 process. The proposed PNN approach reduces the discrepancy between model behavior and circuit dynamics by at least an order of magnitude. This work paves the way for leveraging nonideal physical devices, such as nonvolatile resistive memories, for energy-efficient deep learning applications.

Robotic needle steering plays a critical role in improving the precision and safety of percutaneous interventions across various clinical applications. However, manual needle steering remains challenged by operator-dependent variability, physiological tremor, and limited adaptability to dynamic tissue deformation. To address these limitations, this review examines recent advances in robotic needle steering, structured around three core components: 1) sensing for closed-loop needle steering, 2) modeling of soft tissue deformation and needle deflection, and 3) trajectory planning and closed-loop control strategies. Furthermore, emerging trends are discussed in artificial intelligence-driven autonomy and advanced biocompatible materials, highlighting their potential to enhance steering accuracy and real-time adaptability in future robot-assisted percutaneous procedures.