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.

Symbolism with better explainability and connectionism with better learning ability are the two basic formalisms of artificial intelligence (AI). Though AI is born symbolic, connectionist AI, represented by deep learning, has recently achieved unprecedented success with the support of various hardware accelerators. As connectionist AI has caused a paradigm shift in diverse fields and its applications are being extended to high-stakes domains, its opacity has become a growing concern, giving rise to a rethinking of the need for integrating symbolic logical reasoning with neural networks, i.e., neuro-symbolic computing. However, the very different mathematical nature of logical reasoning and neural networks, i.e., localist and distributed representations, respectively, has impeded their fusion, which has been exacerbated by the lack of suitable hardware. Here, reservoir computing (RC), a typical connectionist model, is experimentally demonstrated to be implemented symbolically within memristor crossbar arrays (MXAs) as a discrete-time 1D rule-based cellular automata (CA). CA-based RC (CARC) within the MXAs achieves up to 86.5% and 100% accuracy in the image recognition task and 20-bit memory task, respectively. Because of hyperdimensionality, CARC requires fewer training samples and is more robust against errors in the emerging MXA hardware. This work expands the functionality of MXAs for AI applications.

This work presents a novel approach to autonomously sort passive beads in 3D environments using an untethered levitating magnetic microrobot. The in situ untethered magnetic weighing technique is introduced, where the classification of beads is based on the magnetic force required to levitate the microrobot and the beads. Autonomous sorting is achieved through the integration of motion control, trajectory planning, and action scheduling. Experimental validation is conducted using a nine-coil electromagnetic actuation system and a microrobot of 3 mm in size. The system demonstrates an average trajectory-following precision of 0.1 mm. The proposed magnetic weighing enables the detection and classification of carried silica beads of 0.75–1.00 mm diameter by measuring the increase in effective weight with a resolution of ≈1 μN. During the sorting process, all particles with a diameter larger than 0.9 mm are classified as heavy, and all those with a diameter smaller than 0.85 mm are classified as light, demonstrating the effectiveness of the approach. Overall, the proposed system holds significant promise for applications in biomedicine and micromanufacturing, driving innovation in autonomous microrobotics.

Existing transformer-based image captioning methods face two primary limitations: first, they struggle to adequately represent visual features from multiple regions during the encoding phase, and second, the decoder fails to effectively utilize future semantic information during the inference phase. To address these challenges, an attention-enhanced image captioning model is proposed. During the encoding phase, multigranular visual features are integrated by combining cross-attention and self-attention mechanisms, fully utilizing both grid and regional features. Additionally, a novel dense global self-attention module is introduced to enhance model performance with minimal computational cost by fully leveraging the contextual information and fine-grained details of the image. This model is particularly well-suited for biomimetic wearable devices, where real-time visual assistance plays a crucial role in enhancing the user experience. In the decoding phase, a bidirectional decoding structure with an adaptive masking module is designed to dynamically adjust the focus on past and future semantic information, enabling the model to combine historical and future context effectively for generating more accurate and relevant descriptions. Experimental results on the MSCOCO dataset show that the model outperforms the baseline, achieving a 2.1 percentage point improvement in the CIDEr metric. Comprehensive hardware evaluations on the wearable platform demonstrate real-time efficiency with minimal memory footprint, significantly outperforming state-of-the-art models in edge deployment scenarios.

In order to control the posture of soft robots in dynamic environments, the movement of soft actuators such as McKibben artificial muscles must be monitored in real time. Researchers have developed various McKibben artificial muscles with embedded sensing components to achieve such monitoring. However, incorporating sensors into McKibben artificial muscles makes the system configuration more complicated and reduces the actuator's contraction rate. Furthermore, complex processing and circuitry are required, which limits applications. This study introduces a thin McKibben artificial muscle with an embedded resistive stretchable textile sensor (TES-MAM), which does not require specialized measuring instruments. TES-MAM measures displacement without additional sensing components outside the thin McKibben artificial muscle by incorporating a stretchable textile sensor inside the McKibben artificial muscle. The textile sensor exhibits excellent stretchability and does not degrade the contraction ratio. By fully embedding the textile sensor within the McKibben artificial muscle, which has an outer diameter of 4.5 mm, the structure of McKibben artificial muscle remains compact. TES-MAM provides a clear and stable resistance response during repeated pressurization and release cycles under various displacement inputs. Using TES-MAM, a single-degree-of-freedom robotic arm is constructed and its ability to measure joint posture in real time is successfully demonstrated.

A soft wearable modular assistive glove for hand assistance based on miniature foldable pouch motor unit (MFPMU) actuated by positive and negative pressure is proposed. The pouch motor consists of multiple rectangular pouches connected in series, with their centers interlinked and one side edge partially heat-sealed. It can achieve bending while simultaneously elongating, which adapts well to the extension of the skin at finger joints when rotating. Based on the principle of virtual work, the relationship between the output torque and the bending angle is established and experimentally verified. Test results indicate that the MFPMU can attain a maximum output torque of 240 mN m. The bending angle of the bending segment can achieve 62° under 10 kPa. The fingertip force is 2.34 N under 50 kPa for a single finger in the original state. The proposed glove utilizes a modular design, allowing users to replace damaged units within 30 s. The proposed soft wearable modular assistive glove can assist the hand to perform different hand motions and grasp objects with various shapes, dimensions, and weights. Finally, during grasping a wooden block, the soft wearable modular assistive glove demonstrates an average increase of 56% in each finger's fingertip force.