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

The potential application of phosphate semiconductor glass in the interlayer of resistive random access memory (RRAM) is investigated. Glasses based on (50–x)% V₂O₅−50% P₂O₅ are synthesized, which are doped with x% MO (where MO = ZnO, CaO, or Na₂O). X-ray diffraction analysis reveals that the ZnO and CaO series are amorphous, while the Na₂O series is crystalline. Differential scanning calorimetry analysis reveals that the glass transition temperature (T_g) is around 200 °C. X-ray photoelectron spectroscopy analysis reveals that the internal V elements are primarily +4 and +5. Initial electrical measurements indicate that the ZnO series glass exhibits semiconductor electrical properties. Additionally, nanodevices are fabricated and measured to demonstrate the resistive switching characteristics, with conduction mechanisms such as trap-assisted tunneling, space-charge limiting current, or Ohmic conduction. This study demonstrates the potential of phosphate semiconductor glass for application in RRAM and paves the way for the future development of all-glass RRAM components.

The postoperative rehabilitation of ankle fractures, particularly in the home setting, has a crucial influence on the recovery of lower limb function. To enhance the portability, real-time performance, and safety of postoperative remote rehabilitation training, this study proposes a novel robot-assisted remote rehabilitation system tailored for postoperative ankle fracture patients. Based on a distributed system architecture, the hardware system enables modular decomposition and facilitates wireless control of the lower controller. The total weight of the robotic system is 2.634 kg. By combining a deep learning algorithm with an interpolation fitting method, the time delay in interaction force signals during remote communication is predicted and compensated. The control frequency is elevated to 100 Hz with a maximum normalized root mean square error of 10.89%, ensuring the precision and continuity of the robot control system. Additionally, a full-cycle rehabilitation training strategy based on adaptive admittance control with system stiffness identification is proposed, encompassing passive, active–passive, isotonic, and active activities of daily living trainings. Experimental results indicate that the robotic system can execute the training strategies at each phase with high accuracy and safety, and the proposed adaptive control strategy has better compliance than fixed parameter admittance control and fuzzy admittance control methods.

Polyetheretherketone (PEEK) offers mechanical properties suitable for orthopedic and dental implants but experiences surface modification challenges, limited bioactivity, and suboptimal integration with host tissue. In this study, an AI-guided design strategy is presented to predict an optimal surface functionalization recipe for 3D-printed PEEK, enabling tailored material performance. The resulting assembly, termed TRYALPEEK, integrates sequential fused deposition modeling, coating with a sodium alginate hydrogel, and incorporation of L-tryptophan as a model bioactive drug. The biomimetic hydrogel architecture is inspired by the structural organization of periodontal ligament fibers. Comprehensive characterization reveals that hydrogel modification significantly increases surface hydrophilicity, lowers surface roughness and friction coefficient, and enhances cytocompatibility and antibacterial performance against E. coli-green fluorescent protein, while supporting sustained tryptophan release. Finite element analysis further demonstrates favorable stress distribution patterns, suggesting reduced risk of localized stress concentration. Cooperatively, these findings establish TRYALPEEK as a multifunctional implant with improved surface properties and cytocompatibility. While these attributes may contribute to enhanced osseointegration and infection management consistent with prior literature, such biological effects remain to be validated through dedicated in vivo studies.

Laser powder bed fusion (PBF-LB) is an additive manufacturing (AM) technology for producing complex geometry parts. However, the high cost of post-processing coarse as-built surfaces drives the need to control surface roughness during fabrication. Prior studies have evaluated the relationship between process parameters and as-built surface roughness, but they rely on forward models using trial-and-error, regression, and data-driven methods based only on areal surface roughness parameters that neglect spatial surface characteristics. In contrast, this study introduces, for the first time, an inverse data-centric framework that leverages machine learning algorithms and an experimental dataset of Inconel 718 as-built surfaces to predict the PBF-LB process parameters required to achieve a desired as-built roughness. This inverse model shows a prediction accuracy of ≈80%, compared to 90% for the corresponding forward model. Additionally, it incorporates deterministic surface roughness parameters, which capture both height and spatial information, and significantly improves prediction accuracy compared to only using areal parameters. The inverse model provides a digital tool to process engineers that enables control of surface roughness by tailoring process parameters. Hence, it establishes a foundation for integrating surface roughness control into the digital thread of AM, thereby reducing the need for post-processing and improving process efficiency.

Tensegrity Robotics

This research presents tensegrity articulated joints with actuation that combine thin pneumatic artificial muscles and energy-restoring elastics, both integrated into the tensile network. It uses a tensegrity spine-inspired topology, further refined through a multi-objective, constraint-based evolutionary algorithm. The method was validated by designing and fabricating two types of joints, which were tested in a quadruped robot and gripper application. More details can be found in the Research Article by Jan Petrš and co-workers (Doi: 10.1002/aisy.202500310).

Cost of Morphing

Adaptive morphogenesis enables robots to navigate diverse environments with enhanced efficiency. JART, an amphibious quadruped, uses laminar jamming with kirigami-cut layers to switch between load-bearing and hydrodynamic limb configurations. This approach reduces morphing energy by 98.5% compared to thermally driven systems, without compromising terrestrial or aquatic performance. The design advances energy-efficient, shape-morphing robotics for multidomain locomotion. More details can be found in the Research Article by Rebecca Kramer-Bottiglio and co-workers (Doi: 10.1002/aisy.202401055).

The remarkable capability of living organisms to reshape themselves—whether the compliant deformation of musculoskeletal structure, the origami-like folding of a butterfly's wing, or the stiffness modulation of a turtle flipper—continues to inspire breakthroughs in robotic design. Nature reveals that form and function are inherently coupled and adaptability is rooted in intelligent mechanical design, e.g., materials and structures. This special issue of Advanced Intelligent Systems assembles the latest progress in shape-morphing robotics and machines that alter their physical geometry to achieve adaptive properties in response to changing environments, task demands, or unexpected disturbances.

Shape-morphing robots lie at the intersection of materials science, mechanical design, and intelligent control. Several contributions translate biological strategies directly into engineered systems. Khan et al. (doi: 10.1002/aisy.202400620) fabricate 3D-printed magnetic butterflies whose vein-embedded composites replicate monarch wing folding, delivering agile, energy-efficient aerial maneuvers. Ramirez et al. (doi: 10.1002/aisy.202401055) present JART, an amphibious robot that uses kirigami-layer jamming to toggle between flippers and legs, cutting morphing energy by approximately 98% relative to thermally driven designs. Jensen et al. (doi: 10.1002/aisy.202500422) describe DAWN, a dual-helical, wave-propelled crawler whose elastomer skins boost friction and shield internal linkages, letting it traverse sand, gravel, and wet soil. Huang et al. (doi: 10.1002/aisy.202500365) mimic human digits in a wearable pneumatic physiotherapy device that integrates actuation, sensing, and control for on-body, customizable massage. These studies showcase how biomimicry delivers both functional versatility and structural elegance, yet also highlight enduring challenges—continuous materials integration, long-term durability, and tightly coupled actuation–sensing architectures.

A recurring theme in this issue is multi-modal morphologies: the robots will tailor not only shape, but also properties, such as stiffness, for high load-bearing capabilities. In terms of shapes, Chen et al. (doi: 10.1002/aisy.202500123) achieve this through tunable-stiffness architecture morphing structures (TSAMS) that exhibit over 300-fold tunable stiffness range using shape memory alloy actuators. Samarakoon et al. (doi: 10.1002/aisy.202400417) further our understanding of the tiling robots through design, kinematic modeling and control of polyform-inspired robots capable of transforming between two polymorphic shapes, and establishing a taxonomy for scalable morphing tiling robots. Petrš et al. (doi: 10.1002/aisy.202500310) integrate McKibben muscles and elastic cords across tensegrity structures to create distributed, fault-tolerant morphing joints with variable stiffness.