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

Magnetically Docked Ingestible Capsules for Non-Invasive Refilling of Implantable Devices

In article number 2400125, Hind Al-Haddad, Leonardo Ricotti, and co-workers advance implanted drug delivery systems with a non-invasive drug-refilling approach. They employ an ISO 13485-compliant ingestible capsule, two magnetic docking units, and a punching system to ensure safe insulin transfer from the capsule to the implanted reservoir. The digestive tract is exploited as an interface, thus enabling controlled and non-invasive refilling of implanted devices. Validation through ex vivo tests on human tissues, bench tests, and a six-week in vivo study on porcine models confirm the system’s safety and efficacy.

Magnetic Field Generator for Magnetic Particle Imaging

Magnetic particle imaging (MPI) is a tomographic imaging technique that uses different magnetic fields to visualize administered magnetic nanoparticles. The cover image shows a novel magnetic field generator for cerebral MPI that surpasses previous generators in flexibility and energy efficiency. Magnetic field lines can be seen as glowing stripes that penetrate the head and interact with previously administered nanoparticles. This advanced generator enables real-time visualization of blood flow throughout the brain, improving the detection and treatment of stroke and cerebral hemorrhage. Further information can be found in article number 2400017 by Fynn Foerger and co-workers.

High-Performance Textile-Based Capacitive Strain Sensors

In article number 2400292, Alexander Shokurov, Carlo Menon, and co-workers present high-performance textile-based capacitive strain sensors for wearable applications. Through vapor-phase polymerization of pyrrole, enhanced via addition of co-vapor and imidazole, good conductivity and robustness are achieved in a stretchable textile. A new insulation technique using polymer composites provides durability and dielectric coating. Intertwining such fibers together creates a stretchable capacitive sensor. Integrated into a textile glove, sensors precisely capture fine hand motions. A machine learning model classifies 12 gestures with 100% accuracy, showcasing its potential for wearable technology.

A Flexible, Architected Soft Robotic Actuator for Motorized Extensional Motion

In article number 2300866 by Ryan L. Truby and co-workers, a flexible, architected soft robotic actuator is reported that comprises a 3D printed, cylindrical handed shearing auxetic structure and a deformable, internal rubber bellows shaft. The actuator linearly extends upon applying torque from a servo motor, while maintaining high flexibility. The photomontage shows the soft actuator used as a crawling soft robot that can move through tight, tortuous surroundings. The mechanical deformability allows the actuator to passively adapt to its environment.

Conventional continuum robots have outstanding flexibility and dexterity. However, when the robot needs to interact with the environment, the softness may affect the performance of the robot. Especially in transport tasks, the softness of continuum robots can lead to handling failures and drastic drops in precision. The variable stiffness continuum robot combines the advantages of flexibility and rigidity, which is conducive to expanding the application scenarios of flexible continuum robots. This article proposes a flexible continuum robot that simultaneously realizes variable stiffness, shape-aware, and self-heating functions using liquid metal. The low-temperature phase transition property of liquid metal is utilized to realize the variable stiffness function; the overall stiffness of the robot can reach the range of 18.5–183 N m⁻¹, which can realize a tenfold stiffness gain. The conductivity of liquid metal is utilized to develop the shape-aware function, and the monitoring accuracy is within 5%. At the same time, this article utilizes the liquid metal's resistive thermal effect to realize heating function, so that the robot no longer needs heating systems such as heating wires and can realize the phase transition by energizing itself. Based on this design, the robot arm can realize the transition between maximum and minimum stiffness within 240 s.

Reconstructing Soft Robotic Touch via In-Finger Vision

The research by Fang Wan, Chaoyang Song, and co-workers (see article number 2400022) introduces a vision-based approach for learning proprioceptive interactions using Soft Robotic Metamaterials (SRMs). By reconstructing shape and touch during physical engagements, the authors achieve real-time, precise estimations of the soft finger mesh deformation in virtual environments. This innovation enhances the adaptability in 3D interactions and suggests promising applications in human–robot collaboration and touch-based digital twin interactions, bridging the gap between physical and virtual worlds via a multi-modal soft touch.

Cable-Actuated Soft Manipulator Based on Deep Reinforcement Learning

In article number 2400112, Juntian Qu and co-workers propose a type of modified TD3 (twin delayed deep deterministic policy gradient) algorithm in combination with LSTM (long short-term memory) neural networks to control the cable-driven soft manipulator. Multi-scenario and multi-task experiments are carried out based on the soft manipulator, such as precisely placing a 6 mm diameter ball into a 10 mm diameter glass bottle and accurately retrieving a shell from within an L-shaped pipe using the soft manipulator.

Reprogrammable, Recyclable Origami Robots

The research highlighted by this cover focuses on creating innovative paper-based origami robots that transform a simple 2D sheet into complicated 3D shapes using magnetic programming (see article number 2400082). Sang Min Won, Yoonseok Park, and co-workers embed these biodegradable robots with conductive nanoparticles and electrical components, enabling them to monitor environmental conditions and repair complex machinery. The authors believe that these advancements will broaden the use of origami robots in various areas of soft robotics, offering versatile, eco-friendly solutions.

Liquid Metal Chameleon Tongues – Bioinspired Soft Actuators

Article number 2400231 by Hongda Lu, Shi-Yang Tang, Weihua Li, and co-workers presents a bio-inspired liquid metal soft actuator inspired by the predation behavior of chameleons. By delicately modulating its surface tension and phase transition, the actuator achieves reciprocating motion, exhibiting high strain rate, enhanced adhesive force, and reconfigurability. These superior performances highlight a potential in cargo delivery, complex 2D motion, and advanced smart mechatronics.

Imitation learning (IL) facilitates intuitive robotic programming. However, ensuring the reliability of learned behaviors remains a challenge. In the context of reaching motions, a robot should consistently reach its goal, regardless of its initial conditions. To meet this requirement, IL methods often employ specialized function approximators that guarantee this property by construction. Although effective, these approaches come with some limitations: 1) they are typically restricted in the range of motions they can model, resulting in suboptimal IL capabilities, and 2) they require explicit extensions to account for the geometry of motions that consider orientations. To address these challenges, we introduce a novel stability loss function that does not constrain the function approximator's architecture and enables learning policies that yield accurate results. Furthermore, it is not restricted to a specific state space geometry; therefore, it can easily incorporate the geometry of the robot's state space. Proof of the stability properties induced by this loss is provided and the method is empirically validated in various settings. These settings include Euclidean and non-Euclidean state spaces, as well as first-order and second-order motions, both in simulation and with real robots. More details about the experimental results can be found at https://youtu.be/ZWKLGntCI6w.