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

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.

Multimodal communication is a human feature that enables diverse interactions. In human–robot interaction (HRI), robots have to communicate using human skills so that they can seem natural and assist effectively. Most research uses predefined gestures to equip robots with social abilities. However, researchers scarcely consider generating bioinspired involuntary behavior to improve a robot's expressiveness and communication. Human studies revealed that involuntary behavior affects how others perceive communicative intentions. Therefore, mimicking human involuntary behavior may positively affect HRI. This article extends our previous work on equipping robots with involuntary behavior with a user study that evaluates the use of bioinspiration for complementing gestures. A preliminary test is conducted with 15 participants to determine if they can perceive the intensities of the involuntary processes heart rate, pupil size, blink rate, breathing rate, and motor activity. 63 new participants interacted with a robot with bioinspired behaviors or a robot only showing predefined gestures to evaluate the robots’ warmth, competence, and discomfort. The results show that the preliminary test participants differentiated the intensities of the involuntary processes. Participants in the second study find the robot with bioinspired behaviors significantly warmer and more competent than the robot with predefined gestures, with no discomfort difference.

The advent of 3D printing has transformed manufacturing. However, extending the library of materials to improve 3D printing quality remains a challenge. Defects can occur when printing parameters like print speed and temperature are chosen incorrectly. These can cause structural or dimensional issues in the final product. This review investigates closed-loop artificial intelligence-augmented additive manufacturing (AI2AM) technology that integrates AI-based monitoring, automation, and optimization of printing parameters and processes. AI2AM uses AI to improve defect detection and prevention, improving additive manufacturing quality and efficiency. This article explores generic 3D printing processes and issues using existing research and developments. Next, it focuses on fused deposition modeling (FDM) printers and reviews their parameters and issues. The current remedies developed for defect detection and monitoring in FDM 3D printers are presented. Then, the article investigates AI-based 3D printing monitoring, closed-loop feedback systems, and parameter optimization development. Finally, closed-loop 3D printing challenges and future directions are discussed. AI-based systems detect and correct 3D printing failures, enabling current printers to operate within optimal conditions and minimizing the risk of defects or failures, which in turn leads to more sustainable manufacturing with minimum waste and extending the library of materials.

Looming Detection in Complex Dynamic Visual Scenes

A novel fly-inspired neural network unleashes an incredibly enhanced looming detection performance in dynamic environments with converged neuro-dynamics, highlighting how the magic coordination of motion and feature signals helps reduce interference. For further details, see article number 2400198 by Bo Gu, Jianfeng Feng, and Zhuoyi Song.

Soft Underwater Robots

Soft robots hold great significance for underwater operations due to their exceptional compliance and adaptability. In article number 2300688, Qin Fang, Zhefeng Gong, Haojian Lu, and co-workers introduce a transparent reconfigurable soft underwater robot with variable stiffness capability. The cover image shows a soft gripper for delicate underwater grasping and a soft manipulator for exploration in confined underwater environments, highlighting the robot’s potential for future underwater applications.

Tactile Sensing and Grasping Through Thin-Shell Buckling

Soft, hemispherical grippers create new opportunities for blending delicate manipulation with intrinsic tactile sensing. Inspired by deep-sea predatory tunicates, the fluidic hemispherical grippers provide a novel solution to the challenge of universal grasping with a unique sense of touch based on the principles of thin shell buckling. The simple device can passively detect environmental information solely by monitoring its internal fluid pressure, which opens new avenues for designing low-cost soft devices in applications ranging from medical robotics to underwater exploration. For more information, see article number 2300855 by Eleonora Tubaldi and co-workers.

Magneto-Responsive Scaffolds for Biomimetic Investigations

In article number 2400261, Daniele De Pasquale, Edoardo Sinibaldi, Gianni Ciofani, and co-workers present geometrically engineered scaffolds fabricated with magneto-responsive materials, exploited for co-culturing glioma and healthy brain cells. The proposed structures are able to sustain 3D cultures and, thanks to the magnetic properties, allow their remote manipulation.