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

Head pose estimation plays a crucial role in various applications, including human–machine interaction, autonomous driving systems, and 3D reconstruction. Current methods address the problem primarily from a 2D perspective, which limits the efficient utilization of 3D information. Herein, a novel approach, called pose orientation-aware network (POANet), which leverages normal maps for orientation information embedding, providing abundant and robust head pose information, is introduced. POANet incorporates the axial signal perception module and the rotation matrix perception module, these lightweight modules make the approach achieve state-of-the-art (SOTA) performance with few computational costs. This method can directly analyze various topological 3D data without extensive preprocessing. For depth images, POANet outperforms existing methods on the Biwi Kinect head pose dataset, reducing the mean absolute error (MAE) by ≈30% compared to the SOTA methods. POANet is the first method to perform rigid head registration in a landmark-free manner. It also incorporates few-shot learning capabilities and achieves an MAE of about $��1�°�$ on the Headspace dataset. These features make POANet a superior alternative to traditional generalized Procrustes analysis for mesh data processing, offering enhanced convenience for human phenotype studies.

Optical encryption is pivotal in information security, offering parallel processing, speed, and robust security. The simplicity and compatibility of speckle-based cryptosystems have garnered considerable attention. Yet, the predictable statistical distribution of speckle optical fields’ characteristics can invite statistical attacks, undermining these encryption methods. The proposed solution, a deep adversarial learning-based speckle modulation network (DeepSLM), disrupts the strong intercorrelation of speckle grains. Utilizing the unique encoding properties of speckle patterns, DeepSLM facilitates license editing within the modulation phase, pioneering a layered authentication encryption system. Our empirical studies confirm DeepSLM's superior performance on key metrics. Notably, the testing dataset reveals an average Pearson correlation coefficient above 0.97 between decrypted images and their original counterparts for intricate subjects like human faces, attesting to the method's high fidelity. This innovation marries adjustable modification, optical encryption, and deep learning to enforce tiered data access control, charting new paths for creating user-specific access protocols.

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.

Space-wall-climbing robots face the challenge of stably attaching to and moving on spacecraft surfaces, which include smooth flat areas and rough intricate surfaces. Although adhesion-based wall-climbing robots demonstrate stable climbing on smooth surfaces in outer space, there is scarce research on their stable adhesion on rough surfaces within a microgravity environment. A novel adhesive material is developed inspired by the adhesion mechanism and locomotion of the Gekko gecko. This material exhibits exceptional adhesion across various materials and surface roughness. A variable-stiffness gecko-inspired paw is engineered, generating substantial adhesion forces while minimizing detachment forces. Impressively, this paw generates up to 180 N of adhesion force on smooth surfaces and achieves detachment without external forces. By integrating such variable-stiffness paws with a wall-climbing robot, a gecko-inspired robot effectively operating in a microgravity environment is created. The robotic satellite surface climbing experiments and robotic satellite capture experiments are conducted using a simulated microgravity environment and a satellite model. The results unequivocally demonstrate the gecko-inspired robot's proficiency in executing various functions, including stable motion and capture on both smooth and rough spacecraft surfaces within a microgravity environment. These experiments underscore the potential of adhesion-based gecko-inspired robots for in-orbit services and spacecraft capture and recovery.

Microrobotic platforms hold significant potential to advance a variety of fields, from medicine to environmental sensing. Herein, minimally functional robotic entities modeled on readily achievable state-of-the-art features in a modern lab or cleanroom are computationally simulated. Inspired by Dou and Bishop (Phys Rev Res. 2019;1(3):1–5), it is shown that the simple combination of unidirectional steering connected to a single environmental (chemical) sensor along with constant propulsion gives rise to highly complex functions of significant utility. Such systems can trace the contours orthogonal to arbitrary chemical gradients in the environment. Also, pairs of such robots that are additionally capable of emitting the same chemical signal are shown to exhibit coupled relative motion. When the pair has unidirectional steering in opposite directions within the 2D plane (i.e., counter-rotating), they move in parallel trajectories to each other. Alternatively, when steering is in the same direction (corotation), the two move in the same epicyclical trajectory. In this way, the chirality of the unidirectional steering produces two distinct emergent phenomena. The behavior is understood as a ratchet mechanism that exploits the differential in the radii of curvature corresponding to different spatial locations. Applications to environmental detection, remediation, and monitoring are discussed.