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

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.

As a new class of robots, soft continuum manipulators have attracted attention due to their flexibility and compliance. However, these characteristics create challenges for precise modeling and control. This study proposes a hybrid offline and online data-driven scheme to achieve high-precision tracking control of a soft continuum manipulator. First, a novel multiscale deep neural network learns the manipulator model offline. Specifically, the feature fusion module extracts highly discriminative features and captures long-term dependencies from the temporal trajectory data. The self-attention module strengthens the ability to represent fusion features and enhances the model prediction accuracy. Then, the learnt model is updated using multisensor data online, and the proposed controller further compensates for the updated model and enhances the tracking accuracy in the movement stage. Finally, the experimental results demonstrate a significant improvement in motion accuracy under different trajectory-tracking scenarios (i.e., deviations of <1 mm in position and <0.8° in orientation). The example of the multiwire cable sorting proves the feasibility of the proposed scheme in high-precision industrial applications.

Magnetic particle imaging (MPI) is an emerging noninvasive molecular imaging modality with high sensitivity and specificity, exceptional linear quantitative ability, and potential for successful applications in clinical settings. Computed tomography (CT) is typically combined with the MPI image to obtain more anatomical information. Herein, a deep learning-based approach for MPI-CT image segmentation is presented. The dataset utilized in training the proposed deep learning model is obtained from a transgenic mouse model of breast cancer following administration of indocyanine green (ICG)-conjugated superparamagnetic iron oxide nanoworms (NWs-ICG) as the tracer. The NWs-ICG particles progressively accumulate in tumors due to the enhanced permeability and retention (EPR) effect. The proposed deep learning model exploits the advantages of the multihead attention mechanism and the U-Net model to perform segmentation on the MPI-CT images, showing superb results. In addition, the model is characterized with a different number of attention heads to explore the optimal number for our custom MPI-CT dataset.

In this article, a game theoretic non-cooperative dynamic target tracking algorithm that empowers defensive unmanned aerial vehicles (UAVs), with directional sensing capabilities to track and collect information on intrusive UAVs, is proposed. Specifically, defenders aim to maximize the collection of identity information from intruders possessing anti-tracking and evading capabilities, while simultaneously preventing their entry into protected areas. Game theory is employed to determine the optimal confrontation paths for defenders against the intruders. The probability perception model is utilized for evaluating the dynamic target tracking capability and designing a tracking merit function to assess tracking performance, taking into account both the target's position and the perception relative angle. Furthermore, considering the dynamic interactive behaviors between intruders and defenders, the iterative linear quadratic game (ILQG) algorithm is employed to solve the Nash equilibrium of the non-cooperative target tracking game. Through simulation experiments, the effectiveness of the proposed algorithm in accomplishing multi-agent dynamic target tracking tasks is demonstrated and the performance of the algorithm under varying parameters in the intruder's cost function is evaluated, which represent different intrusion intentions.

Retinal surgery requires enclosed spatial constraints to improve the safety and success of the surgery. Herein, a shared control method is proposed for master–slave robot systems, utilizing tubular guidance constraints based on a novel potential field function to optimize the commands of the surgeon. Within the tube, attractive constraints intensify with increasing task error and approach infinity at the boundary of the tube. This ensures that the surgery is confined within a closed tubular space. Haptic feedback provides force cues to inform the surgeon about the feasibility of the input commands. Theoretical derivations demonstrate that the entire closed-loop system is passive. Two simulation experiments are conducted on the ophthalmic surgery robot platform to evaluate the functionality of the proposed method. The experimental results indicate that translational errors are kept less than certain predefined values. Furthermore, the proposed method outperforms the comparison method in terms of task accuracy and efficiency.

Artificial intelligence (AI) is increasingly used to solve multi-objective problems and reduce the turnaround times of semiconductor processes. However, only brief AI explanations are available for process/device/circuit engineers to provide holistic feedback on the manufactured results. Herein, linear/nonlinear variation decomposition (LVD/NLVD) of neural networks is demonstrated to quantitatively evaluate the influence of unit processes on the figure of merit (FoM) and co-analyze the unit process influences with device characteristic behaviors. The NLVD can evaluate the output variation from each input of neural networks in an individual sample, although neural networks are not available in an analytic form. The NLVD is successfully verified by confirming that a) the output and summation of all decomposed output variations perfectly coincide and b) the process influences on the FoM are decomposed to 6.01–54.86% more accurately compared with those of LVD in 1Y nm node dynamic random-access memory test vehicles with a baseline and split tests introducing high-k metal gates with a minimum gate length of 1 A nm node for further node scaling. The approaches identify defective processes and defect mechanisms in each sample and wafer, which enhance causal analyses for individual cases in diverse fields based on regression artificial neural networks.

The growing interest in the flexibility and operational capabilities of soft manipulators in confined spaces emphasizes the need for precise modeling and accurate motion control. Conventional control methods encounter difficulties in modeling and involve intricate computations. This work introduces a novel deep reinforcement learning (DRL) control algorithm based on neural network modeling. Using the Whale Optimization Algorithm, an approximate dynamic model for the soft manipulator is established. The twin delayed deterministic policy gradient is employed for DRL control. Domain randomization is applied during pretraining in a simulated environment. The algorithm addresses issues related to dependency on measurement data quality and redundant mappings, outperforming other methods by 8–15 mm in control accuracy. The trained DRL controller achieves precise trajectory tracking within the soft manipulator's task space, enabling successful grasping tasks in various complex environments, including pipelines and other narrow spaces. Experimental results confirm the autonomy of our controller in performing these tasks without human intervention.

Capsule networks promise significant benefits over convolutional neural networks (CNN) by storing stronger internal representations and routing information based on the agreement between intermediate representations’ projections. Despite this, their success has been limited to small-scale classification datasets due to their computationally expensive nature. Though memory-efficient, convolutional capsules impose geometric constraints that fundamentally limit the ability of capsules to model the pose/deformation of objects. Further, they do not address the bigger memory concern of class capsules scaling up to bigger tasks such as detection or large-scale classification. Herein, a new family of capsule networks, deformable capsules (DeformCaps), is introduced to address object detection problem in computer vision. Two new algorithms associated with our DeformCaps, a novel capsule structure (SplitCaps), and a novel dynamic routing algorithm (SE-Routing), which balance computational efficiency with the need for modeling a large number of objects and classes, are proposed. This has never been achieved with capsule networks before. The proposed methods efficiently scale up to create the first-ever capsule network for object detection in the literature. The proposed architecture is a one-stage detection framework and it obtains results on microsoft common objects in context which are on par with state-of-the-art one-stage CNN-based methods, while producing fewer false-positive detection, generalizing to unusual poses/viewpoints of objects.

Fluid-Driven Director-Field Design

In article number 2400179, Yaron Veksler and co-workers present a modular platform of interconnected multi-stable tubes that can transform into a wide range of desired shapes on demand. Using detachable links designed based on director-field theory and viscous fluid actuation, they easily control the shape morphing process. This enables dramatic changes in the final shape while unlocking numerous intermediate configurations. Their method opens new possibilities for deployable structures in applications ranging from soft robotics to medical devices and space exploration.