In confined spaces, conventional ground mobile robots may be unable to reach the target location because of limited maneuvering space or the inability to overcome obstacles. This study presents a ground mobile mechanism with a multi-loop reconfigurable trunk (GMMRT) designed to enhance mobility in constrained spaces, such as narrow gaps, ditches, and cross-shaped channels. GMMRT can effectively overcome obstacles in confined spaces through the coordinated motion of its wheels and reconfigurable trunk. Its reconfigurable trunk comprises six limbs and four vertex platforms, forming a versatile, adaptable structure. GMMRT supports two topological structures: wheeled mobility and overall rolling. It features three distinct motion modes: (i) adjusting external dimensions, (ii) performing zero-radius steering, and (iii) overall deformation rolling motion. The kinematic model of GMMRT is established, and its parameters are described using Denavit–Hartenberg parameters. The degrees of freedom under the two topological structures are analyzed on the basis of screw theory. Torque analysis of servo motors is conducted through dynamic analysis. An experimental prototype is designed to validate the three motion modes and servo motor selection, and relevant experiments are performed. Through the development and experimental validation of GMMRT, this work advances mobile robot design for confined spaces and provides a promising approach to overcome the limitations of conventional ground robots.
As semiconductor manufacturing moves toward fine feature sizes, precise and efficient resist model calibration has become crucial for optical proximity correction to ensure pattern fidelity. However, traditional calibration methods struggle with efficiency and scalability and are prone to becoming trapped in local optima. Herein, we propose a surrogate-assisted genetic algorithm (SAGA) that integrates Kriging interpolation-based surrogate models and dynamic adaptive mechanisms to optimize resist model coefficients, convolution kernel parameters, and aerial image settings jointly. By leveraging surrogate models to predict high-performance solutions and adaptively adjusting crossover/mutation rates, SAGA balances global exploration and local exploitation, achieving rapid convergence and superior model accuracy compared with other algorithms. Experimental validation across three resist cases demonstrates that SAGA outperforms conventional genetic algorithms and grid search. Compared with other algorithms, SAGA not only achieves higher accuracy but also converges faster, with its optimization trajectories stabilizing earlier in the iterative process. These results highlight SAGA’s potential for efficient and high-precision resist calibration in computational lithography.
A method is proposed to control the minimum width of lattice structure in the topology optimization by using a Multiple Variable Cutting (M-VCUT) based substructure. The geometry of substructure is described by using the M-VCUT level set approach, and the substructures are condensed to superelements. A data-driven model of substructure is constructed, and it is used for the finite element analysis and sensitivity analysis during the optimization, so that computational costs are reduced. More importantly, only the substructures whose minimum width are larger than an admissible value are considered in the data-driven model, thus inherently enforcing the constraint of minimum width and making the optimization much easier. The effectiveness of the proposed method is demonstrated through several numerical examples.
Micro/nano functional structures (MNFSs) have attracted substantial attention because of their outstanding performance in optical, tribological, thermal, electronic, and biomedical applications. Despite the development of various mechanical and non-mechanical machining methods, achieving the high-efficiency, high-precision fabrication of MNFS from difficult-to-cut materials remains a significant technical challenge. This review begins with an introduction to typical artificial MNFSs and their stringent requirements and then provides a comprehensive survey of MNFSs, focusing on etching methods. In particular, plasma etching demonstrates notable advantages in MNFS fabrication. However, two critical challenges persist: accurately controlling topographical information during pattern transfer in plasma etching and achieving high-quality, uniform patterning masks over large areas. These issues are addressed by thoroughly analyzing and summarizing the modeling of plasma etching and the simulation of feature profiles. Various hybrid etching machining (HEM) strategies, including laser and etching combined machining, cutting and etching combined machining, molding and etching combined machining, and self-assembly and etching combined machining, are categorized and compared in detail to facilitate the manufacturing of complex MNFSs. Finally, this review summarizes current deficiencies and future challenges of HEM, laying the groundwork for further advancements in MNFS fabrication and intelligent HEM technologies.
Resonance-based devices are extensively utilized in engineering applications due to their low energy consumption and high energy conversion efficiency. However, fluctuations in resonance frequency caused by variations in stiffness and damping in the system can lead to a mismatch with the excitation frequency, which degrades the performance of the system. To address this issue, real-time resonance frequency tracking (RFT) is crucial. This study proposes a phase-locked loop-based adaptive extremum seeking control (PLL-AESC) method for online RFT, which is demonstrated using a three-degree-of-freedom resonance system. The method employs the response amplitude of the system as a cost function and estimates the local gradient in real time, which enables indirect optimization of the excitation frequency. Compared with conventional direct RFT approaches, PLL-AESC offers reduced computational complexity and improved real-time performance. Furthermore, its learning rate is adaptively adjusted based on the gradient and phase difference signals, which improves the efficiency of the method to various disturbances. The effectiveness of PLL-AESC is validated through simulations and comparative analyses. Results indicate that the proposed method demonstrates a shorter settling time than traditional extremum seeking control methods. Compared with the phase-locked loop method, it successfully maintains RFT performance under damping variation disturbances.
Hierarchical and porous complex metal structures have important prospects in biomedical medicine, aerospace, and other important industries. Current processing methods, including conventional and additive manufacturing, still face many challenges in fabricating metal structures with complex internal structures, especially aluminum alloy ones. Metal molten infiltration on the basis of sacrificial salt templates provides a feasible solution for manufacturing complex internal structures of aluminum alloy, and the development of paste for direct ink writing is pivotal for the successful fabrication of salt templates. This study introduces a novel salt-based paste for printing sacrificial templates by direct ink writing. A print analysis model is formulated based on direct ink writing. Model and experimental results show a remarkable link between strut size and printing pressure and moving speed, but no such link exists between strut size and nozzle height. The paste can be printed into various complex structures by direct ink writing devices. Complex structures of aluminum alloys can be fabricated by metal infiltration and leaching of salt templates. This work provides a new reference technology for fabricating aluminum alloys with complex structures in the future.
In a convolution material removal process, taking grinding free-form surfaces as an example, the workpiece’s complex shape may lead to dynamic tool–workpiece contact state, and the curved tool path results in an uneven dwell time distribution. These factors contribute to non-uniform material removal (NMR), causing over-grinding or under-grinding in localized areas. This work aims to model NMR accurately and propose a method to enhance material removal uniformity. First, a dynamic tool–workpiece contact model integrating the workpiece’s complex shape, contact force, and the mechanical properties of the tool and the workpiece is proposed by introducing the measured workpiece point cloud. Second, path geodesic curvature is employed to calculate the dwell time distribution. Third, a material removal model that combines the dynamic tool–workpiece contact and the uneven dwell time distribution is introduced. Then, the tool influence function is optimized by adjusting the tool orientation to improve material removal uniformity. Finally, the proposed material removal model and optimization method are validated through experiments, with results showing a remarkable improvement in material removal uniformity using this approach.
Real-time slip detection and state estimation are crucial for locomotion control, facilitating posture adjustment and stability recovery of multi-legged robots moving on slippery terrain. However, existing proprioceptive methods rely on the fixed-contact assumption with fixed noise and suffer from low accuracy when multiple legs slip simultaneously. This paper proposes a novel proprioceptive approach for multi-legged robots moving in slippery scenarios to cope with slippage of multiple legs. In slip detection, the proprioceptive states of the robot are fed into a convolutional neural network to detect slip event(s) of the robot, enabling accurate identification of slipping legs even under simultaneous multi-leg slippage. For state estimation, an invariant extended Kalman filter is employed to fuse the motion information with the detected slip event(s) to obtain the robot state. By incorporating slip event(s) and foot velocity into the system motion equation of the filter, the proposed method better leverages leg odometry information and achieves more precise state estimation compared with existing methods. Simulations on a quadruped and a hexapod demonstrate the effectiveness and increased accuracy during multi-leg slippage. Experimental results for the quadruped robot show that the proposed approach achieves a % reduction in the root mean square error and a % reduction in the maximum error in velocity estimation under severe multi-leg slippage compared with the existing methods.