Technical advancement has propelled the proliferation of unmanned vehicles. Out of the multiple paths between origin (O) and destination (D), the optimal O-D path should be selected in the light of travel distance, travel time, fuel cost and pollutant emissions. This paper proposes a dynamic path planning strategy based on fuzzy logic (FL) and improved ant colony optimization (ACO). Firstly, the classic ACO was improved into the rank-based ant system. The rank-based ant system works well in static environments, but cannot adapt well to dynamic environments. Considering the difficulty in accurate digitization of dynamic factors, the improved ACO was integrated with the FL into the fuzzy logic ant colony optimization (FLACO) to find the optimal path for unmanned vehicles. Finally, the FLACO, the classic ACO and the improved ACO were separately applied to find the optimal path in a road network, with a novel concept called virtual path length. The results show that the FLACO output the shortest virtual path among the three algorithms, i.e. identified the most cost-effective path. This mean the FLACO can find the most efficient and safe path for unmanned vehicles in a dynamic manner.

This study proposed a rotary traveling wave ultrasonic motor utilizing the B (0, 5) axial bending mode of a ring-shape stator. The proposed motor had a compact structure as only four groups of piezoelectric ceramics were nested into the stator to produce a bending traveling wave, a new design method was proposed utilized less PZT ceramics to reduce the volume and to improve the mechanical output characteristics. The operating principle of the proposed motor was illustrated. The finite element analysis was performed to obtain the vibration modes and the motion trajectories of the stator. A prototype was manufactured to validate the operating principle. The two standing waves and the motion trajectories of the driving tips were measured. The results denoted that this motor obtained an output speed of 53.86 rpm under a preload of 0.69 N when the frequency and voltage were 24.86 kHz and 250 Vp-p, the maximum stall torque was tested as about 0.11 N·m under the preload of 3.14 N Finally, this study was compared with a previous design and it was found that the volume was reduced markedly; furthermore, the no-load speed, the efficiency, the torque density and the power density were improved significantly.

Seed phenomics is a critical research field for understanding seed germination mechanisms. Metasurfaces, composed of subwavelength nanostructures, offer a promising pathway to achieve both dispersion control and imaging functionalities within an ultra-compact form factor. Recent advances in micro–nano-optics and computational imaging have opened new avenues for high-dimensional, multimodal imaging. However, conventional hyperspectral and light-field systems still face limitations in compactness, depth resolution, and spectral–spatial integration. This review summarizes recent progress in metalens and metasurface lens array-based light-field systems for hyperspectral imaging and 3D reconstruction, with a focus on the underlying principles, design strategies, and reconstruction algorithms that enable single-shot 3D hyperspectral acquisition. We further present a forward-looking roadmap toward the realization of a revolutionized imaging paradigm: a metasurface-based light-field platform that fully integrates 3D and hyperspectral imaging capabilities. In particular, we examine how dispersive metasurfaces serve as core optical elements for precise dispersion control in hyperspectral imaging systems, while metalens arrays enable accurate modulation of spatial–angular distributions in light-field configurations. We systematically review both 3D and spectral reconstruction algorithms, highlighting their roles in decoding complex optical encodings. The application of these integrated systems in seed phenotyping is emphasized, demonstrating their capability to capture 3D spatial–spectral distributions in a single exposure. This approach facilitates high-throughput analysis of morphological traits, germination potential, and internal biochemical composition, offering a comprehensive solution for advanced seed characterization. Finally, we outline a practical roadmap for implementing a metasurface-based light-field platform that integrates hyperspectral imaging and computational 3D reconstruction. This review offers a comprehensive overview of the state of the art in compact 3D light-field systems and multimodal hyperspectral imaging platforms, while providing forward-looking insights aimed at advancing smart seed phenotyping, precision agriculture, and next-generation optical imaging technologies.

The Fast Steering Mirror (FSM) is a critical beam steering component in precision optical systems. However, its design often relies on repetitive finite element analysis (FEM) and experimentation due to the lack of universal and efficient theoretical models. To address this, this paper proposes a generalized and modular modeling and analysis framework for FSMs based on the Transfer Matrix Method (TMM). A generalized four-actuator FSM structure is first decomposed into basic elements such as flexure hinges and rigid links, whose individual transfer matrices are assembled according to the system topology to construct the overall dynamic model. Validation against FEM shows static pointing angle errors are within 0.5%, and the error for the first natural frequency is less than 4%. Crucially, the framework's generality was validated by substituting elliptical hinges with a circular variant without altering the global equations, achieving a 1.77% dynamic prediction error. Furthermore, computational time per case was reduced from 10–30 minutes using FEM to approximately 13 milliseconds. By circumventing the manual equation re-derivation typical of geometry-specific models, this significant efficiency improvement, combined with high accuracy, makes the proposed modular framework an effective tool for rapid conceptual design, parametric optimization, and the evaluation of diverse topological architectures.

Three-dimensional seed phenotyping requires imaging systems capable of achieving micron-level resolution across a centimeter-level field of view (FOV), a goal constrained by the resolution–FOV trade-off in conventional light field architectures. This paper presents a hardware–software co-optimized framework that integrates a reconfigurable optical system with computational imaging pipelines to address this limitation. At the hardware level, we develop a tunable-focus lens module that enables flexible adjustment of the effective focal length, combined with a custom-designed microlens array (MLA). A mathematical model is established to analyze the interdependencies among FOV, lateral resolution, depth of field (DOF), and system configuration, guiding the design of individual optical components. On the computational side, we propose a hybrid aberration correction strategy: first, a co-calibration of lens and MLA aberrations based on line-feature detection; second, a conditional generative adversarial network (cGAN) with attention-guided residual learning to enhance sub-aperture images, achieving a PSNR of 34.63 dB and an SSIM of 0.9570 on seed datasets. Experimentally, the system achieves a resolution of 6.2 lp/mm at MTF50 over a 2–3 cm FOV, representing a 307% improvement over the initial configuration (1.52 lp/mm). The reconstruction pipeline combines epipolar plane image (EPI) analysis with multi-view consistency constraints to generate dense 3D point clouds at a density of approximately 1.5 × 10⁴ points/cm² while preserving spectral and textural features. Validation on bitter melon and rice seeds demonstrates accurate 3D reconstruction and accurate extraction of morphological parameters across a large area. By integrating optical and computational design, this work establishes a reconfigurable imaging framework that overcomes the resolution–FOV limitations of conventional light field systems. The proposed architecture is also applicable to robotic vision and biomedical imaging.