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.

For space target images captured by a sky-based visible light camera, various conditions are influenced by the imaging system itself and the observation environment; these conditions include uneven image background intensity, complex noise, stray light composition, and diverse target forms. A mean wavelet transform algorithm is proposed. This algorithm initially performs mean filtering and wavelet transform filtering on the noise-containing space target images and then performs a wavelet inverse transform on the filtered results to generate the final image. The experimental results show that our algorithm has good denoising performance and can effectively maintain the image details.

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.

Aiming at the on-orbit safety problem of the two-dimensional tracking servo system for space, based on ant colony optimization (ACO) and expert systems, this paper proposes a fault diagnosis and handling strategy of the two-dimensional (2D) tracking servo system for space. Specifically, a fault library was established for the tracking servo system according to field observations. The ACO was called to optimize the classification of fault features. In addition, sensitivity and specificity were defined to evaluate the classification performance of the diagnosis rules found by the ant colony, aiming to suppress the redundant information in fault diagnosis, reduce the rows of the in-orbit codes, and improve the accuracy of in-orbit fault diagnosis. Meanwhile, the diagnosis information will be processed by the expert system, such that the servo system can work safely and stably in orbit, and less number of in-orbit computing resources were used. Simulation results show that the proposed strategy achieves highly precise and reliable diagnosis.

As a space camera works in orbit, the stress rebound caused by gravity inevitably results in the deformation of its optomechanical structure, and the relative position change between different optical components will affect the Line-Of-Sight pointing of the camera. In this paper, the optical sensitivity calculation of a space camera’s Line-Of-Sight pointing is realized based on the optomechanical constraint equations, and the Line-Of-Sight equations are constructed using the second type of response (DRESP2) method to realize an optomechanical integrated analysis of the camera’s Line-Of-Sight stability at the structural finite element solver level. The verification results show that the Line-Of-Sight stability error is 6.38%, meaning that this method can identify the sensitive optical elements of the optical system efficiently and quickly. Thus, the method in this paper has important significance as a reference for the analysis of the Line-Of-Sight stability of complex optical systems.