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A polarized light sensor is applied to the front-end detection of a biomimetic polarized light navigation system, which is an important part of analyzing the atmospheric polarization mode and realizing biomimetic polarized light navigation, having received extensive attention in recent years. In this paper, biomimetic polarized light navigation in nature, the mechanism of polarized light navigation, point source sensor, imaging sensor, and a sensor based on micro nano machining technology are compared and analyzed, which provides a basis for the optimal selection of different polarized light sensors. The comparison results show that the point source sensor can be divided into basic point source sensor with simple structure and a point source sensor applied to integrated navigation. The imaging sensor can be divided into a simple time-sharing imaging sensor, a real-time amplitude splitting sensor that can detect images of multi-directional polarization angles, a real-time aperture splitting sensor that uses a light field camera, and a real-time focal plane light splitting sensor with high integration. In recent years, with the development of micro and nano machining technology, polarized light sensors are developing towards miniaturization and integration. In view of this, this paper also summarizes the latest progress of polarized light sensors based on micro and nano machining technology. Finally, this paper summarizes the possible future prospects and current challenges of polarized light sensor design, providing a reference for the feasibility selection of different polarized light sensors.

Based on the landing environment of airport aircraft and the cleaning requirements of a large number of navigation aids, this paper proposes a scheme of intelligent cleaning of navigation aids with mobile robots on the delivery vehicle. The cleaning efficiency of the robot can be effectively improved by optimizing the navigation mode and the moving path of the robot using the feature point navigation according to the layout of the navigation aids. Full automation of navigation light cleaning has been achieved, which saves a lot of manpower and resources for cleaning airports.

Underwater navigation remains constrained by technological limitations, driving the exploration of alternative approaches such as polarized light-based systems. This review systematically examines advances in polarized navigation from three perspectives. First, the principles of atmospheric polarization navigation are analyzed, with their operational mechanisms, advantages, and inherent constraints dissected. Second, innovations in bionic polarization multi-sensor fusion positioning are consolidated, highlighting progress beyond conventional heading-direction extraction. Third, emerging underwater polarization navigation techniques are critically evaluated, revealing that current methods predominantly adapt atmospheric frameworks enhanced by advanced filtering to mitigate underwater interference. A comprehensive synthesis of underwater polarization modeling methodologies is provided, categorizing physical, data-driven, and hybrid approaches. Through rigorous analysis of studies, three persistent barriers are identified: (1) inadequate polarization pattern modeling under dynamic cross-media conditions; (2) insufficient robustness against turbidity-induced noise; (3) immature integration of polarization vision with sonar/IMU (Inertial Measurement Unit) sensing. Targeted research directions are proposed, including adaptive deep learning models, multi-spectral polarization sensing, and bio-inspired sensor fusion architectures. These insights establish a roadmap for developing reliable underwater navigation systems that transcend current technological boundaries.

Adequate homing is essential for the survival of any animal when it leaves its home to find prey or a mate. There are several strategies by which homing can be carried out: (a) retrace the outbound path; (b) use a ‘cognitive map’; or (c) use path integration (PI). Here, I review the state of the art of research on spiders (Araneae) and whip spiders (Amblypygi) homing behaviour. The main strategy described in the literature as being used by these arachnids is PI. Behavioural and neural substrates of PI are described in a small group of spider families (Agelenidae, Lycosidae, Gnaphosidae, Ctenidae and Theraphosidae) and a whip spider family (Phrynidae). In spiders, the cues used to detect the position of the animal relative to its home are the position of the sun, polarized light patterns, web elasticity and landmarks. In whip spiders, the cues used are olfactory, tactile and, with a more minor role, visual. The use of a magnetic field in whip spiders has been rejected both with field and laboratory studies. Concerning the distance walked in PI, the possibility of using optic flow and idiothetic information in spiders is considered. The studies about outbound and inbound paths in whip spiders seem to suggest they do not follow the PI rules. As a conclusion, these arachnids’ navigation relies on multimodal cues. We have detailed knowledge about the sensory origin (visual, olfactory, mechanosensory receptors) of neural information, but we are far from knowing the central neural structures where sensory information is integrated.

To rectify significant heading calculation errors in polarized light navigation for unmanned aerial vehicles (UAVs) under tilted states, this paper proposes a method for compensating horizontal attitude angles based on horizon detection. First, a defogging enhancement algorithm that integrates Retinex theory with dark channel prior is adopted to improve image quality in low-illumination and hazy environments. Second, a dynamic threshold segmentation method in the HSV color space (Hue, Saturation, and Value) is proposed for robust horizon region extraction, combined with an improved adaptive bilateral filtering Canny operator for edge detection, aimed at balancing detail preservation and noise suppression. Then, the progressive probabilistic Hough transform is used to efficiently extract parameters of the horizon line. The calculated horizontal attitude angles are utilized to convert the body frame to the navigation frame, achieving compensation for polarization orientation errors. Onboard experiments demonstrate that the horizontal attitude angle estimation error remains within 0.3°, and the heading accuracy after compensation is improved by approximately 77.4% relative to uncompensated heading accuracy, thereby validating the effectiveness of the proposed algorithm.

This paper considers the feasibility of using augmented reality (AR) as a tool for enhancing visualization in maritime operations to avoid collision in different environmental conditions. According to the International Maritime Organization (IMO 2010), 90% of maritime accidents due to collisions at sea are caused in part by human error. This study investigates the new technology (AR) used to superimpose holographic images onto the real world; now reaching a state of readiness for commercial application. This paper demonstrates the competence of AR technology to serve as a maritime navigational aid. The research explores the viability of improving navigational safety in low visibility by projecting holograms of real-world objects in the same geo-location as the real object to make them “visible”. The paper presents the logical deconstruction of the technical problems and identified solutions, together with results of experiments used to validate the concept and technology readiness for real word maritime application. The paper presents a verified demonstrator; a proposed holographic bridge interface with an innovative way of presenting information using AR technology. Furthermore, it identifies that new technologies offer the opportunity for enhanced operator performances, with the expectation being that this should lead to reduce risk to persons, property, and the environment.

This paper proposes an artificial neural network to determine orientation using polarized skylight. This neural network has specific dilated convolution, which can extract light intensity information of different polarization directions. Then, the degree of polarization (DOP) and angle of polarization (AOP) are directly extracted in the network. In addition, the exponential function encoding of orientation is designed as the network output, which can better reflect the insect’s encoding of polarization information and improve the accuracy of orientation determination. Finally, training and testing were conducted on a public polarized skylight navigation dataset, and the experimental results proved the stability and effectiveness of the network.

Angnuureng, D. B., Almar, R., Appeaning Addo, K., Senechal, N., Castelle, B., Laryea, S. W., Wiafe, G., 2016. Video observation of waves and shoreline change on the microtidal James town Beach in Ghana.. In: Vila-Concejo, A.; Bruce, E.; Kennedy, D.M., and McCarroll, R.J. (eds.), Proceedings of the 14th International Coastal Symposium (Sydney, Australia). Journal of Coastal Research, Special Issue, No. 75, pp. 1022 - 1026, Coconut Creek (Florida), ISSN 0749-0208. The morphology of sandy beaches is highly dynamic. They are influenced by the geology of the coastal area and external hydrodynamic forcing. On long timescales (years to decades), it is more efficient and convenient to monitor beach evolution through remote sensing technics rather than through direct field measurements. Erosion is a major problem along the coastline of Ghana with over 25 erosion hotspots, including James town. Here, tides, ECMWF EraInterim re-analysis wave data and images covering the beach area have been obtained for the 2013–2014 period. This paper presents preliminary results of the first efforts in processing video-derived observations of waves and shoreline change in Ghana. The pilot application shows a strong potential of the video system in providing fair quality wave data for beach management purposes where video wave characteristics are in good agreement with EraInterim global reanalysis (daily RMSE = 0.8 m and 0.7 m for Hb and Tp, respectively). Shorelines extracted from video suggest large monthly variability driven by wave seasonality while shoreline change shows a subsequent erosion/accretion cycle.

This paper presents the conceptualization of Smart Night-Vision Glasses, an innovative assistive device aimed at individuals with night blindness and Retinitis Pigmentosa (RP). These conditions, characterized by significant difficulty in seeing in low-light or dark environments, currently have no effective medical solution. The proposed glasses utilize advanced sensor technologies such as LiDAR, infrared, and ultrasonic sensors, combined with artificial intelligence (AI), to create a real-time, visual representation of the surroundings. Unlike conventional camera-based systems, which require light to function, this device relies on non-visible, non-harmful rays to detect environmental data, making it suitable for use in pitch-dark conditions. The AI processes the sensor data to generate a simplified, user-friendly view of the environment, outlined with clear, cartoon-like visuals for easy identification of objects, obstacles, and surfaces. The glasses are designed to look like regular prescription eyewear, ensuring comfort and discretion, while a button or trigger can switch them to \"night mode\" for enhanced vision in low-light settings. This concept aims to improve the independence, safety, and quality of life for individuals with night blindness and RP, offering a transformative solution where no medical alternatives currently exist. However, challenges such as sensor miniaturization, power consumption, and AI integration must be addressed for successful implementation. Beyond its direct benefits for users, the device could have broader societal and economic impacts by enhancing accessibility, reducing nighttime accidents, and fostering technological innovation in assistive wearables. The paper also discusses future directions for research and refinement of the technology while supporting the Process Innovation.