The artificial potential field method has efficient obstacle avoidance ability, but this traditional method suffers from local minima, unreasonable paths, and sudden changes in heading angles during obstacle avoidance, leading to rough paths and increased energy consumption. To enable autonomous mobile robots (AMR) to escape from local minimum traps and move along reasonable, smooth paths while reducing travel time and energy consumption, in this paper, an artificial potential field method based on subareas is proposed. First, the optimal virtual subgoal was obtained around the obstacles based on the relationship between the AMR, obstacles, and goal points in the local environment. This was done according to the virtual subgoal benefit function to solve the local minima problem and select a reasonable path. Secondly, when AMR encountered an obstacle, the subarea-potential field model was utilized to solve problems such as path zigzagging and increased energy consumption due to excessive changes in the turning angle; this helped to smooth its planning path. Through simulations and actual testing, the algorithm in this paper demonstrated smoother heading angle changes, reduced energy consumption, and a 10.95% average reduction in movement time when facing a complex environment. This proves the feasibility of the algorithm.

The relationship between protein changes and egg quality during storage is a critical area of investigation in both basic science and egg product preservation. Viscosity changes in albumen are a sensitive sign of the deterioration process. Ovomucin, which is present in albumen, plays an important role in the gelation of the albumen. Egg freshness indices and ovomucin concentration will alter in response to storage time. This paper analyzed the correlation and gray relational degree between the Haugh unit (HU), yolk index, albumen pH, and ovomucin concentration. We studied the differences in the ovomucin concentration at different levels of HU, yolk index, and pH during storage. We established an equivalent egg age prediction model using ovomucin concentration as the independent variable. The findings indicated a correlation between the freshness indices and the ovomucin concentration. There was a good, significantly positive relationship between HU and ovomucin concentration (r = 0.713, ), a positive correlation between the yolk index and ovomucin concentration (r = 0.699, ), and a negative correlation between albumen pH and ovomucin concentration (r = −0.683, ). The highest Pearson correlation coefficient (r = 0.970, ) was obtained between the albumen and ovomucin concentration. Significant differences in ovomucin concentration were observed when HU, yolk index, and pH were varied. The gray relational degree between each freshness parameter and ovomucin concentration was greater than 0.8. Between the HU and ovomucin concentration, there was a gray correlation degree of 0.885, indicating that the HU was the primary factor affecting ovomucin concentration variation during storage. During storage, at 22°C, the ovomucin concentration in albumen was significantly and negatively related to storage time (r = −0.926, ). The coefficient of determination for the equivalent egg age prediction model with ovomucin concentration as the independent variable was 0.985 ( ), indicating strong reliability. The study’s findings show the possibility of nondestructive prediction of an egg’s internal microscopic protein composition using its freshness index value.

As living standards rise, people have higher requirements for the quality of duck eggs. The quality of duck eggs is related to their origin. Thus, the origin traceability and identification of duck eggs are crucial for protecting the rights and interests of consumers and preserving food safety. As the world’s largest producer and consumer of duck eggs, China’s duck egg market suffers from a severe lack of duck egg traceability and rapid origin identification technology. As a result, a large number of duck eggs from other regions are sold as products from well-known brands, which seriously undermines the rights and interests of consumers and is not conducive to the sound development of the duck egg industry. To address the above issues, this study collected visible/near-infrared spectral data online from duck eggs of three distinct origins. To reduce noise in the spectral data, various pre-processing algorithms, including MSC, SNV, and SG, were employed to process the spectral data of duck eggs in the range of 400–1100 nm. Meanwhile, CARS and SPA were used to select feature variables that reflect the origin of duck eggs. Finally, classification models of duck egg origin were developed based on RF, SVM, and CNN, achieving the highest accuracy of 97.47%, 98.73%, and 100.00%, respectively. To promote the technology’s implementation in the duck egg industry, an online sorting device was built for duck eggs, which mainly consists of a mechanical drive device, spectral software, and a control system. The online detection performance of the machine was validated using 90 duck eggs, and the final detection accuracy of the RF, SVM, and CNN models was 90%, 91.11%, and 94.44%, with a detection speed of 0.1 s, 0.3 s, and 0.5 s, respectively. These results indicate that visible/near-infrared spectroscopy can be exploited to realize rapid online detection of the origin of duck eggs, and the methodologies used in this study can be immediately implemented in production practice.