Timely detection and early warning of livestock health issues are critical for green and efficient management within large-scale cattle farms. Traditional manual inspections are time-consuming, labor-intensive, and prone to missed or erroneous detections. Robotic inspections offer significant advantages including all-weather operation, high precision, high efficiency, and low cost. However, existing path planning approaches predominantly focus on dynamic obstacle avoidance and fixed target point inspection path, often failing to address two key challenges in dynamic large-scale farm environments: global traversal of individual large livestock (e.g., beef cattle, dairy cows) and accessibility of local areas compromised by dynamic obstacles. This study aims to overcome the limitations of existing robotic inspection systems in large-scale cattle farms, specifically addressing the lack of comprehensive inspection capability for dynamic individuals, excessive path redundancy, and insufficient proactive obstacle avoidance capability.

A global-local optimization algorithm was proposed for large-scale cattle farm intelligent inspection path planning, which integrated the traveling salesman problem (TSP), A^* and dynamic window approach (DWA), and solved the problems of global multi-objective individual traversal, path redundancy and local passability with proactive obstacle avoidance in dynamic cattle farm scenarios. For global traversal optimization, a global path planning algorithm was introduced which combined improved TSP and optimized A^*. Specifically, the inspection status list tracking breeding sheds and individual cattle was maintained to enhance the TSP's Nearest Neighbor Algorithm, dynamically updating targets to avoid re-visits. A dynamic priority mechanism optimized multi-objective inspection, determining the optimal visitation sequence across barns and dynamic paths within barns. The data structure of the A^* algorithm was optimized, a diagonal distance heuristic function was introduced to replace Manhattan distance, which more accurately reflected the movement cost in eight directions. The path obtained by the A^* algorithm through greedy strategy was simplified, and Bresenham's line algorithm was used to check whether there were obstacles in the straight line field of view. If there were no obstacles, redundant inflection points were removed to construct an efficient moving path between sheds. For local passability optimization, an enhanced DWA-based local path was proposed for planning algorithm. The dynamic safety threshold of obstacle size was introduced to improve the DWA. When the inspection robot judged that the size of the obstacle in the local accessible area was too large and the robot was difficult to pass, it would actively avoid or detour in advance to ensure the safe avoidance of large obstacles in narrow passages. The improved DWA also increased the task progress potential field, drived the robot to move to the breeding shed to be visited with the attractive force field model, balanced the local obstacle avoidance and global inspection efficiency, and realized the real-time judgment of local area passability caused by dynamic obstacles and proactive obstacle avoidance in advance.

The optimized A^* algorithm's data structures significantly improved search efficiency. The diagonal distance heuristic and greedy strategy substantially enhanced path smoothness. Compared to the traditional A^*, the improved A^* achieved average reductions of 90.06% in planning time, 85.13% in path turns, and 1.83% in path length. The global inspection algorithm combining improved TSP and optimized A^* achieved 100% average coverage of individual cattle. Inspection path length and time were reduced by 17.99% and 20.85%, respectively, compared to the classic ant colony optimization (ACO) algorithm, demonstrating superior efficiency in dynamic multi-objective inspection scenarios. The improved DWA successfully enabled proactive judgment of local path passability based on obstacle size. By adjusting the robot's linear velocity, angular velocity, and attitude angle in real time, the algorithm achieved robust proactive obstacle avoidance. The inspection robot would reduce the linear velocity in advance when encountering obstacles, and realize proactive obstacle avoidance by adjusting the attitude angle. Simulation experiments confirmed that robots equipped with the improved DWA effectively navigated around unknown static and dynamic obstacles while maintaining global pathtracking capability.

The global inspection algorithm combining improved TSP and optimized A^*, utilizing dynamic inspection status lists and path optimization techniques, achieved global inspection coverage of individual cattle and could significantly improve inspection quality and efficiency. The local inspection algorithm based on improved DWA, incorporating obstacle size dynamic safety threshold and task progress, achieved real-time judgment of local passability and proactive obstacle avoidance, ensuring safe robot navigation in complex environments. The global-local co-optimization framework demonstrated adaptability to the dynamic farm environment, enabling the timely completion of individual traversal tasks, and providing a robust solution for intelligent inspection in large-scale cattle operations. Future work involves integrating the proposed path planning algorithm with simultaneous localization and mapping (SLAM), cattle identification, distance detection systems on inspection robot platforms, and conducting extensive field tests within operational cattle farms. Exploring multi-robot collaborative inspection frameworks and incorporating the Visionand-Language Navigation model to enhance environmental perception and anomaly-handling capabilities are promising directions for adapting to the complexities of even larger-scale farming scenarios.

Developing new-quality productivity is of great significance for promoting high-quality development of animal husbandry. However, there is currently limited research on new-quality productivity in animal husbandry, and there is a lack of in-depth analysis on its connotation, characteristics, constraints, and promotion path.

This article conducts a systematic study on the high-quality development of animal husbandry productivity driven by artificial intelligence. The new-quality productivity of animal husbandry is led by cutting-edge technological innovations such as biotechnology, information technology, and green technology, with digitalization, greening, and ecologicalization as the direction of industrial upgrading. Its basic connotation is manifested as higher quality workers, more advanced labor materials, and a wider range of labor objects. Compared with traditional productivity, the new-quality productivity of animal husbandry is an advanced productivity guided by technological innovation, new development concepts, and centered on the improvement of total factor productivity. It has significant characteristics of high production efficiency, good industrial benefits, and strong sustainable development capabilities. China's new-quality productivity in animal husbandry has a good foundation for development, but it also faces constraints such as insufficient innovation in animal husbandry breeding technology, weak core competitiveness, low mechanization rate of animal husbandry, weak independent research and development capabilities of intelligent equipment, urgent demand for "machine replacement", shortcomings in the quantity and quality of animal husbandry talents, low degree of scale of animal husbandry, and limited level of intelligent management. Artificial intelligence in animal husbandry can be widely used in environmental control, precision feeding, health monitoring and disease prevention and control, supply chain optimization and other fields. Artificial intelligence, through revolutionary breakthroughs in animal husbandry technology represented by digital technology, innovative allocation of productivity factors in animal husbandry linked by data elements, and innovative allocation of productivity factors in animal husbandry adapted to the digital economy, has given birth to new-quality productivity in animal husbandry and empowered the high-quality development of animal husbandry.

This article proposes a path to promote the development of new-quality productivity in animal husbandry by improving the institutional mechanism of artificial intelligence to promote the development of modern animal husbandry industry, strengthening the application of artificial intelligence in animal husbandry technology innovation and promotion, and improving the management level of artificial intelligence in the entire industry chain of animal husbandry.