Development and Application of an Inspection-Robot-Based Digital Twin Platform for Cage-Reared Broilers

With the expansion of broiler production and the transition toward intelligent and labor-saving management, conventional manual inspection is limited by high labor intensity, unintuitive spatial representation of abnormalities, and inefficient on-site verification. To address these limitations, this study developed an abnormality monitoring system for cage-reared broiler houses by integrating an inspection robot, a digital twin environment, and cloud-based data services. A parameterized three-dimensional model and semantic cage anchors were established according to the dimensions of the physical broiler house and the cage arrangement rules. Robot simultaneous localization and mapping (SLAM) poses, inspection aisles, camera identifiers, and cage arrangement parameters were combined to calculate the semantic locations of dead-bird events and map them within the digital twin environment. Open-mouth breathing, infrared abnormalities, and acoustic abnormalities were additionally visualized at the candidate-cage, local-region, or inspection-aisle level according to the completeness of the available localization information. The system also enabled virtual–physical synchronization of the robot’s position, orientation, and operating status, as well as remote interactive control through a WebGL-based interface. Field tests conducted over approximately 100 days showed that model optimization reduced the triangle count, vertex count, and file size by 48.34%, 30.47%, and 44.81%, respectively, while shortening the initial WebGL scene loading time from 3.84 to 3.05 s. When 500 abnormality markers were displayed simultaneously, the optimized scene maintained an average frame rate of 67.83 fps. Among 2035 dead-bird events, 1954 were correctly localized in terms of cage row, tier, and group, yielding a cage-level localization accuracy of 96.0%. A total of 6412 robot control-command records were evaluated, achieving an overall execution success rate of 99.50%, with mean feedback times ranging from 1.0 to 1.2 s. These results demonstrate that the proposed system provides an integrated workflow for abnormality event acquisition, cage-level localization, three-dimensional visualization, and inspection robot management.