# Navigation Path Extraction and Experimental Research of Pusher Robot Based on Binocular Vision
## 3 Results and Analysis  
### Navigation Path Extraction Test  
Image data were collected at the Jinlan Dairy Farming Company (Tai'an, Shandong, China) to verify the precision and accuracy of the navigation path extraction method. The image acquisition time was from 3 November to 7 November 2021. At 9:00 and 16:00 every day, the farm used the TMR spreader to add feed to the cowshed, and the dairy cows are freely. Two hours after the work of the spreader was over, the pusher robot was used to automatically move along the forage stacking area to the front of the dairy cow feed fence. As shown in Figure 18, during operation, the robot pushed the feed to the vicinity of the feed fence and concentrated the scattered feed to the feeding area without disturbing the normal feeding. In all experiments, the operating speed was monitored by the encoder's odometer, and the travel speed was maintained at 0.5 m/s. Figure 17: Extraction process of job path. (**a**) Outline drawing of the feed band; (**b**) extraction of the feed band; (**c**) path extraction; (**d**) projection of the path on the RGB image. To verify the performance of the proposed vision-based navigation path-tracking extraction system, field tests were performed using the pusher robot designed in this study. The laser ranging module (measurement range is 0.03-40 m, accuracy \(\pm\)1 mm) was installed in the center and tail of the pusher robot, and the center height of all laser transmitters was consistent with the center height of the horizontal fence to ensure the accuracy of the measurement results. The algorithm proposed was used to extract the path and control the robot to perform the actual operation. The lateral deviation (D) and angular deviation (\(\theta\)) of the pusher robot could be obtained by calculating the difference between the actual and theoretical operation route. The performance of the navigation path extraction algorithm proposed was evaluated by D and \(\theta\). Therefore  
pusher robot was set up to operate autonomously along the extraction navigation path with a working distance of 50 m. Figure 19 shows the lateral deviation from the different D values. It was found from Figure 17 that when the initial lateral distances were +0.1, \(-\)0.1 and 0 m, the maximum lateral deviation between the actual navigation path and the target path was less than 12, 14 and 11 cm, respectively. The absolute deviations of the three initial lateral distances of the pusher robot were all within 15 cm. Although there were some deviations between the actual navigation route and the target route, there was no serious deviation. At the same time, all three groups of experiments were able to complete autonomous navigation and reach the end point safely. This proved the stability of the path extraction and motion control system under different initial lateral errors. In the tests of path extraction and motion control system accuracy, the five barn work areas were randomly selected and the robot was placed at the beginning of each work area. The path of the pusher robot was measured by the laser ranging module installed in the middle of the robot. The distance between the pusher robot and the fence was measured and recorded every time the robot advanced 0.5 m, and the D value of the pusher robot was obtained by calculating the difference between it and the theoretical operating route. The statistical results are shown in Table 1.  
Table 1 shows the errors generated by the path extraction and motion control systems in actual operation. It can be seen from Table 1 that the maximum lateral deviation in the five different test areas was 8.9 cm, and the average maximum lateral deviation was 7.6 cm; the largest average lateral deviation was 4.8 cm, and all the average lateral deviations were 3.5 cm. This showed that the pusher robot could operate stably without affecting the feeding of the dairy cows.