# Utilization of Quasi-Zenith Satellite System for Navigation of a Robot Combine Harvester
## 4 Discussion
### Performance Evaluation of Robot Combine Harvester  
The logged position data of the robot combine harvester in Figure 8 shows that there are oscillations after the combine harvester finishes the turning maneuver and enters the straight path. These oscillations are caused by utilization of the FDS with steering wheel for turning. However, the robot combine harvester autonomous navigation using both CLAS and MADOCA could return to the straight travel path along the predesigned navigation map. From Figure 8, it can be concluded that it is possible to use either of these positioning augmentation systems for the robot combine harvester autonomous navigation. However, Table 4 results shows that the MADOCA performance is poor for the first experimental run. An RMSE of 0.32 m suggests that is not possible to use this positioning augmentation technique for the robot combine harvester. The MADOCA performance improves in the second run and it gets better in the third run because the MADOCA positioning augmentation system requires at least two hours for stabilization, as evidenced in the data shown in Figure 7b. Therefore, this positioning augmentation system is not suitable for the practical function of the robot combine harvester. It is not practical to have an agricultural robot waiting in the field for two hours to complete a harvest task that might take one hour for a small field. The results summarized in Table 4 are consistent to the results summarized in Table 3 and the data displayed in Figure 7.  
Results summarized in Tables 5 and 6 suggest that when using either CLAS or MADOCA it is necessary to consider the bias compensation for each augmentation system. The overlap between each path of the predesigned navigation map should be set to 0.09 m when using either CLAS or MADOCA. However, this overlap should be updated periodically due to the earth's crustal movement for compensation of biases between the RTK and either CLAS or MADOCA. The logged position data of the RTK shown in Figure 9 by the solid dark line displays a bigger bias respect to the map points represented by the solid clear line in comparison to both CLAS and MADOCA used as inputs in the automatic controller. This is because the RTK logged position data are more accurate for both the static and dynamic positioning experiments. The automatic controller tries to follow the map points according to the position provided by either CLAS or MADOCA, but it cannot tell the difference between one or another respect to the real path. Therefore, the logged position data of the RTK represent the actual path that the robot combine harvester ran. The position data from the second path were logged after finishing the turning maneuver of the first path. It can be observed in Figure 9 that by using the CLAS to control the robot combine harvester, the position data provided by the CLAS converges to almost the same position data provided by the RTK positioning augmentation system. In contrast, the positioning data provided by the MADOCA diverges considerably respect to the position data provided by the RTK while using the MADOCA to control the robot combine harvester. This situation occurs because the MADOCA position augmentation system is a time dependent system; which means it requires several hours to achieve an acceptable control performance. The lateral deviation was calculated by the comparison between the map points and the logged position data from each positioning augmentation system and the RTK. Therefore, the CLAS and the RTK have a smaller RMSE of the lateral deviation in comparison to the MADOCA and the RTK. Considering the bias observed in Figure 9 and the data summarized in Table 5, it is possible to conclude that the RMSE of the lateral deviation of the CLAS is stable. However, it is necessary to consider the bias. This bias is caused by the earth's crustal movement consideration, which the CLAS neglects.