# RBF-Based Monocular Vision Navigation for Small Vehicles in Narrow Space below Maize Canopy
## 3 Experimental and Discussion  
### Laboratory Tests  
This study conducted laboratory tests and field trials to test the performance of the robot in terms of the path detection and planning system. Laboratory tests were conducted at the Chinese Academy of Sciences under different path characteristics. RBF path planning simulation was conducted, and the verification results are obtained as shown below. In Figure 10, the front of the vehicle, both left and right sides, are placed with separate columns, and the columns of the same row have a spacing of 10 cm to 15 cm. The spacing between two rows is in a range of 65, 70, and 75 cm, and the rows are intentionally placed in a straight line path, left-turn, right-turn, and S-turn. The vehicle speed is set at 0.3-1.3 m/s with a velocity increment of 0.2 m/s. Any instance of the vehicle crashing into a post, as it  
Figure 9: Comparison between centerline extraction method and RBF for crops outside the crop rows, (**a**) experimental scenario; (**b**) result of centerline extraction algorithm; and (**c**) result of RBF.  
passes through the post area at various speeds, is recorded, and the speed of path planning is analyzed and optimized.  
Figure 11 shows the simulation results of the RBF method for straight line, left-turn, and S-turn. It is easy to obtain in a non-linear case; the vehicle path planning by the RBF method was more reasonable for collision avoidance in a confined space.  
A number of simulations were conducted in the laboratory to demonstrate the applicability of the approach in different shapes of crop rows, such as straight line, left-turned, right-turned, and S-bent. All of the paths are accessible, with a width of 65-75 cm, and the vehicle has an operating width of 60 cm. As listed in Table 1, after correcting the direction of the body, almost no extra steering action occurs in the straight path, a speed of 0.3-1.3 m/s is successfully achieved. However, in the left-turned and right-turned paths, increasing the speed increases the chances of a collision. In addition, the results  
Figure 11: Results of path planning by RBF.  
Figure 10: Laboratory tests: (**a**) straight line; (**b**) right-turn; (**c**) S-turn; and (**d**) the display of the front camera.  
of RBF are more significantly improved than those of Hough transform in an S-bent path. In particular, when the turning radius changes, the calculations for the front and rear frames do not always fully coincide, resulting in deviations in the execution of the vehicle, which are likely to cause collision with the columns. Overall, in the case of a changing turning radius, the rate of vehicle and column collision is below 27% under the RBF algorithm.  
In response to these four scenarios, respectively, the Hough transform algorithm and simplified RBF algorithm performed each plan 10 times, and the average search time and the minimum distance of the boundary results were compared. As shown in the Table 2 below, calculations using the Hough transform time are faster than the average RBF by about 3-5 ms; however, the minimum distance to the boundary of the RBF is significantly better than the results of Hough transform.