# Target Points Tracking Control for Autonomous Cleaning Vehicle Based on the LSTM Network
## 4 Experiment and Analysis
### Field Experiment of Cleaning Vehicle  
In order to test the actual working effect of this algorithm, the cleaning vehicle shown in Figure 8 is used in the field test. This cleaning vehicle is equipped with the laser radar and ultrasonic sensors. Its body length is 1.5 m and the wheelbase of two driving wheels is 1 m. There is a cleaning device in front of the vehicle and two driving wheels in the rear. Based on the practical experience and security factors, this machine is designed to have a speed of no more than 1 m/s. Figure 6: (**a**) Target points tracking performance on the straight path. (**b**) Tracking lateral error of straight path points under four different control schemes. Figure 7: (**a**) Target points tracking performance on the path with multiple curvature. (**b**) Tracking lateral error under the path with multiple curvature. The field experiment is carried out in the subway station hall with the length of 30 m and the width of 3.8 m. It is necessary to manually specify the running route (pre-defined path) before the experiment by pushing the cleaning vehicle. The total length of running route is 197.3 m, the initial position of the vehicle is (0, 0), the initial yaw angle is 0\({}^{\circ}\), and the initial speed is 0 m/s. The dark blue curve in Figure 9 is the specified running route. In practice, an autonomous cleaning vehicle has to work on the paths with various shapes, so it needs a very robust tracking controller. Rapid changing path points require a re-computation of the speed and the yaw rate. Because the effect of traditional pure pursuit algorithm and backstepping algorithm control are often poor, these two methods are not tested in the field experiment. The target points tracking diagram is displayed in Figure 10, it can be seen that the proposed algorithm has more advantages than the improved pure pursuit algorithm, especially in the turning path and encountering the noise points. The proposed controller can track the reference path well in straight lines as well as corners, freedom from the impacts of noise points. However, the improved pure pursuit controller has large deviation at turning and it is easily disturbed by noise points. There may be a phenomenon that the cleaning vehicle does not follow the reference path when the noise point is too close to the real path. Besides, the cleaning vehicle can run or stop smoothly near the start point and the end point by adjusting the control parameters in Figure 10a. But in Figure 10b, noise points near the starting point affect the tracking process in the initial stage. In the final stage, the vehicle tracking ends ahead of schedule, because the preview distance and speed cannot be changed adaptively. Therefore, the field experimental results indicate that the proposed controller is robust and effective for the autonomous cleaning robot to track the pre-defined path points. Figure 8: Front view of the cleaning vehicle. Figure 9: Experimental background and pre-defined path, top down view. The Figures 11 and 12 show the velocity and foresight distance variations of the two algorithms in tracking. The proposed controller is capable of adjusting the cleaning vehicle's speed and the foresight distance intelligently. Throughout this process, these two values dropped observably on the corners. It is important to note that the LSTM network has learned the characteristics of the sample data. And the speed, as well as foresight distance when the vehicle walking against the wall (Saying that in approximately 0-50 s), are significantly less than the speed as well as foresight distance when not walking against the wall, except in the case of turning.