# Target Points Tracking Control for Autonomous Cleaning Vehicle Based on the LSTM Network
## 1 Introduction  
With the continuous development of advanced technologies such as Information Technology (IT), sensor technology, system engineering, and artificial intelligence, Intelligent Transportation System (ITS) has attracted more and more researchers' attention, especially the design of automatic mobile robots and mobile systems [1]. One of the main hotspots in the field of autonomous mobile robots research is to design a target point tracking controller [2; 3; 4; 5], which focuses on reaching the pre-defined point through longitudinal and lateral control of the vehicle [6], thus achieving a whole path points tracking in the given time. To be specific, the longitudinal controller is responsible for adjusting the vehicle's cruise velocity while the lateral controller steers the vehicle's wheels for trajectory tracking [7; 8]. Therefore, the resulting control output should minimize the difference between the actual and expected points with respect to the lateral distance and the vehicle heading, and should ensure that the actual paths are smooth. However, for automatic cleaning robot, there is a strong coupling relationship between the longitudinal and lateral dynamics of the vehicle whose dynamic response is very complicated during the cleaning process, because its running involves sprinkling, vacuuming and scrubbing [9]. So the kinematic or dynamic model of the cleaning vehicle is complex [10], and to determine an appropriate target points tracking control algorithm is difficult. At present, many scholars have studied target points tracking methods based on varied types of models such as geometric, kinematic and dynamic model or combination of them. Compared with the controller based on complex dynamic model, the controllers based on geometry and motion models are easier to implement and more robust to path curvature. In addition, the latter are more suitable for lower velocity driving because they do not consider the internal force of the whole vehicle system. However, the tracking accuracy of the controller based on dynamic model is usually better at higher speed, because the controller gives priority to the non-linear vehicle dynamic. Existing tracking methods concretely include PID control [11], fuzzy control [12; 13], optimal control [14], pure pursuit control [15], neural network control [16; 17], and so on. They improve the goal points tracking performance for automatic vehicles in different aspects. In [11], a classical PID approach is proposed for the path tracking controller, which utilizes a simple linearized model consisting of an integrator and a delay to simulate a mobile robot. This experimental results demonstrate the good tracking performance and robustness of the proposed controller. The advantage of fuzzy control is that it does not require precise kinematics model or dynamic model for robots, reference [12] combines the linear regulator theory with Takagi-Sugeno fuzzy methodology and proposes a method to fulfil path tracking of nonlinear systems. It is worth mentioning that this method is easy to implement due to the use of linear control techniques. Reference [14] requires the vehicle to run at low speed, so the dynamic properties have little influence on the path tracking behavior. In [14], an optimal controller is designed to minimize the orientation error and the offset from a path. The controller structure is defined in terms of the running speed of the vehicle. Thus, it has the superiority of being flexible for speed changes, but the simulation or experiment performances of the controller are not specifically described in the text. Pure pursuit control [15] presents a steering control law for automatic vehicles according to the geometric relation between the ego-vehicle and the desire tracking path points. A parameter named preview distance is usually used to obtain the control variables and it has certain potentially abilities to extend from simple circular arc calculations to much more complex geometric theorems.