KEYWORDS: Systems modeling, Agriculture, Vehicle control, Unmanned ground vehicles, Control systems design, Control systems, Applied research, Unmanned vehicles, Process control, Photonics
Unmanned ground vehicles (UGVs) will be widely adopted in agricultural applications. To accomplish autonomous cruising in farm, path following is an essential skill. However, in the process of field cruising, some obstacles such as wild animals or motorcycles are present. In this study, tracked vehicles are utilized with deep deterministic policy gradient (DDPG) compensating for model uncertainties and achieving collision avoidance simultaneously. Among all, the most important issue is to keep the UGV following the predetermined path in specific agricultural field environment and coping with the uncertainty of the surroundings. Path following and obstacle avoidance of field tracked vehicles are conducted by using model predictive control (MPC) with a controller (agent) trained by DDPG. Therefore, we proposed control algorithm fusion with MPC and model-free DDPG.
KEYWORDS: Robots, Kinematics, Visual process modeling, RGB color model, System identification, Simulink, Visualization, Cameras, Control systems, Actuators
Model predictive control (MPC) with prediction and control horizons under multivariable constraints can prompt field tracked vehicles to follow the reference path accurately. However, a kinematic model or a classic dynamic model of a vehicle is needed in MPC, and both of them must be linearized and hence the computation cost is large. Also, the parameters of a classic dynamic model are difficult to be measured. In this paper, system identification approach for estimated the linear state-space dynamic model of a field tracked vehicle in farm has been utilized. The dynamic model has been identified with more than 50% estimated fitting. Using the dynamic model, a linear MPC can be adopted, and hence the computation can be saved more than 2/3, compared with the conventional nonlinear MPC with a kinematic model. Furthermore, the tracked vehicle adopted the linear MPC with the dynamic model can achieve superior S-curve and L-shape path following.
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