Predictive tracking and control method for vision-guided navigation of agricultural robot

To avoid the unfavorable working conditions and guarantee the sustainability of agriculture, it is widely thought that the wheeled mobile robot guided by machine vision, substituting for conventional tractors in some farming activities, will play an important role in the future. The time lag, however, produced mainly by robot vision system and the other signal processing will exert some negative effects on the autonomous navigation of the wheeled mobile robot. Based on Kalman filtering, the measurements of all sensors were fused and a predictive control method was developed skillfully to overcome it. Then the kinematical behavior of the wheeled mobile robot was analyzed in detail, and the corresponding nonlinear stochastic model equation and the observation equation were set up respectively. Both the simulation and the initial experiment show that this method is effective for the visual navigation of the wheeled mobile robot in the field.