In robot navigation experiment, a localization and motion control system is required to secure the agricultural robot motion in the environment. However, the high cost of localization system and complex structure of motion controller has limited the low cost agricultural mobile robot development. In this paper, a low-cost localization system and simple motion control system is presented. The localization system has been implemented using a dead reckoning method by accessing an incremental encoder’s reading. A simple cascaded motion control system based on proportional feedback kinematics controller and PI based controller was used to control the mobile robot motion. The performances of different turning methods: U turn and a π turn, were compared for lane changing, based on completion time, controller’s error and distance travelled. Simulation test of robot motion was conducted using a Simulink3d animation in MATLAB software. An experimental test in a real greenhouse environment was conducted to verify the simulation performance in motion control and localization system. The experimental and simulation results have shown that a U turn has the best turning performance with 69.1 % better efficiency in experimental mode and it is recommended to be applied in agricultural field.
A Deep Learning Approach for the Mobile-Robot Motion Control System
