Design and grey wolf optimization algorithm based intelligent control of a four wheeled mobile agriculture robot

Agricultural robots have been widely used in agricultural applications in recent years, with the development of industry 4.0. In this direction, in this study, a mobile agricultural robot design was made that allows the insemination process in the agricultural sector and other related activities to be collected in a single agricultural machine and integrated into the automation and industry 4.0 system. With the design, it was emphasized that the robot can adapt to the generally rough terrain conditions, and the static, dynamic and kinematic analyzes of the mobile robot, which has excavation, seeding and irrigation subsystems, and the most suitable design that is compatible with environmental conditions, durable and able to fulfill the given tasks. In addition, a four-wheeled mobile robot is mathematically modeled and its trajectory tracking performance is tested under different conditions. In the mathematical model extraction phase, the kinematics, inverse kinematics and dynamic model of the robot were obtained. While obtaining the dynamic model, Lagrange Energy Method was used. A controller design has been made to determine the voltages to be fed to the DC motors on the 4 wheels of the robot that is required to follow the given reference trajectory. In the system where PID controller is used for four different motors, the parameters of the controllers are adjusted based on the gray wolf optimization algorithm. According to the results obtained, it is seen that the designed robot successfully fulfills the given tasks. In line with the values entered from a smart device, it performs digging, planting, closing and irrigation operations on the x and y axes at certain intervals. It follows the given trajectory successfully and the planting process is carried out in the specified places. From the simulation results, it is seen that the designed gray wolf optimization based PID controller gives successful results for different trajectories.