The paper deals with comparing electricity power consumption of various control algorithms by simulating differential mobile robot motion control in a vineyard row. In field of autonomous mobile robotics, the quality of control is a crucial aspect. Besides the precision of control, the energy consumption for motion is becoming an increasingly demanding characteristic of a controller due to the increasing costs of fossil fuels and electricity. A simulation model of a differential drive mobile robot motion in a vineyard row was created, including robot dynamics for evaluating motion consumption, and there were implemented commonly used PID, Fuzzy, and LQ control algorithms, the task of which was to navigate the robot through the centre of vineyard row section by measuring distances from trellises on both robot sides. The comparison was carried out using Matlab software and the best results in terms of both power consumption and control accuracy were achieved by LQI controller. The designed model for navigating the robot through the vineyard row centre and optimized controllers were implemented in a real robot and tested under real conditions.

The main contribution of the paper is the simplification of the computational process of fuzzy control of a mobile robot controlled by a microcontroller. We present a way to implement this control method with a reduced computation time of control actions and memory demand. Our way to accomplish this, was to replace the fuzzy controller with the approximation of its resulting control surfaces. In the paper, we use the previously presented approximation by the table and describe other methods of approximation of the control area through polynomial and exponential function. We tested all approximation methods in simulations and with a real mobile robot. Based on the measured trajectory of the EN20 mobile robot, we found that approximation through the table is the most accurate in terms of the fuzzy surface but delivers noticeable oscillations of mobile robot control in real conditions. Polynomial and exponential functions fuzzy surface approximations were less accurate than the table, but provide smoother control based on robot trajectories and are much more appropriate in terms of microcontroller implementation due to lower demand on memory.