This study proposed an approach for robot localization using data from multiple low-cost sensors with two goals in mind, to produce accurate localization data and to keep the computation as simple as possible. The approach used data from wheel odometry, inertial-motion data from the Inertial Motion Unit (IMU), and a location fix from a Real-Time Kinematics Global Positioning System (RTK GPS). Each of the sensors is prone to errors in some situations, resulting in inaccurate localization. The odometry is affected by errors caused by slipping when turning the robot or putting it on slippery ground. The IMU produces drifts due to vibrations, and RTK GPS does not return to an accurate fix in (semi-) occluded areas. None of these sensors is accurate enough to produce a precise reading for a sound localization of the robot in an outdoor environment. To solve this challenge, sensor fusion was implemented on the robot to prevent possible localization errors. It worked by selecting the most accurate readings in a given moment to produce a precise pose estimation. To evaluate the approach, two different tests were performed, one with robot localization from the robot operating system (ROS) repository and the other with the presented Field Robot Localization. The first did not perform well, while the second did and was evaluated by comparing the location and orientation estimate with ground truth, captured by a hovering drone above the testing ground, which revealed an average error of 0.005 m±0.220 m in estimating the position, and 0.6°±3.5° when estimating orientation. The tests proved that the developed field robot localization is accurate and robust enough to be used on a ROVITIS 4.0 vineyard robot. Keywords: localization, odometry, IMU, RTK GPS, vineyard, robot, sensors fusion, ROS, precision farming DOI: 10.25165/j.ijabe.20221506.6415 Citation: Rakun J, Pantano M, Lepej P, Lakota M. Sensor fusion-based approach for the field robot localization on Rovitis 4.0 vineyard robot. Int J Agric & Biol Eng, 2022; 15(6): 91–95.

The concept of precision farming is wide, and it represents the efficiency which is achieved with the help of precision. For the navigation of field machines, the RTK (Real Time Kinematic) navigation is needed. In order to verify the positive effects in practice, RTK navigation system equipped with Fentd 828 was applied to test the width of overlap, and fuel and time that could be saved compared with manual driving. The experiment was conducted on two areas of land size of 172 m × 58 m with two working machines width 3 m and 6 m. Results indicated that 15.7% of the time and 8.66% of the fuel were saved on a working machine of 3 meters width, and 12.6% of the time and 8.28% of the fuel were saved on a working machine of 6 m width. The width of the overlap represent 10% of the working width of the machine, and with the method of turning, which RTK navigation allows, additional time was saved. Ecological footprint, CO2 emissions and global warming potential (GWP) was estimated under different guiding systems. The largest footprint was related to manual tillage with 3 m width working machine, while estimation on CO2 (kg) emissions and GWP obtained the same result. The use of precision agriculture technologies allows better planning and analyzing of working procedures. The air, water and soil pollution are less intensive. Keywords: precision farming, RTK technology, ecological footprint, environmental pollution, tillage DOI: 10.25165/j.ijabe.20191205.4932 Citation: Kelc D, Stajnko D, Berk P, Rakun J, Vindiš P, Lakota M. Reduction of environmental pollution by using RTK-navigation in soil cultivation. Int J Agric & Biol Eng, 2019; 12(5): 173–178.

In this chapter, we present and evaluate three different infield navigation algorithms, based on the readings from a LIDAR sensor. All three algorithms are tested on a small field robot and used to autonomously drive the robot between the two adjacent rows of maze plants. The first algorithm is the simplest one and just takes distance readings from the left and right side. If robot is not in the center of the mid-row space, it adjusts its course by turning the robot in the right direction accordingly. The second approach groups the left and right readings into two vertical lines by using least-square fit approach. According to the calculated distance and orientation to both lines, it adjusts the course of the robot. The third approach tries to fit an optimal triangle between the robot and the plants, revealing the most optimal one. Based on its shape, the course of the robot is adjusted. All three algorithms are tested in a simulated (ROS stage) and then in an outdoor (maze test field) environment comparing the optimal line with the actual calculated position of the robot. The tests prove that all three approaches work with an error of 0.041 ± 0.034 m for the first algorithm, 0.07 ± 0.059 m for the second, and 0.078 ± 0.055 m error for the third.

Purpose: This study introduces an optimized algorithm for autonomous navigation of field robots, aiming to improve navigation accuracy, reduce crop damage and shorten execution times in agricultural environments. Methods: The enhanced solution integrates advanced data filtering with sensor fusion techniques, combining LiDAR and IMU inputs to produce precise 3D point cloud representations for reliable navigation in structured crop rows. Both the legacy and improved algorithms were evaluated through simulation and physical trials on the FarmBeast robotic platform. Results: The improved algorithm reduced traversal time by up to 33% on certain field sections and lowered crop damage by 25% compared to the previous version. Conclusions: Results confirm the robustness and effectiveness of the enhanced navigation system in complex agricultural field conditions, demonstrating its potential for practical deployment within farming automation.