An Efficient Guiding Manager for Ground Mobile Robots in Agriculture

Abstract

Mobile robots have become increasingly important across various sectors and are now essential in agriculture due to their ability to navigate effectively and precisely in crop fields. Navigation involves the integration of several technologies, including robotics, control theory, computer vision, and artificial intelligence, among others. Challenges in robot navigation, particularly in agriculture, include mapping, localization, path planning, obstacle detection, and guiding control. Accurate mapping, localization, and obstacle detection are crucial for efficient navigation, while guiding the robotic system is essential to execute tasks accurately and for the safety of crops and the robot itself. Therefore, this study introduces a Guiding Manager for autonomous mobile robots specialized for laser-based weeding tools in agriculture. The focus is on the robot’s tracking, which combines a lateral controller, a spiral controller, and a linear speed controller to adjust to the different types of trajectories that are commonly followed in agricultural environments, such as straight lines and curves. The controllers have demonstrated their usefulness in different real work environments at different nominal speeds, validated on a tracked mobile platform with a width of about 1.48 m, in complex and varying field conditions including loose soil, stones, and humidity. The lateral controller presented an average absolute lateral error of approximately 0.076 m and an angular error of about 0.0418 rad, while the spiral controller presented an average absolute lateral error of about 0.12 m and an angular error of about 0.0103 rad, with a horizontal accuracy of about ±0.015 m and an angular accuracy of about ±0.009 rad, demonstrating its effectiveness in real farm tests.

Potential of Eco-Weeding with High-Power Laser Adoption from the Farmers’ Perspective

“…Moreover, digitalization increases economic, social, environmental, and geopolitical resilience. Machines enable the digital transformation of agriculture by using sensors on machines to detect actual soil and crop information (weed recognition, amount of biomass, nutrient status, pests, and diseases), which allows for mapping to ensure the successful control of variable rate applications, communication protocols, and cloud connectivity to facilitate data flow [2,[9][10][11][12][13][14][15]. There is an increasing role that knowledge and information can play in gaining control of resources, increasing profits, and reducing risk in farming [16,17].…”