Weed-removal system based on artificial vision and movement planning by A* and RRT techniques

Abstract

The recent exploration of autonomous robotics tasks in agro-industry has permitted the integration of theories of artificial vision and mobile robotics with tasks in precision agriculture. Artificial vision allows for the classification of weeds and crops from images of plantations. With 3D-image processing systems, the location of the weeds is determined, and then the movement of the tool responsible for eradication is proposed. This article presents the solution for finding weeds within a crop field using classifiers and the integration of a 3D-vision system that builds a point cloud featuring the plants to safeguard, the weeds and the free space using Zed technology. With this information, search techniques such as A* (A star) and RRT (Rapidly exploring Random Tree) are used to determine the trajectory that the weed-removal tool must follow. The last feature is an integral part of an XYZ-positioning system, and this is part of a mobile robot dedicated to precision agriculture tasks.

Keywords

• mobile robotics
• precision agriculture
• weed recognition
• weed removal
• trajectory planning.