Development of Topological Mappings for Autonomous Agricultural Vehicles

Automation system of agricultural crop plantation requires many subsystems such as low level tracking, path planning, obstacle detection, manoeuvres at the path terminations, etc. This study proposes semantic annotation for the information flow between the automation subsystems, filling the gap between the planning and implementation of crop production by developing two missing subunits: determination of obstacles that may threaten agricultural vehicles using the satellite images of target field, and determination of proper path for the agricultural vehicles to process rows of crops. For the attributes of obstacles, semantic annotation on the map of target field is preferred using Resource Description Framework/Extensible Mark-up Language (RDF/XML) in order to be exchangeable and reusable with other stages, systems, devices and applications. Developed Matlab code determines the target field by a GPS coordinate inside the field. An interactive initialization stage provides download of the satellite images from Google Maps API for determination of the field boundaries. The code for detection and positioning of the circular shaped obstacles are using Prewitt, Sobel, Roberts, and Canny edge detection, and Hough transformation algorithms. The developed method is tested on 51 target fields. It provides 45% improvement in detection error rate compared to raw application of the algorithms. Keywords: Image processing, Obstacle detection, Path planning, Semantic annotation, RDF/XML mapping.