Abstract
Robotics will significantly impact large sectors of the economy with relatively low productivity, such as Agri-Food production. Deploying agricultural robots on the farm is still a challenging task. When it comes to localising the robot, there is a need for a preliminary map, which is obtained from a first robot visit to the farm. Mapping is a semi-autonomous task that requires a human operator to drive the robot throughout the environment using a control pad. Visual and geometric features are used by Simultaneous Localisation and Mapping (SLAM) Algorithms to model and recognise places, and track the robot’s motion. In agricultural fields, this represents a time-consuming operation. This work proposes a novel solution—called AgRoBPP-bridge—to autonomously extract Occupancy Grid and Topological maps from satellites images. These preliminary maps are used by the robot in its first visit, reducing the need of human intervention and making the path planning algorithms more efficient. AgRoBPP-bridge consists of two stages: vineyards row detection and topological map extraction. For vineyards row detection, we explored two approaches, one that is based on conventional machine learning technique, by considering Support Vector Machine with Local Binary Pattern-based features, and another one found in deep learning techniques (ResNET and DenseNET). From the vineyards row detection, we extracted an occupation grid map and, by considering advanced image processing techniques and Voronoi diagrams concept, we obtained a topological map. Our results demonstrated an overall accuracy higher than 85% for detecting vineyards and free paths for robot navigation. The Support Vector Machine (SVM)-based approach demonstrated the best performance in terms of precision and computational resources consumption. AgRoBPP-bridge shows to be a relevant contribution to simplify the deployment of robots in agriculture.
Highlights
Agriculture is among the most critical sectors of the global economy
The training time with the Support Vector Machine (SVM) tool takes less than one minute, while, with the deep learning tool, this time can reach several hours, even with the process parallelized in a GPU
The proposed work presented an approach to deal with big dimensions of agricultural terrain in robotic path planning
Summary
Agriculture is among the most critical sectors of the global economy. The sector has been adapted along years to fulfil the worlds population demand, which has doubled in the last 50 years [1]. Demarcated Region (Portugal), UNESCO Heritage place), obtaining a preliminary map is critical These scenarios present several challenges to autonomous robot navigation: Global Navigation Satellite. To obtain a preliminary map and solve AgRobPP memory requirements, this work contribution proposes a novel solution called AgRobPP-bridge, with two stages: AgRob Vineyard Detector and. The first stage performs vineyard rows detection from satellite images, which will provide a pre-map of the farm for the robot’s first visit, reducing the human operator need. This tool is based on a Support Vector Machine (SVM) classifier approach.
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