1. DeepWay: A Deep Learning waypoint estimator for global path generation
- Author
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Marcello Chiaberge, Diego Aghi, Vittorio Mazzia, and Francesco Salvetti
- Subjects
0106 biological sciences ,FOS: Computer and information sciences ,Computer Science - Machine Learning ,Occupancy grid mapping ,Computer Science - Artificial Intelligence ,Computer science ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition ,Context (language use) ,Horticulture ,01 natural sciences ,Machine Learning (cs.LG) ,Computer Science - Robotics ,Waypoint ,FOS: Electrical engineering, electronic engineering, information engineering ,Precision agriculture ,business.industry ,Deep learning ,Image and Video Processing (eess.IV) ,Forestry ,Robotics ,04 agricultural and veterinary sciences ,Unmanned ground vehicles ,Electrical Engineering and Systems Science - Image and Video Processing ,Industrial engineering ,Automation ,Global path planning ,Computer Science Applications ,Artificial Intelligence (cs.AI) ,Deep learning, Unmanned ground vehicles, Precision agriculture, Global path planning ,Path (graph theory) ,040103 agronomy & agriculture ,0401 agriculture, forestry, and fisheries ,Artificial intelligence ,business ,Agronomy and Crop Science ,Feature learning ,Robotics (cs.RO) ,010606 plant biology & botany - Abstract
Agriculture 3.0 and 4.0 have gradually introduced service robotics and automation into several agricultural processes, mostly improving crops quality and seasonal yield. Row-based crops are the perfect settings to test and deploy smart machines capable of monitoring and manage the harvest. In this context, global path generation is essential either for ground or aerial vehicles, and it is the starting point for every type of mission plan. Nevertheless, little attention has been currently given to this problem by the research community and global path generation automation is still far to be solved. In order to generate a viable path for an autonomous machine, the presented research proposes a feature learning fully convolutional model capable of estimating waypoints given an occupancy grid map. In particular, we apply the proposed data-driven methodology to the specific case of row-based crops with the general objective to generate a global path able to cover the extension of the crop completely. Extensive experimentation with a custom made synthetic dataset and real satellite-derived images of different scenarios have proved the effectiveness of our methodology and demonstrated the feasibility of an end-to-end and completely autonomous global path planner., Comment: Submitted to Computers and Electronics in Agriculture
- Published
- 2021