3D hybrid path planning for optimized coverage of agricultural fields: A novel approach for wheeled robots

This article presents a new hybrid approach (H-CCPP) for optimizing coverage path planning for agricultural robots. Unlike previous methods, H-CCPP improves processing speed, explores diverse driving directions, and integrates row-skip patterns for both simple and complex fields. It automatically determines entry and exit points and was rigorously evaluated on a dataset of 30 French fields, showing superior performance in minimizing slope costs while maintaining efficiency in other key metrics. Future research will focus on improving headland coverage, integrating real-world experiments, handling obstacles, and optimizing multirobot coordination.

Over the last few decades, the agricultural industry has made significant advances in autonomous systems, such as wheeled robots, with the primary objective of improving efficiency while reducing the impact on the environment. In this context, determining a path for the robot that optimizes coverage while taking into account topography, robot characteristics, and operational requirements, is critical. In this paper, we present H-CCPP, a novel hybrid method that combines the comprehensive coverage benefits of our previous approach O-CCPP with the computational efficiency of the Fields2Cover algorithm. Besides optimizing coverage area, overlaps, and overall travel time, it significantly improves the computation process, and enhances the flexibility of trajectory generation. H-CCPP also considers terrain inclination to address soil erosion and energy consumption. In an effort to support this innovative approach, we have also created and made available a public data set that includes both 2D and 3D representations of 30 agricultural fields. This resource not only allows us to illustrate the effectiveness of our approach but also provides invaluable data for future research in complete coverage path planning (CCPP) for modern agriculture.