This study presents a new path-planning framework for precision agriculture, designed for hedgerow systems, which combines cutting-edge technology and data analysis to enhance crop management in light of climate chang...
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This study presents a new path-planning framework for precision agriculture, designed for hedgerow systems, which combines cutting-edge technology and data analysis to enhance crop management in light of climate change challenges. The framework creates detailed digital field models by employing Unmanned Aerial Vehicles (UAVs), or drones, either with high-precision LiDAR or Structure-from-Motion (SfM) data. Then, these models are inputs for the pathplanning algorithm, crucial for directing drones on the most efficient paths for surveys or spraying. The key feature is its ability to adjust to the specific conditions of agricultural fields, considering the current biophysical environment, ensuring paths are closely aligned with crop rows and adapting to vegetation changes. This leads to significant efficiency improvements, especially in cases of irregular row spacing or heterogeneous vegetation, achieving paths up to 40% shorter than traditional geometry-based methods. The effectiveness of the algorithm relies on the quality of input data, with LiDAR being recommended due to its higher accuracy despite its longer processing time. Field tests were conducted in a vineyard in Spain to validate the effectiveness of the framework. Integrating drone technology with precise routing and high-quality data, the proposed framework can potentially enhance the sustainable and efficient management of woody crops.
This study presents a metaheuristic approach to coveragepathplanning for ground-based forest operations, focusing on minimizing path lengths for forest vehicles while considering terrain characteristics and vehicle ...
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This study presents a metaheuristic approach to coveragepathplanning for ground-based forest operations, focusing on minimizing path lengths for forest vehicles while considering terrain characteristics and vehicle parameters. Forest vehicles can usually tolerate higher pitch than roll angles, which makes them vulnerable to rollover. To mitigate that, this method utilizes a genetic algorithm to optimize the sequence of nodes, which are scattered over the site with equal spacing. The coveragepath planner then calculates the Dubins path distance between every node in the fitness function, together with penalties for exceeding pitch, roll and soil moisture constraints for the vehicle. This ensures that the path planner tries to make the most traversable path as possible, while trying to minimize the driving distance. Two synthetic test sites resembling primitive challenging terrains, and one real site were utilized to theoretically evaluate the proposed method. The results show that aligning the node patterns with the critical slope headings, instead of having a straight pattern, had little effect on the path length. However, square grids can yield shorter paths across multiple runs, while triangular grids ensure consistent results in single runs. A two-hectare site took 43 minutes to calculate on average. This suggests that further development of the path planner could lead to significant improvements, enabling the management of sites larger than a few hundred nodes. However, the calculation time is justified for the reduced path length during deployment. The study presents a methodology that supports manual operators and establishes foundations for full autonomy.
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