Three-dimensional area coverage planning model for robotic application
Mahdi Vahdanjoo, René Gislum, Claus Aage Grøn Sørensen
Abstract
Due to the increasing global population, the supply of nonrenewable resources such as fossil fuels are limited. Therefore, it is urgent to implement measures to control and reduce energy consumption in agriculture and other sectors. Improving energy efficiency and reducing pollution in agriculture can bring both financial and environmental benefits. By optimizing in-field coverage planning of agricultural vehicles, fuel consumption can be reduced. Recent developments in automated coverage planning algorithms, both two-dimensional and three-dimensional, aim to optimize and automate operations. The elevation property of agricultural fields has a significant impact on coverage path planning, which means that 3D coverage path planning can be highly advantageous for field operations. In this study, a 3D coverage path planning model was developed that effectively determines the optimal driving direction in a field based on energy consumption criteria. A digital elevation model (DEM) was utilized to construct an analytical model that provides improved characterization of the topographical features of three-dimensional landscapes. The aims of this paper encompass the formulation and execution of a 3D coverage planning strategy for material input operations that aims to reduce energy consumption, reduce overall traveled distance, generate a map to display variable application rates for fertilizers using remote sensing techniques, and the evaluation of soil compaction risks in an agricultural field. The results of this study demonstrate the effectiveness of our optimization model. In the first sample field, the model was able to generate a solution that reduced the required energy by up to 9.5%, while in the second sample field, it achieved a reduction of up to 15.6% in required energy. Overall, this study highlights the importance of considering multiple objectives in coverage path planning and demonstrates how our proposed optimization model can contribute to significant energy savings and reduced environmental impact in agricultural fields.