In the agricultural industry, an evolutionary effort has been made over the last two decades to achieve precise autonomous systems to perform typical in-field tasks including harvesting, mowing, and spraying. One of the main objectives of an autonomous system in agriculture is to improve the efficiency while reducing the environmental impact and cost. Due to the nature of these operations, complete coverage path planning approaches play an essential role to find an optimal path which covers the entire field while taking into account land topography, operation requirements and robot characteristics. The aim of this paper is to propose a complete coverage path planning approach defining the optimal movements of mobile robots over an agricultural field. First, a method based on tree exploration is proposed to find all potential solutions satisfying some predefined constraints. Second, a Similarity check and selection of optimal solutions method is proposed to eliminate similar solutions and find the best solutions. The optimization goals are to maximize the coverage area and to minimize overlaps, non-working path length and overall travel time. In order to explore a wide range of possible solutions, our approach is able to consider multiple entrances for the robot. For fields with a complex shape, different dividing lines to split it into simple polygons are also considered. Our approach also computes the headland zones and covers them automatically which leads to a high coverage rate of the field.

Over the last few decades, the agricultural industry has witnessed significant advancements in autonomous systems, primarily aimed at improving efficiency while reducing environmental impact. The critical role of complete coverage path planning cannot be overstated in this context. It involves determining an optimal path for tasks such as harvesting, mowing, and spraying, taking into account various factors like land topography, operational requirements, and robot characteristics. Our previous approach introduced a tree-based exploration method to generate potential solutions, coupled with an optimization process to select the best ones, considering field complexities and robot characteristics. Yet, despite its strengths, it had certain limitations, notably in computational time and number of examined driving directions. In this paper, we present a novel hybrid method that combines the comprehensive coverage benefits of our original approach with the computational efficiency of the Fields2Cover algorithm. Besides combining our previous approach and Fieds2Cover strengths for optimizing coverage area, overlaps, non-working path length and overall travel time, it significantly improves the computation process, enhances the flexibility of trajectory generation. It also takes into account the working trajectory inclinations for more advanced optimization to address soil erosion and energy consumption. In an effort to support this innovative approach, we have also created and made available a public dataset. This dataset includes both 2D and 3D representations of thirty agricultural fields located in France. This resource not only illustrates the effectiveness of our approach but also provides an invaluable data for future research in complete coverage path planning within the context of modern agriculture.