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.
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 (CCPP) 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 CCPP 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, nonworking path length, and overall travel time. 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 them 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 past two decades, an evolutionary effort has been established in the agricultural sector to develop efficient autonomous systems that can carry out common infield operations including harvesting, mowing, and spraying. Increasing production while decreasing costs and environmental damages is one of the main objectives for these autonomous systems. Due to the nature of these tasks, complete coverage path planning techniques are crucial to determining the best path that covers the entire field while accounting for terrain characteristics, operational needs, and robot properties.In this study, we propose a novel complete coverage path planning approach to define the ideal path for a wheeled robot across an agricultural field. To identify all feasible solutions satisfying a set of predefined constraints, a method based on tree exploration is first proposed that examines skip-row patterns. Second, the most optimal solution is selected by a selection method. Maximizing the covered area while minimizing overlaps, non-working path length, number of turns containing reverse moves, and overall travel time are the objectives of the selection method.We showed on 6 real-world fields geometries that the row skip approach offered benefits in terms of reduction of the required headland size, and often helped decreasing the number of necessary reverse moves and the overlaps, while increasing the covered area.
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