Enabling Autonomous Navigation on the Farm: A Mission Planner for Agricultural Tasks

Ruth Cordova-Cardenas, Luis Emmi () and Pablo Gonzalez- de-Santos
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Ruth Cordova-Cardenas: Centre for Automation and Robotics (UPM-CSIC), 28500 Arganda del Rey, Madrid, Spain
Luis Emmi: Centre for Automation and Robotics (UPM-CSIC), 28500 Arganda del Rey, Madrid, Spain
Pablo Gonzalez- de-Santos: Centre for Automation and Robotics (UPM-CSIC), 28500 Arganda del Rey, Madrid, Spain

Agriculture, 2023, vol. 13, issue 12, 1-19

Abstract: This study presents the development of a route planner, called Mission Planner, for an agricultural weeding robot that generates efficient and safe routes both in the field and on the farm using a graph-based approach. This planner optimizes the robot’s motion throughout the farm and performs weed management tasks tailored for high-power laser devices in narrow-row crops (wheat, barley, etc.) and wide-row crops (sugar beet, maize, etc.). Three main algorithms were integrated: Dijkstra’s algorithm to find the most optimal route on the farm, the VRMP (Visibility Road-Map Planner) method to select the route within cultivated fields when roads are not visible, and an improved version of the Hamiltonian path to find the best route between the crop lines. The results support the effectiveness of the strategies implemented, demonstrating that a robot can safely and efficiently navigate through the entire farm and perform an agricultural treatment, in this case study, in laser-based weed management. In addition, it was found that the route planner reduced the robot’s operation time, thus improving the overall efficiency of precision agriculture.

Keywords: agricultural robotics; route planner; Dijkstra algorithm; VRMP method; Hamiltonian trajectory; laser weed removal; machine learning; precision agriculture (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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