The increasing number of human population and its growing need for food presents a great challenge for global farming. In the future, agricultural robotics can greatly contribute to increased production of crops, which would increase yield, sustainability and access to crops and at the same time reduce the use of pesticides. In this work we focus on one of the many challenges of precision farming, namely autonomous navigation in an agricultural environment. We developed a navigation algorithm that is capable of successfully navigating a small agricultural robot in a simulated maize field. We used measurements from a 2D LiDAR sensor to plan a path that guides the robot through the field rows and from one row to another. These measurements were processed using the clustering algorithm DBSCAN and a SLAM algorithm. The robustness of the navigation algorithm was tested on simulated fields with different levels of terrain roughness and plant density. In our tests we show that higher levels of terrain roughness affect the quality of the robot driving and SLAM algorithm, however lowering the plant density has the largest effect on the quality of the navigation. We conclude that our algorithm is capable of navigating a small mobile robot in a simulated maize field as long as the field is sufficiently densely planted.
|
