Abstract
Robotics in precision agriculture has the potential to improve competitiveness and increase sustainability compared to current crop production methods and has become an increasingly active area of research. Tractor guidance systems for supervised navigation and implement control have reached the market, and prototypes of field robots performing precision agriculture tasks without human intervention also exist. But research in advanced cognitive perception and behaviour that is required to enable a more efficient, reliable and safe autonomy becomes increasingly demanding due to the growing software complexity. A lack of collaboration between research groups contributes to the problem. Scientific publications describe methods and results from the work, but little field robot software is released and documented for others to use. We hypothesize that a common open software platform tailored to field robots in precision agriculture will significantly decrease development time and resources required to perform experiments due to efficient reuse of existing work across projects and robot platforms. In this work we present the FroboMind software platform and evaluate the performance when applied to precision agriculture tasks.
Highlights
Robotics in precision agriculture has the potential to improve competitiveness and increase sustainability compared to current crop production methods [1], and has become an increasingly active area of research during the past decades
We hypothesize that a common open software platform tailored to field robots in precision agriculture will significantly decrease the development time and resources required to perform field experiments due to efficient reuse of existing work across projects and robot platforms
The aim of this work is to establish such a software platform and evaluate the performance when applied to different precision agriculture tasks and field robots
Summary
Robotics in precision agriculture has the potential to improve competitiveness and increase sustainability compared to current crop production methods [1], and has become an increasingly active area of research during the past decades. One example is the John Deere iTEC Pro which supports GNSS based steering in straight and curved rows and at headlands while controlling speed and performing active implement guidance. Another example is the Claas Cam Pilot system which navigates a tractor through row crops using 3D computer vision to detect the location of the crop rows. Research in advanced cognitive [7] perception and behaviour that are required to enable a more efficient, reliable and safe autonomy becomes increasingly demanding
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