Field-deployed robotic fleets can provide solutions that improve operational efficiency, control operational costs, and provide farmers with transparency over day-to-day scouting operations. The topology of agricultural environments, such as polytunnels, provides a basic configuration that can be exploited to create topological maps aiding operational planning and robot navigation. However, these environments these environments, optimised for human operations or large farming vehicles, pose a major challenge for multiple moving robots to coordinate their navigation while performing tasks. An unmodified farm environment, not tailored for robotic fleet deployments, can cause traffic bottlenecks, thereby affecting the overall efficiency of the fleet. In this work, we propose a Genetic Algorithm-based Topological Optimisation (GATO) algorithm that discretises the search space of topological modifications into finite integer combinations. Each solution is encoded as an integer vector that contains the location information of the topology modification. We evaluate our algorithm through a discrete event simulation of the picking and in-field logistics processes on a commercial strawberry farm, and the results demonstrate its effectiveness in identifying topological modifications that enhance the efficiency of robotic fleet operations.
On Optimising Topology of Agricultural Fields for Efficient Robotic Fleet Deployment
Lect. Notes in Networks, Syst.
International Conference on Intelligent Autonomous Systems ; 2023 ; Suwon, Korea (Republic of) July 04, 2023 - July 07, 2023
2024-04-21
17 pages
Article/Chapter (Book)
Electronic Resource
English
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