Measuring Trampling in Autonomous Mowers with Systematic Trajectories: Comparison with the Ordinary Random Patterns

Automation and robotics are now also spreading in agriculture, landscape and green areas scenarios. An ordinary autonomous mower is able to work inside an area, bounded by a physical wire, following a random pattern. Professional robotic mowing in sport turfgrass need systematic trajectories in order to improve the operative efficiency of the autonomous mower. A systematic cutting pattern needs an higher technology which is usually based on real time kinematic global navigation satellite system (RTK-GNSS). The aim of this study is to compare an ordinary autonomous mowers with random trajectories to an RTK-GNSS autonomous mower with systematic trajectories on an ornamental turfgrass made by cool season species, in terms of quality and trampling level. The experiment was in a private area located in the province of Pisa, Italy. Two different autonomous mowers were compared, the first one with systematic trajectories and the other one with random trajectories. The turfgrass was a mix of cool season species. The cutting height was set to 90 mm and the cutting width to 240 mm for both models. A mowing frequency of three times per week was established. The number of passages was assessed as a function of the type of autonomous mower operating pattern and position (centre and edges). A custom built software and remote sensing system was used for this purpose. Higher number of passages were detected in the area mowed with random trajectories (3.70) compared to the area mowed with systematic trajectories (2.02). Higher mean value of passages was detected at the edge (3.30) compared to the centre (2.42). These findings highlight the optimization of mowing trajectories with the systematic operating pattern, reducing the trajectories overlapping and the related more numerous stressed areas due to the trampling activity.