Browsing by Author "Auat Cheein, Fernando"

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    A two-stage deep learning strategy for weed identification in grassfields
    (2024) Calderara Cea, Felipe Antonio; Torres Torriti, Miguel Attilio; Auat Cheein, Fernando; Delpiano, José
    Machine vision strategies for weed identification, whether in industrial crops or grassfields, are fundamental to the development of automated removal systems necessary to increase agricultural yield and field maintenance efficiency. Identifying plant species considered invasive on grassfields is particularly challenging due to reduced color and morphological contrast, as well as phenotypic variability. This work presents a two-stage weed identification strategy using visible spectrum images. The first stage employs a convolutional siamese neural network to identify candidate regions that may contain weeds of irregular or regular morphology. The second stage employs a convolutional neural network to confirm the presence of irregular morphology weeds. The results of each stage are combined to produce an output containing a per-pixel probability of irregular weed and bounding boxes for the morphologically regular weed. The two-stage strategy has an accuracy score of 97.16% and a balanced accuracy score of 89.94% and macro F1 score of 81.14%. In addition to the good performance scores obtained with the proposed approach, it is to be noted that the convolutional Siamese network allows achieving a good performance with a relatively small dataset compared to other strategies that employ data-intensive training phases for optimizing the convolutional neural networks. The results were obtained with a dataset of weeds that has been made publicly available, as well as the neural network models and associated computer code. The dataset contains samples Trifolium repens and Lectuca virosa on grass obtained with two different cameras under varying illumination conditions and different geographic locations. The lightweight nature of the proposed strategy provides a solution amenable to implementation using currently existing embedded computer technology for real-time weed detection.
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    Adaptive Nonlinear MPC for Efficient Trajectory Tracking Applied to Autonomous Mining Skid-Steer Mobile Robots
    (IEEE, 2020) Prado, Alvaro Javier; Chávez, Danilo; Camacho, Oscar; Torres Torriti, Miguel Attilio; Auat Cheein, Fernando
    The heterogeneous nature of the navigation surface suggests adaptation capabilities in vehicle motion control to overcome the effects of the wheel-terrain interaction. In such scenario, this paper presents an integral adaptive control framework built upon a Nonlinear Moving Horizon Estimator and a Nonlinear Model Predictive Control scheme, under which the objective is to on-line estimate states and model parameters of a robot motion model while autonomously navigating in off-road terrain conditions. With an adjustable model, the controller is made adaptive against terrain changes while tracking prescribed trajectories. The system is composed by two coupled subsystems to represent the vehicle motion and tire slip dynamics. The combined control-estimation strategy works under the Real-Time Iteration scheme to attain reliable computational activity for high-speed tire dynamics (e.g., slip). Trials in a simulation and real test environment with a compact mini-loader Cat® 262C, as those found in the mining industry, showed that the approach is able to efficiently estimate states and model parameters without exceeding constraints. The analysis of computational efficiency in various hardware configurations is also provided, exhibiting that the rapid optimization involved in the proposed controller is possible for high-speed dynamics.
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    Cluster Analysis for Agriculture
    (Springer, 2023) Arévalo Ramírez, Tito; Auat Cheein, Fernando
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    General Dynamic Model for Skid-Steer Mobile Manipulators with Wheel-Ground Interactions
    (2017) Aguilera Marinovic, Sergio Francisco; Torres Torriti, Miguel Attilio; Auat Cheein, Fernando
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    Overcoming the Loss of Performance in Unmanned Ground Vehicles Due to the Terrain Variability
    (2018) Prado, Javier; Yandun, Francisco; Torres Torriti, Miguel Attilio; Auat Cheein, Fernando