Abstract:In this paper, we introduce a translational force control method with disturbance observer (DOB)-based force disturbance cancellation for precise three-dimensional acceleration control of a multi-rotor UAV. The acceleration control of the multi-rotor requires conversion of the desired acceleration signal to the desired roll, pitch, and total thrust. But because the attitude dynamics and the thrust dynamics are different, simple kinematic signal conversion without consideration of those difference can cause ser… Show more
“…12 (a)) could also be a reference in the future. An elaborated controller considering disturbance rejection and asynchronization between thrust and attitude [40] might be necessary to reduce the relative angle and velocity errors at the time of perching.…”
Perching on surfaces of moving objects, like vehicles, could extend the flight times and range of quadrotors. For surfaces attachment, suction cups are usually adopted due to their durability and large adhesion force. To seal on surfaces, suction cups are required to be aligned with surfaces and match to the frictions on end faces induced by relative tangential velocity.
“…12 (a)) could also be a reference in the future. An elaborated controller considering disturbance rejection and asynchronization between thrust and attitude [40] might be necessary to reduce the relative angle and velocity errors at the time of perching.…”
Perching on surfaces of moving objects, like vehicles, could extend the flight times and range of quadrotors. For surfaces attachment, suction cups are usually adopted due to their durability and large adhesion force. To seal on surfaces, suction cups are required to be aligned with surfaces and match to the frictions on end faces induced by relative tangential velocity.
“…Usually, the system uncertainty is defined as unknown disturbance or as unknown input. In the existing work [15,16,17,31,32], system uncertainties and external disturbances are bounded by some scalars, without more detailed expressions. In order to better enhance the robustness of the proposed control method to system uncertainties, crosswind disturbances and other non-linear disturbances, the research should be basically developed on a control-oriented model.…”
Section: Problem Formulationmentioning
confidence: 99%
“…In order to design controllers that are based on measured outputs and limited model, different kinds of observer-based controllers have been investigated. In [15][16][17], disturbance observer-based control was applied to design hierarchical controllers for a multi-rotor UAV and a coaxial-rotor UAV, respectively. The observer was used for estimating the state and the unknown aerodynamic disturbances.…”
“…Due to decreasing prices, coupled with technological developments, drones have become more popular and widely used in modern agriculture for machine vision applications. Multirotor drones are the most common types of drones used by professionals for applications such as aerial photography [22].…”
Knowledge of the number and distribution of oil palm trees during the crop cycle is vital for sustainable management and predicting yields. The accuracy of the conventional image processing method is limited for the hand-crafted feature extraction method and the overfitting problem occurs due to the insufficient dataset. We propose a modification of the Faster Region-based Convolutional Neural Network (FRCNN) for palm tree detection to reduce the overfitting problem and improve the detection accuracy. The enhanced FRCNN (EFRCNN) leads to improved performance for detecting objects (in the same image) when they are of multiple sizes by using a feature concatenation method. Transfer learning based on a ResNet50 model is used to extract the features of the input image. High-resolution images of oil palm trees from a drone are used to form the data set, containing mature, young, and mixed oil palm tree regions. We train and test the EFRCNN, the FRCNN, a CNN used recently for oil palm image detection, and two standard methods, namely, the support vector machine (SVM) and template matching (TM). The results reveal an overall accuracy of ≥96.8% for the EFRCNN on the three test sets. The accuracy is higher than the CNN and FRCNN and substantially higher than SVM and TM. For large-scale plantations, the accuracy improvement is significant. This research provides a method for automatically counting the oil palm trees in large-scale plantations.
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