Real-Time Hovering Control of Unmanned Aerial Vehicles

Lúa, Cuauhtémoc Acosta; García, Claudia Carolina Vaca; Gennaro, S. Di; Castillo–Toledo, B.; Sánchez-Morales, M. E.

doi:10.1155/2020/2314356

Cited by 7 publications

(2 citation statements)

References 14 publications

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“…Motors 1 and 3 rotate counterclockwise, while motors 2 and 4 rotate clockwise, with respect to the z-axis of the body frame B, each with an angular velocity ω j ≥ 0, generating thrust forces f j for j ∈ 1, 2, 3, 4 in free air. For a detailed and comprehensive dynamic model of the quadrotor, including the equations governing its motion, please refer to [27,[36][37][38][39].…”

Section: Quadrotor Dynamic Modelmentioning

confidence: 99%

Quadrotor Trajectory Control Based on Energy-Optimal Reference Generator

Bianchi,

Borri,

Cappuzzo

et al. 2024

Drones

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Inspired by the limited battery life of multi-rotor unmanned aerial vehicles (UAVs), this research investigated hierarchical real-time control of UAVs with the generation of energy-optimal reference trajectories. The goal was to design a reference generator and controller based on optimal-control theory that would guarantee energy consumption close to optimal with lower computational cost. First, a least-squares-estimation-(LSE) algorithm identified the parameters of the UAV mathematical model. Then, by considering a precise electrical model for the brushless DC motors and rest-to-rest maneuvers, the extraction of clear rules to compute the optimal mission time and generate ’energetic trajectories’ was performed. These rules emerged from analyzing the optimal-control strategy results that minimized the consumption over many simulations. Afterward, a hierarchical controller tracked those desired energetic trajectories identified as sub-optimal. Numerical experiments compared the results regarding trajectory tracking, energy performance index, and battery state of charge (SOC). A co-simulation framework consisting of commercial software tools, Simcenter Amesim for the physical modeling of the UAV, and Matlab-Simulink executed numerical simulations of the implemented controller.

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Section: Quadrotor Dynamic Modelmentioning

confidence: 99%

Quadrotor Trajectory Control Based on Energy-Optimal Reference Generator

Bianchi,

Borri,

Cappuzzo

et al. 2024

Drones

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“…Real-time control is a critical and essential feature for operation of autonomous industrial manipulators [38]. Multiple techniques have emerged to enable real-time control of autonomous systems [39,40]. To enable flexible and easy control of industrial manipulators, suppliers like universal robot or Barrett often include external controllers or internal PCs.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Robotic Manipulation: Real-Time, Deep-Learning Approach for Grasping of Unknown Objects

Sayour

Kozhaya

Saab

2022

Journal of Robotics

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Recent advancement in vision-based robotics and deep-learning techniques has enabled the use of intelligent systems in a wider range of applications requiring object manipulation. Finding a robust solution for object grasping and autonomous manipulation became the focus of many engineers and is still one of the most demanding problems in modern robotics. This paper presents a full grasping pipeline proposing a real-time data-driven deep-learning approach for robotic grasping of unknown objects using MATLAB and convolutional neural networks. The proposed approach employs RGB-D image data acquired from an eye-in-hand camera centering the object of interest in the field of view using visual servoing. Our approach aims at reducing propagation errors and eliminating the need for complex hand tracking algorithm, image segmentation, or 3D reconstruction. The proposed approach is able to efficiently generate reliable multi-view object grasps regardless of the geometric complexity and physical properties of the object in question. The proposed system architecture enables simple and effective path generation and a real-time tracking control. In addition, our system is modular, reliable, and accurate in both end effector path generation and control. We experimentally justify the efficacy and effectiveness of our overall system on the Barrett Whole Arm Manipulator.

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Small unmanned helicopter modeling method based on a hybrid kernel function PSO-LSSVM

Zhou

Wang

et al. 2023

J Supercomput

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Real-Time Hovering Control of Unmanned Aerial Vehicles

Cited by 7 publications

References 14 publications

Quadrotor Trajectory Control Based on Energy-Optimal Reference Generator

Quadrotor Trajectory Control Based on Energy-Optimal Reference Generator

Autonomous Robotic Manipulation: Real-Time, Deep-Learning Approach for Grasping of Unknown Objects

Small unmanned helicopter modeling method based on a hybrid kernel function PSO-LSSVM

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