Modeling and flight control of a commercial nano quadrotor

Garcia, Gonzalo; Kim, A Ram; Jackson, Ethan K.; Kashmiri, Shawn; Shukla, Daksh

doi:10.1109/icuas.2017.7991439

Cited by 11 publications

(2 citation statements)

References 5 publications

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“…where g ∈ R + is the gravity constant, whereas m ∈ R + and J z ∈ R + denote the mass and the coefficient of inertia around z B i -axis of the body frame which are supposed to be the same for all the UAVs in the formation. From now on, we assume that each quadrotor is able to track both reference position and yaw angle with negligible error by implementing an effective control law (e.g., based on PID regulators [36,37], MPC approach [38,39], geometric control scheme [40,41]) and resting upon the available measurements. To this end, we assume that each i-th UAV is supplied with a high-precision position sensor (as, for instance, a GNSS sensor) allowing the platform to localize itself in the world frame and regarding the other quadrotors, and a communication interface, i.e., a (ideal) radio module with communication range (communication radius) larger than the formation size.…”

Section: Quadrotor Modelmentioning

confidence: 99%

Cooperative Optimization of UAVs Formation Visual Tracking

et al. 2019

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The use of unmanned vehicles to perform tiring, hazardous, repetitive tasks, is becoming a reality out of the academy laboratories, getting more and more interest for several application fields from the industrial, to the civil, to the military contexts. In particular, these technologies appear quite promising when they employ several low-cost resource-constrained vehicles leveraging their coordination to perform complex tasks with efficiency, flexibility, and adaptation that are superior to those of a single agent (even if more instrumented). In this work, we study one of said applications, namely the visual tracking of an evader (target) by means of a fleet of autonomous aerial vehicles, with the specific aim of focusing on the target so as to perform an accurate position estimation while concurrently allowing a wide coverage over the monitored area so as to limit the probability of losing the target itself. These clearly conflicting objectives call for an optimization approach that is here developed: by considering both aforementioned aspects and the cooperative capabilities of the fleet, the designed algorithm allows controling in real time the single fields of view so as to counteract evasion maneuvers and maximize an overall performance index. The proposed strategy is discussed and finally assessed through the realistic Gazebo-ROS simulation framework.

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Section: Quadrotor Modelmentioning

confidence: 99%

Cooperative Optimization of UAVs Formation Visual Tracking

et al. 2019

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“…Для этого приведем обобщенный лагранжиан 𝐿, который представляет собой разность кинетической и потенциальной энергий моделируемого объекта. В случае квадрокоптера [5][6][7] рассматриваемая модель может быть основана на модели абсолютно твердого тела [8]:…”

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Numerical method for solving the optimization problem of trajectory control and formation maintenance by a group of autonomous UAVs with predictive models

Ganshin

2023

News of KBSC RAS

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Аннотация. Статья посвящена разработке численного метода решения оптимизационной задачи траекторного управления единичным БПЛА на основе метода штрафных функций с вычислительным ускорением. Описан численный метод поиска решения задачи траекторного управления, которая представлена в виде задачи квадратичного программирования, использующий метод штрафных функций с вычислительным ускорением Эйткина.

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