Teaching Aids for Laboratory Experiments with AR.Drone2 Quadrotor**The authors gratefully acknowledge the financial support of the Slovak Research and Development Agency under the project APVV 0551-11 and the Scientific Grant Agency of the Slovak Republic under the grant 1/0053/13 and the internal grant of the Slovak University of Technology in Bratislava for support of young researchers.

Stevek, Juraj; Fikar, Miroslav

doi:10.1016/j.ifacol.2016.07.183

Cited by 11 publications

(4 citation statements)

References 6 publications

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“…Furthermore, advances in MEMS technology [24][25][26][27][28] and rapid prototyping technology makes quadrotor has a simplest electronics and mechanical structures design. Therefore, quadrotor always being considered in research with wide applications and various tasks such as civil commercial, calamity and meteorological observation, agricultural and military purpose such as surveillances, reconnaissance and communication [29][30][31] also educational purposes [32].…”

Section: Quadrotor Uavmentioning

confidence: 99%

Sensor Fusion Algorithm by Complementary Filter for Attitude Estimation of Quadrotor with Low-Cost IMU

2018

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This paper proposes a sensor fusion algorithm by complementary filter technique for attitude estimation of quadrotor UAV using low-cost MEMS IMU. Angular rate from gyroscope tend to drift over a time while accelerometer data is commonly effected with environmental noise. Therefore, high frequency gyroscope signal and low frequency accelerometer signal is fused using complementary filter algorithm. The complementary filter scaling factor K1=0.98 and K2=0.02 are used to merge both gyro and accelerometer. The results show that the smooth roll, pitch and yaw attitude angle can be obtained from the low cost IMU by using proposed sensor fusion algorithm.

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Section: Quadrotor Uavmentioning

confidence: 99%

Sensor Fusion Algorithm by Complementary Filter for Attitude Estimation of Quadrotor with Low-Cost IMU

2018

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“…Furthermore, for university engineering programs, drone platforms have been used to encourage the development of intellectual curiosity, teamwork and disciplinary skills related to automation, programming, sensors, actuators, among others. Laboratory proposals to familiarize oneself with the drone's dynamics and its automatic control [27] have been described; in addition, the design of a toolkit to be applied to project-orientedlearning activities [28], as well as complete courses (from design to flight tests) [29] have been reported. Other results are implementations of activities and complete courses with simulated scenarios or real prototypes [30][31][32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

A Challenge-Based Learning Intensive Course for Competency Development in Undergraduate Engineering Students: Case Study on UAVs

et al. 2022

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Accelerated advances in science and technology drive the need for professionals with flexible problem-solving abilities towards a collaborative working environment. The advances pose a challenge to educational institutions about how to develop learning environments that contribute to meeting the aforementioned necessity. Additionally, the fast pace of technology and innovative knowledge are encouraging universities to employ challenge-based-learning (CBL) approaches in engineering education supported by modern technology such as unmanned aerial vehicles (UAVs) and other advanced electronic devices. Within the framework of competency-based education (CBE) and CBL are the design, implementation, and evaluation of an intensive 40 h elective course which includes a 5-day challenge to promote the development of disciplinary and transversal competencies in undergraduate engineering students whilst relying on UAVs as the medium where the teaching–learning process takes place. Within this credit course, a case study was carried out considering the framework of an exploratory mixed-methods educational research approach that sought a broad understanding of the studied phenomena using various data collection instruments with quantitative and qualitative characteristics. An innovative academic tool was introduced, namely a thematic UAV platform that systematically exposed students to the principles underlying robotic systems and the scientific method, thereby stimulating their intellectual curiosity as a trigger to solve the posed challenge. Moreover, students came up with innovative teamwork-based solutions to a designed challenge while having an enjoyable and motivating time flying drones on an indoor obstacle course arranged by themselves. The preliminary findings may contribute to the design of other CBL experiences, supported by technology applied for educational purposes, which could promote the development of more disciplinary and transversal competencies in future engineers.

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“…Various controllers based on classic or modern control theory were already designed and verified. The non-linear or linear model of a quadrotor is used depending on the chosen control method [11].The linear model of a quadrotor is achieved by linearization of the non-linear model around a hovering point. Controllers using the linearized model generally perform well around this hovering point.…”

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Control Methods Comparison for the Real Quadrotor on an Innovative Test Stand

et al. 2020

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This article is a continuation of our previously published work that presented a comparison of nine attitude quaternion-based controllers of the quadrotor in simulation environment. In this article, the best three controllers were implemented into the real quadrotor. Namely proportional derivative (PD), linear quadratic regulator (LQR) and backstepping quaternion-based control techniques were evaluated. As a suitable test stand was not available on the basis of literature analysis, the article also outlines the requirements and the development of a new innovative test stand. In order to provide a comprehensive overview, the hardware and software that was used is also presented in the article. The main contribution of this article is a performance comparison of the controllers, which was based on absolute quaternion (positioning) error and energy consumption.The quadrotor is coupled and underactuated system (i.e., six degrees of freedom are controlled only by four actuators). The translational motion of the quadrotor depends on the attitude of the quadrotor as can be seen from the equations in [9]. By this means, the quadrotor is a cascade system [10]. Therefore, a cascade structure of a controller is usually applied to control position and attitude of the quadrotor. Various controllers based on classic or modern control theory were already designed and verified. The non-linear or linear model of a quadrotor is used depending on the chosen control method [11].The linear model of a quadrotor is achieved by linearization of the non-linear model around a hovering point. Controllers using the linearized model generally perform well around this hovering point. When the quadrotor goes away from the linearized point, the performance may worsen. Furthermore, the input saturation can cause control failure when large rapid maneuvers are required [3,5,12,13]. The main advantages of linear controllers are simplicity and ease of implementation to a real platform. The disadvantage of this approach is the use of the linearized model of the quadrotor during the process of designing a controller. Commonly used linear controllers are a linear quadratic regulator (LQR) and a proportional derivative (PD) controller [14][15][16].Another group of controllers consists of a wide variety of non-linear controllers. The serious disadvantage of these controllers is the complexity that prevents the wide adoption of non-linear controllers in real applications. Among non-linear methods, the backstepping control technique based on the Lyapunov function is widely adopted due to its systematic design and an intuitive approach [17]. The proposed control law is based on the compensation of non-linear forces or torques depending on whether an attitude or position controller is being designed. Applying Lyapunov stability analysis proves that the closed-loop system is asymptotically stable. This approach was used to stabilize the quadrotor in [3,5,12,[18][19][20][21].A backstepping-based inverse optimal attitude controller (BIOAC) was derived in [3] ta...

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Cited by 11 publications

References 6 publications

Sensor Fusion Algorithm by Complementary Filter for Attitude Estimation of Quadrotor with Low-Cost IMU

Sensor Fusion Algorithm by Complementary Filter for Attitude Estimation of Quadrotor with Low-Cost IMU

A Challenge-Based Learning Intensive Course for Competency Development in Undergraduate Engineering Students: Case Study on UAVs

Control Methods Comparison for the Real Quadrotor on an Innovative Test Stand

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