Sensor fusion with enhanced Kalman Filter for altitude control of quadrotors

Hetenyi, Daniel; Gotzy, Marton; Blázovics, László

doi:10.1109/saci.2016.7507412

Cited by 8 publications

(6 citation statements)

References 4 publications

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“…In order to study the kinematic law of aircraft, two coordinate systems [44] depicted in Figure 2 The rest of this paper is organized as follows. In II, dynamic models of the quadrotor are described.…”

Section: Quadrotor Dynamic Modelsmentioning

confidence: 99%

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Real-Time Terrain-Following of an Autonomous Quadrotor by Multi-Sensor Fusion and Control

et al. 2021

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For the application of the autonomous guidance of a quadrotor from confined undulant ground, terrain-following is the major issue for flying at a low altitude. This study has modified the open-source autopilot based on the integration of a multi-sensor receiver (a Global Navigation Satellite System (GNSS)), a Lidar-lite (a laser-range-finder device), a barometer and a low-cost inertial navigation system (INS)). These automatically control the position, attitude and height (a constant clearance above the ground) to allow terrain-following and avoid obstacles based on multi-sensors that maintain a constant height above flat ground or with obstacles. The INS/Lidar-lite integration is applied for the attitude and the height stabilization, respectively. The height control is made by the combination of an extended Kalman filter (EKF) estimator and a cascade proportional-integral-derivative (PID) controller that is designed appropriately for the noise characteristics of low accuracy sensors. The proposed terrain-following is tested by both simulations and real-world experiments. The results indicate that the quadrotor can continuously navigate and avoid obstacles at a real-time response of reliable height control with the adjustment time of the cascade PID controller improving over 50% than that of the PID controller.

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Section: Quadrotor Dynamic Modelsmentioning

confidence: 99%

“…In order to study the kinematic law of aircraft, two coordinate systems [44] depicted in Figure 2 According to the conversion relationship between the Euler angle and the attitude matrix, the attitude transfer matrix R b E from the geographic coordinate system to the airframe coordinate system can be obtained:…”

Section: Quadrotor Dynamic Modelsmentioning

confidence: 99%

Real-Time Terrain-Following of an Autonomous Quadrotor by Multi-Sensor Fusion and Control

et al. 2021

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“…Sensor fusion techniques such as extended Kalman Filtering(KF) [39], [40] and it variants [41] had been used in robotics for an object estimation [42], a robot pose estimation, localization [43], [44] and navigation [45]. Kalman filter (KF) is normally used in real time applications to fuse dynamic sensor information [46].…”

Section: A Localizationmentioning

confidence: 99%

A Systematic Review on Fusion Techniques and Approaches Used in Applications

Jusoh

Almajali

2020

IEEE Access

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Fusion technologies have rapidly evolved. These technologies are normally customized according to the needs of domains. Despite a large number of publications on intelligence fusion applications for various domains, they are scattered. The aim of this review is to present the state of the art for intelligence fusion applications within a specific domain. We identified three major domains for the purpose, namely robotics, military, and healthcare, during the initial process of the systematic review. These three domains are always in need of superior intelligence. Articles were searched mainly in IEEE Xplore. We limit the range of publications to the year 2014 to 2019, to focus on the most recent publications. We adopt the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol to screen, filter and evaluate qualities of each retrieved article. As a result, we retrieved 675 articles at the initial stage of the search, we conducted screening and filtering process and reviewed 153 articles potential articles, and finally, we excluded 36 articles as they do not comply with our quality assessment criteria. Only 117 articles are included. The results of this study are a list of classified applications within the domains and a number of relevant techniques or approaches used in each classified application. The finding of this review showed that the most published works for the use of intelligence fusion are mainly applications in the robotics domain, where mostly used techniques are Kalman Filter and its variants. Outcomes of this study can be a guideline or an insight for researchers to further develop and implement in this field.

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“…Since the time of its introduction, the Kalman filter has been the subject of extensive research and application, particularly in the area of autonomous or assisted navigation. It can be applied to compute vehicle attitude by fusion of accelerometer, gyroscopes and magnetometer data (DEIBE et al, 2020;JURMAN et al, 2007), altitude by fusion of accelerometer and barometer data (HETéNYI;GóTZY;BLáZOVICS, 2016), and position by fusion of GPS and accelerometer data (CARON et al, 2006). Also, there is the attitude filter proposed by (MAHONY; HAMEL; PFLIMLIN, 2008) which is formulated as a deterministic kinematic observer on the Special Orthogonal group SO(3) driven by an instantaneous attitude and angular velocity measurements.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Thus, in the prediction step the altitude and vertical velocity are computed in Eq. (7.70), following (HETéNYI;GóTZY;BLáZOVICS, 2016).…”

Section: Altitude and Vertical Velocity Discrete Kalman Filtermentioning

confidence: 99%

A tilt-wing VTOL UAV configuration study: flight dynamics modeling, simulation, states estimation, control, systems integration and flight testing

Filho¹

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Agradeço aos meus pais e meu irmão por me incentivarem e darem a força para continuar com este trabalho de doutorado, e a não desistir mesmo com todas as dificuldades de todo esse processo.Ao meu orientador Prof. Dr. Eduardo Morgado Belo pelo apoio, disposição, discussões, conselhos e sabedoria, me ajudando sempre que precisei.Aos meus amigos da pós-graduação Carlos, Fer, Luis, Igor, Natanael, Felipe, Helô, pela amizade, apoio, pelos conselhos, sugestões e bons momentos que passamos juntos.Aos funcionários da universidade Cláudio, Gi, Gallo, Ana, Rosana, Marlene, Osnan, João e Zé por terem me ensinado muita coisa e ajudado sempre que precisei.Aos professores Ricardo, Glauco, Hernan, Bidinotto, Álvaro e Catalano pelo incentivo, conselhos, sugestões e disposição em me ajudar com o projeto.

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Sensor fusion with enhanced Kalman Filter for altitude control of quadrotors

Cited by 8 publications

References 4 publications

Real-Time Terrain-Following of an Autonomous Quadrotor by Multi-Sensor Fusion and Control

Real-Time Terrain-Following of an Autonomous Quadrotor by Multi-Sensor Fusion and Control

A Systematic Review on Fusion Techniques and Approaches Used in Applications

A tilt-wing VTOL UAV configuration study: flight dynamics modeling, simulation, states estimation, control, systems integration and flight testing

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