Review and comparative analysis of mini- and micro-UAV autopilots

Кортунов, В.И.; Mazurenko, Oleksandr; Gorbenko, Anatoliy; Mohammed, Watheq; Hussein, Ali K.

doi:10.1109/apuavd.2015.7346622

Cited by 22 publications

(12 citation statements)

References 3 publications

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“…There are many autopilots on the market with different capabilities, but those designed for outdoor autonomous flight share the same basic characteristics [ 27 ]. Autopilots typically use data from an inertial motion unit (IMU), GPS/GNSS, and a barometer to estimate the ‘state’, or orientation and motion, of the aircraft [ 31 ]. Micro-electrical-mechanical systems (MEMS) IMUs are most common and are composed of accelerometers, magnetometers, and gyros.…”

Section: Methods Detailsmentioning

confidence: 99%

Adapting open-source drone autopilots for real-time iceberg observations

Carlson

Rysgaard

2018

MethodsX

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Drone autopilots are naturally suited for real-time iceberg tracking as they measure position and orientation (pitch, roll, and heading) and they transmit these data to a ground station. We powered an ArduPilot Mega (APM) 2.6 with a 5V 11 Ah lithium ion battery (a smartphone power bank), placed the APM and battery in a waterproof sportsman’s box, and tossed the box and its contents by hand onto an 80 m-long iceberg from an 8 m boat. The data stream could be viewed on a laptop, which greatly enhanced safety while collecting conductivity/temperature/depth (CTD) profiles from the small boat in the iceberg’s vicinity. The 10 s position data allowed us to compute the distance of each CTD profile to the iceberg, which is necessary to determine if a given CTD profile was collected within the iceberg’s meltwater plume. The APM position data greatly reduced position uncertainty when compared to 5 min position data obtained from a Spot Trace unit. The APM functioned for over 10 h without depleting the battery. We describe the specific hardware used and the software settings necessary to use the APM as a real-time iceberg tracker. Furthermore, the methods described here apply to all Ardupilot-compatible autopilots. Given the low cost ($90) and ease of use, drone autopilots like the APM should be included as another tool for studying iceberg motion and for enhancing safety of marine operations.Commercial off-the-shelf iceberg trackers are typically configured to record positions over relatively long intervals (months to years) and are not well-suited for short-term (hours to few days), high-frequency monitoringDrone autopilots are cheap and provide high-frequency (>1 Hz) and real-time information about iceberg drift and orientationDrone autopilots and ground control software can be easily adapted to studies of iceberg-ocean interactions and operational iceberg management

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Section: Methods Detailsmentioning

confidence: 99%

Adapting open-source drone autopilots for real-time iceberg observations

Carlson

Rysgaard

2018

MethodsX

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“…And as the next generation of systems, CPS is expected to be a key method to implement the artificial intelligence [45]. Thus, integrating a [24], 2017 [25], 2016 [26], 2015…”

Section: A Cps Can Inspire the Uav Networkmentioning

confidence: 99%

Survey on Unmanned Aerial Vehicle Networks: A Cyber Physical System Perspective

Wang

Zhao

Zhang

et al. 2020

IEEE Commun. Surv. Tutorials

172

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Unmanned aerial vehicle (UAV) networks are playing an important role in various areas due to their agility and versatility, which have attracted significant attention from both the academia and industry in recent years. As an integration of the embedded systems with communication devices, computation capabilities and control modules, the UAV network could build a closed loop from data perceiving, information exchanging, decision making to the final execution, which tightly integrates the cyber processes into the physical devices. Therefore, the UAV network could be considered as a cyber physical system (CPS). Revealing the coupling effects among the three interacted components in this CPS system, i.e., communication, computation and control, is envisioned as the key to properly utilize all the available resources and hence improve the performance of the UAV networks. In this paper, we present a comprehensive survey on the UAV networks from a CPS perspective. Firstly, we respectively research the basics and advances with respect to the three CPS components in the UAV networks. Then we look inside to investigate how these components contribute to the system performance by classifying the UAV networks into three hierarchies, i.e., the cell level, the system level, and the system of system level. Further, the coupling effects among these CPS components are explicitly illustrated, which could be enlightening to deal with the challenges in each individual aspect. New research directions and open issues are discussed at the end of this survey. With this intensive literature review, we try to provide a novel insight into the state-of-the-art in the UAV networks.

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“…These FW belong to mini UAVs [20]. The SX8FW uses the mini autopilot AP-AVIA, which was developed at the National Aerospace University named after N. Zhukovsky (KhAI) [21,22].…”

Section: Problem Statementmentioning

confidence: 99%

“…The errors of the AP-AVIA autopilot sensors are presented in work [22]. The frequency of the data recording can be 20 Hz (saved to an SD card by the autopilot) or 5 Hz (saved to a log file by the ground control station).…”

Section: Problem Statementmentioning

confidence: 99%

“…It is concluded that it is possible to reduce the colinearity by removing the dependent parameters. In order to obtain some feel for the importance of parameters in (22), the estimated parameters in this equation for the SX8FW are compared with the data for the aerodynamic parameters of the ZFW and SX8FW in Table 4. Parameters estimation errors are three times larger than the standard deviation (3σ) shown in Table 4.…”

Section: No /mentioning

confidence: 99%

See 1 more Smart Citation

Estimation of the Lateral Aerodynamic Coefficients for Skywalker X8 Flying Wing From Real Flight-Test Data

et al. 2018

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Stability and control derivatives of Skywalker X8 flying wing from flight-test data are estimated by using the combination of the output error and least square methods in the presence of the wind. Data is collected from closed loop flight tests with a proportional-integral-derivative (PID) controller that caused data co-linearity problems for the identification of the unmanned aerial vehicle (UAV) dynamic system. The data co-linearity problem is solved with a biased estimation via priori information, parameter fixing and constrained optimization, which uses analytical values of aerodynamic parameters, the level of the identifiability and sensitivity of the measurement vector to the parameters. Estimated aerodynamic parameters are compared with the theoretically calculated coefficients of the UAV, moreover, the dynamic model is validated with additional flight-test data and small covariances of the estimated parameters.

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Review and comparative analysis of mini- and micro-UAV autopilots

Cited by 22 publications

References 3 publications

Adapting open-source drone autopilots for real-time iceberg observations

Adapting open-source drone autopilots for real-time iceberg observations

Survey on Unmanned Aerial Vehicle Networks: A Cyber Physical System Perspective

Estimation of the Lateral Aerodynamic Coefficients for Skywalker X8 Flying Wing From Real Flight-Test Data

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