scholarly journals Aerial physical interaction via IDA-PBC

2019 ◽  
Vol 38 (4) ◽  
pp. 403-421 ◽  
Author(s):  
Burak Yüksel ◽  
Cristian Secchi ◽  
Heinrich H. Bülthoff ◽  
Antonio Franchi
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Control Method ◽  
Low Cost ◽  
Surface Inspection ◽  
Physical Interaction ◽  
External Disturbances ◽  
Aerial Vehicle ◽  
Passivity Based Control ◽  
The Stability ◽  
First Time

This paper proposes the use of a novel control method based on interconnection and damping assignment–passivity-based control (IDA-PBC) in order to address the aerial physical interaction (APhI) problem for a quadrotor unmanned aerial vehicle (UAV). The apparent physical properties of the quadrotor are reshaped in order to achieve better APhI performances, while ensuring the stability of the interaction through passivity preservation. The robustness of the IDA-PBC method with respect to sensor noise is also analyzed. The direct measurement of the external wrench, needed to implement the control method, is compared with the use of a nonlinear Lyapunov-based wrench observer and advantages/disadvantages of both methods are discussed. The validity and practicability of the proposed APhI method is evaluated through experiments, where for the first time in the literature, a lightweight all-in-one low-cost force/torque (F/T) sensor is used onboard of a quadrotor. Two main scenarios are shown: a quadrotor responding to external disturbances while hovering (physical human–quadrotor interaction), and the same quadrotor sliding with a rigid tool along an uneven ceiling surface (inspection/painting-like task).

Dynamic gyroscope sensors fusion and calibration using Kalman filter

2018 ◽  
Vol 7 (2.3) ◽  
pp. 18
Author(s):  
Mishell D. Lawas ◽  
Sherwin A. Guirnaldo
Keyword(s):  
Kalman Filter ◽  
Unmanned Aerial Vehicle ◽  
Systems Design ◽  
Mean Square ◽  
Fusion Technique ◽  
System Experiment ◽  
Aerial Vehicle ◽  
Sensors Fusion ◽  
The Stability ◽  
Actual Test

The stability of an Unmanned Aerial Vehicle (UAV) during actual flight conditions is one parameter that is very important in systems design in Avionics. In this research, two sensors, the autopilot microcontroller and the smartphone gyroscope sensing mechanism, are fused together and calibrated to monitor the flying behavior of the UAV prior to actual test flights. The two fused sensors and installed inside the UAV for relatively increased sensing accuracy and best flight monitoring capabilities. A Kalman filter is used as fusion technique and a Stewart Motion tracker is also used to test the ruggedness and accuracy of the fused sensor system. Experiment results show that fused system can give an overall mean square error or 1.9729.

scholarly journals Suitability of a Non-Dispersive Infrared Methane Sensor Package for Flux Quantification Using an Unmanned Aerial Vehicle

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4705 ◽  
Author(s):  
Adil Shah ◽  
Joseph Pitt ◽  
Khristopher Kabbabe ◽  
Grant Allen
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Mole Fraction ◽  
Low Cost ◽  
Methane Flux ◽  
Gain Factor ◽  
Measured Signal ◽  
Allan Deviation ◽  
Methane Sensor ◽  
Aerial Vehicle ◽  
Flux Quantification

Point-source methane emission flux quantification is required to help constrain the global methane budget. Facility-scale fluxes can be derived using in situ methane mole fraction sampling, near-to-source, which may be acquired from an unmanned aerial vehicle (UAV) platform. We test a new non-dispersive infrared methane sensor by mounting it onto a small UAV, which flew downwind of a controlled methane release. Nine UAV flight surveys were conducted on a downwind vertical sampling plane, perpendicular to mean wind direction. The sensor was first packaged in an enclosure prior to sampling which contained a pump and a recording computer, with a total mass of 1.0 kg. The packaged sensor was then characterised to derive a gain factor of 0.92 ± 0.07, independent of water mole fraction, and an Allan deviation precision (at 1 Hz) of ±1.16 ppm. This poor instrumental precision and possible short-term drifts made it non-trivial to define a background mole fraction during UAV surveys, which may be important where any measured signal is small compared to sources of instrumental uncertainty and drift. This rendered the sensor incapable of deriving a meaningful flux from UAV sampling for emissions of the order of 1 g s−1. Nevertheless, the sensor may indeed be useful when sampling mole fraction enhancements of the order of at least 10 ppm (an order of magnitude above the 1 Hz Allan deviation), either from stationary ground-based sampling (in baseline studies) or from mobile sampling downwind of sources with greater source flux than those observed in this study. While many methods utilising low-cost sensors to determine methane flux are being developed, this study highlights the importance of adequately characterising and testing all new sensors before they are used in scientific research.

scholarly journals An overview of various kinds of wind effects on unmanned aerial vehicle

2019 ◽  
Vol 52 (7-8) ◽  
pp. 731-739 ◽  
Author(s):  
Bo Hang Wang ◽  
Dao Bo Wang ◽  
Zain Anwar Ali ◽  
Bai Ting Ting ◽  
Hao Wang
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Great Influence ◽  
Wind Disturbance ◽  
Wind Fields ◽  
Wind Model ◽  
Aerial Vehicle ◽  
Low Altitude ◽  
The Stability ◽  
The Mathematical Model ◽  
Vehicle Movement

Attitude, speed, and position of unmanned aerial vehicles are susceptible to wind disturbance. The types, characteristics, and mathematical models of the wind, which have great influence on unmanned aerial vehicle in the low-altitude environment, are summarized, including the constant wind, turbulent flow, many kinds of wind shear, and the propeller vortex. Combined with the mathematical model of the unmanned aerial vehicle, the mechanism of unmanned aerial vehicle movement in the wind field is illustrated from three different kinds of viewpoints including velocity viewpoint, force viewpoint, and energy viewpoint. Some simulation tests have been implemented to show the effects of different kinds of wind on unmanned aerial vehicle’s path and flight states. Finally, some proposals are presented to tell reader in which condition, which wind model should be added to simulation, and how to enhance the stability of unmanned aerial vehicle for different kinds of wind fields.

scholarly journals Simulation of UAV Systems

10.14311/754 ◽  
2005 ◽  
Vol 45 (4) ◽  
Author(s):  
P. Kaňovský ◽  
L. Smrcek ◽  
C. Goodchild
Keyword(s):  
Dynamical System ◽  
Unmanned Aerial Vehicle ◽  
Low Cost ◽  
Design Tool ◽  
Inertial System ◽  
Principal Function ◽  
Low Weight ◽  
High Bandwidth ◽  
Aerial Vehicle ◽  
Selection Of

The study described in this paper deals with the issue of a design tool for the autopilot of an Unmanned Aerial Vehicle (UAV) and the selection of the airdata and inertial system sensors. This project was processed in cooperation with VTUL a PVO o.z. [1]. The feature that distinguishes the autopilot requirements of a UAV (Figs. 1, 7, 8) from the flight systems of conventional manned aircraft is the paradox of controlling a high bandwidth dynamical system using sensors that are in harmony with the low cost low weight objectives that UAV designs are often expected to achieve. The principal function of the autopilot is flight stability, which establishes the UAV as a stable airborne platform that can operate at a precisely defined height. The main sensor for providing this height information is a barometric altimeter. The solution to the UAV autopilot design was realised with simulations using the facilities of Matlab® and in particular Simulink®[2]. 

scholarly journals Design of a Low-Cost Fixed Wing UAV

2018 ◽  
Vol 159 ◽  
pp. 02045
Author(s):  
Mochammad Ariyanto ◽  
Joga D. Setiawan ◽  
Teguh Prabowo ◽  
Ismoyo Haryanto ◽  
Munadi
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Low Cost ◽  
Flight Test ◽  
Body Part ◽  
Aerial Vehicle ◽  
Straight Wing

This research will try to design a low cost of fixed-wing unmanned aerial vehicle (UAV) using low-cost material that able to fly autonomously. Six parameters of UAV’s structure will be optimized based on basic airframe configuration, wing configuration, straight wing, tail configuration, fuselage material, and propeller location. The resulted and manufactured prototype of fixed-wing UAV will be tested in autonomous fight tests. Based on the flight test, the developed UAV can successfully fly autonomously following the trajectory command. The result shows that low-cost material can be used as a body part of fixed-wing UAV.

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