Micro Aerial Vehicles

This thesis describes a dynamic height estimator and controller for rotorcraft landing and hovering in ground effect based on flowfield sensing and modeling. The rotor downwash in ground effect is represented using a ring-source potential flow model selected for real-time use. Experimental verification of the flow model and an augmented flow model for tilt are presented. A nonlinear dynamic model of a compound pendulum heave test stand that reduces to the dynamics of a rotorcraft in ground effect is presented with open-loop analysis and closed-loop control simulation. Equations of motion and stability characterization of of a heaving rotor IGE are derived for external perturbations and it is shown that a uniform sideward wind does not cause instability and uniform axial wind from the top can cause instability. Flowfield velocity measurements are assimilated into a grid-based recursive Bayesian filter to estimate height above ground in both simulation and experiment. Height tracking in ground effect and landing using the estimated height are implemented with a dynamic linear controller in both simulation and experiment. Mean estimation and motion errors are found to b e no greater than 5% and 9% respectively, demonstrating that height estimation and control is possible with only flow sensing and modeling.

We would like to invite you to join this exciting new project as a chapter contributor. Since this is a textbook, a great deal of this chapter entails a survey on the topic under the paradigm of cyber-physical systems, what can be done onboard and remotely, the distributed nature of the system and some exercises on futurology (anticipating trends can shed some light on upcoming designs). IET will bring great visibility to your work. You are welcome to suggest another topic/chapter title if you feel it would be more suitable. Each chapter should be around 20-25 pages each and can be submitted as a Word or Latex File. The IET will send you additional information (formatting, permission form, etc.) with the contributor's agreement once you have agreed to contribute to the book. Visit http:// www.theiet.org/resources/author-hub/books/index.cfm to get all information you need as a contributor to an IET research-level book. Each book is expected to have a total number of 500 printed pages (based on approximately 550 words per page with a 20% allowance for figures and tables). We have included a tentative schedule and list of topics below. If this is something you would consider, please send me the title of your chapter, a short description/abstract of the chapter content, and your full contact details. We will expect original content and new results for this book. You can, of course, reuse published material but the percentage of material reuse for the chapter should be less than 40%. The IET will run a piracy software on the full manuscript to control that you are including original material and will reject chapters who contain a large amount of already-published material so please do take this into consideration.

The present study is devoted to the study of the flapping wing of an

ornithopter using computational fluid dynamics (CFD) and the ex-
perimental data from the obtained from the experiments. With this

bigger picture in mind, the problem is divided into three sub-problems.
Firstly, the flapping and the twisting of the wings are modelled with
fourier series using the experimental data obtained. The geometry
of the wing is considered as a thin plate with no thickness. This is

modelled using tetrahedron elements to mesh the computational do-
main. For the motion of the mesh we are using the overset meshing

technique. The forces and moments about all the axes are estimated
in the simulation.
In the next phase of this study we are planning to implement the same
simulation techniques to more complex flapping wing mechanisms.

All of these CFD simulations are performed in 3-Dimensional compu-
tational domain using ANSYS FLUENT and the post-processing is

done using ANSYS CFD-Post. The k-ω SST model is used to model

the turbulence of the flow. The solver used in this study is Roe flux-
difference splitting (Roe-FDS).

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Computer Vision (CV) and Sensors play a decisive role in the operation of Unmanned Aerial Vehicle (UAV), but there exists a void when it comes to analysing the extent of their impact on the entire UAV system. In general, the fact that a UAV is a Cyber-Physical System (CPS) is not taken into account. In this proposal, we propose to expand on earlier books covering the use of CV and sensing in UAVs. Among other things, an entirely autonomous UAV can help to (i) obtain information about the environment, (ii) work for an extended period of time without human interference, (iii) move either all or part of itself all over its operating location devoid of human help and (iv) stay away from dangerous situations for people and their possessions. A Vision System (VS) entails the way CV data will be utilized, the appropriate architecture for total avionics integration, the control interfaces, and the UAV operation. Since the VS core is its sensors and cameras, multi-sensor fusion, navigation, hazard detection, and ground correlation in real time are important operational aspects that can benefit from CV knowledge and technology. This book will aim to collect and shed some light on the existing information on CV software and hardware for UAVs as well as pinpoint aspects that need additional thinking. It will list standards and a set of prerequisites (or lack of them thereof) when it comes to CV deployment in UAVs. The issue of data fusion takes a centre place when the book explores ways to deal with sensor data and images as well as their integration and display. The best practices to fuse image and sensor information to enhance UAV performance by means of CV can greatly improve all aspects of the corresponding CPS. The CPS viewpoint can improve the way UAVs interact with the Internet of Things (IoT), use cloud computing, meet communications requirements, implement hardware/software paradigms necessary to handle video streaming, incorporate satellite data, and combine CV with Virtual/Augmented Realities.

VOLUME 1-CONTROL AND PERFORMANCE: This tome explores how sensors and computer vision technologies are used in unmanned aerial vehicles for the navigation, control, stability, reliability, guidance, fault detection, self-maintenance, strategic replanning and reconfiguration of the entire system. It helps analyse the manner UAVs interact with the Internet of Things (IoT), use cloud computing, meet communications requirements, implement hardware/software paradigms necessary to handle still imagery, video streaming, incorporate satellite data, and combine computer vision with virtual/augmented realities (VR/AR).NB: This is planned to be the companion volume of Estrela, Hemanth, Saotome (Eds) / Imaging and Sensing for Unmanned Aerial Vehicles: Volume 2-Deployment and Applications

Position
ontrol of Mi
ro Air Vehi
les (MAV) is
hallenging, be
ause position measurements by global naviga-
tion satellite systems or laser s
anners are typi
ally available at mu
h lower rates than the
ontrol frequen
y.
Furthermore, the transient response of
lassi
PID
ontrollers is either slow or indu
es overshoot.
In this work, we address this issue by a model-based
ontrol approa
h. We model and identify the dynami
s of
the MAV and use this knowledge in a nonlinear
as
aded
ontroller to generate time-optimal traje
tories. The
proposed method is evaluated in simulation and two real MAVs.

We would like to invite you to join this exciting new project as a chapter contributor on one of the topics listed below. Since this is a textbook, a great deal of this chapter entails a survey on the topic under the paradigm of cyber-physical systems, what can be done onboard and remotely, the distributed nature of the system and some exercises on futurology (anticipating trends can shed some light on upcoming designs). IET will bring great visibility to your work. Each chapter should be around 20-25 pages each and can be submitted as a Word or Latex File. The IET will send you additional info (formatting, permission form, etc.) with the contributor's agreement once you have decided to contribute to the book. Visit http://www.theiet.org/resources/author-hub/books/index.cfm to get all contributor's information to an IET research-level book. Each book is expected to have a total number of 500 printed pages (with approximately 550 words per page and a 20% allowance for figures and tables). We have included a tentative schedule and list of topics below. If this is something you would consider, please send us the title of your chapter, a short description/abstract of the chapter content, and your full contact details. We will expect original content and new insights for this book. You can, of course, reuse published material but the percentage of material reuse for the chapter should be less than 40%. The IET will run a piracy software on the full manuscript to control that you are including original material and will reject chapters who contain a large amount of already-published material so please do take this into consideration. We would appreciate your feedback by December 31, 2017. Please do not hesitate to contact us if you have any queries. We look forward to working with you towards the successful publication.

AbstrAct: The cooperative control of small unmanned aerial vehicles such as the multicopters has been extensively investigated worldwide for functionality augmentation and cost reduction with respect to a single larger vehicle. The present paper proposes a software-in-the-loop simulation scheme for performance evaluation and demonstration of formation flight control systems of multicopters. The simulation scheme consists of a computer network where each computer simulates one of the vehicles using the MATLAB/Simulink for implementing the local control system and the X-Plane for simulating the flight dynamics and environment. For cooperation, the local control systems exchange position data by means of the network. In order to illustrate the proposed scheme, a group of 3 octocopters is taken into consideration and a leader-follower strategy is chosen for triangular position formation, with the leader moving in a straight line with constant speed.

Flapping Wings MAV (FMWAV) is the result of UAV technology development with the mechanism of flapping wings and with the dimensions of the object as small as possible. Modeling of FWMAV dynamics is needed to predict future conditions based on information from current conditions and designed control actions. Modeling of the FWMAV system is done by system identification, based on measurable data from IMU sensors. Modeling is done each with a black-box approach using the N4SID estimation, while the grey-box approach is used structure parametric [3], using SSEST estimation. Then a fit model of these two models is compared by plotting the Paparazzi log with the output of the system obtained in MATLAB for the same input. Then this model will be simulated on SIMULINK. The automated PID Tuning is perform to obtain PID constants to be implemented into Paparazzi software for Pitch Loop and Roll Loop.

Volume 1 concentrates on UAS control and performance methodologies including Computer Vision and Data Storage, Integrated Optical Flow for Detection and Avoidance Systems, Navigation and Intelligence, Modeling and Simulation, Multisensor Data Fusion, Vision in Micro-Aerial Vehicles (MAVs), Computer Vision in UAV using ROS, Security Aspects of UAV and Robot Operating System, Vision in Indoor and Outdoor Drones, Sensors and Computer Vision, and Small UAVP for Persistent Surveillance.

Volume 1 concentrates on UAS control and performance methodologies including Computer Vision and Data Storage, Integrated Optical Flow for Detection and Avoidance Systems, Navigation and Intelligence, Modeling and Simulation, Multisensor Data Fusion, Vision in Micro-Aerial Vehicles (MAVs), Computer Vision in UAV using ROS, Security Aspects of UAV and Robot Operating System, Vision in Indoor and Outdoor Drones, Sensors and Computer Vision, and Small UAVP for Persistent Surveillance.