[Studies in Computational Intelligence 481] Artur Babiarz, Robert Bieda, Karol Jędrasiak, Aleksander Nawrat (auth.), Aleksander Nawrat, Zygmunt Kuś (eds.) - Vision Based Systemsfor UAV Applications (2013, Sprin

Recommendations

Info

220 K. Daniec et al. The construction and service for unmanned aircraft is fairly expensive and it should be added that the cameras are usually the most expensive part of the aircraft. Furthermore, prototyping autonomous flight algorithms and vision based algorithms is time consuming and requires multiple tests and a priori assumptions. In prototyping the algorithms helpful appear to be a real-world simulators. Therein can be tested the control algorithms including the dynamics of flying object, GPS system simulation and among others earth model WGS-84. Using the simulator allows to run the tests without fear of destruction of the real object, and what goes with it, from possible accidents and exposure to the risk of third parties. Using a simulation of the real world we are making a large financial savings resulting from no need for rebuilding the model in the case of (undesirable) breakdown. We also save the time that we could spend much more, during the design of mechanisms for autonomous flight. Using a simulation environment Prepar3D® Lockheed Martin, we are able to get data from all kinds of sensors and also to control the flying object model. This environment has mapped physics, not only in the air, but also on land and in the water. Importantly, the simulation allows us to change the weather, which greatly influences the dynamics of moving objects in it. The simulation includes, for example, buildings and artificial traffic, which can be used to prototype the vision based algorithms. In addition, it should be mentioned, that realistic is also a terrain, as well as locations of all airports and cities. Main contribution of this paper are control algorithms for UAV on the set trajectory. This trajectory can overlap with the existing highway network, waterways and air routes. Thanks to this, it is possible to use a video stream from the onboard virtual camera for prototyping vision based algorithms. 2 Literature Review The Control of the unmanned objects based on vision is an active area of research. In many publications we met with topics devoted just to control of flying objects using a flight simulators. One of them, worth mentioning is the work of Eric R. Mueller [1], which was based on the studies carried out with Hardware-in-the- Loop simulation. Simulation was based there on Matlab/Simulink® mathematic model, while the control values was calculated by using LQR controllers [2]. Another important publication is the work of Eric N. Johnson and Sumit'a Mishra [3], which uses simulation both in SIL (Software In the Loop) and HIL (Hardware In the Loop) and the simulation visualization. In the will of the explanation, the SIL-loop simulation controls a physical process model (the simulator) using the software, which is a model of the regulator, which was run on the same hardware platform. In the HIL-simulation process is involved a real regulator, which communicates with the simulator through the appropriate interfaces. In the articles [4] and [5] the authors use a simulation environment implemented in the "Matlab/Simulink® 6-DOF", and visualization, "FlightGear". The simulator that was used there is characterized by a greater reality, because it includes the basic model of atmospheric, gravity and magnetic field.
Prototyping the Autonomous Flight Algorithms Using the Prepar3D® Simulator 221 Fig. 1. Simulator implemented in "C" language In the work [6] the flight simulator was implemented in "C" language and tak- ing into account an airplane model servos and simulated GPS (fig. 1). Procerus Technologies [7] developed even its own electronic system (autopilot), which allows to control both the real object as well as testing the HIL-simulation. Kestrel Autopilot is designed to control the UAV objects of flying wing type, where the control system is based on a PID controllers cascade. In closing we can mention the work of the Institute of Aviation [8],which uses the Kestrel autopilot system for designing robust optimal control based on H-infinity and μ-Synthesis method. The articles mentioned above describe only control algorithms, and the simulators characterized by them are not so advanced to support the image data processing from on-board cameras. Based on the experience of C. Kownacki [9], at the beginning we have performed autonomous flight control algorithms, that allowed us to get better re- sults with prototyping the vision based algorithms. It is important, that this article describes the use of images from simulation to road tracking algorithms. Image which was taken from a camera (fig. 2), was processed by the Sobel operator followed by linear Hough algorithm. Similar assumptions and conclusions are described in derivative works [10] and [11]. Fig. 2. Example of road edge detection [9]
Page 1 and 2:
Studies in Computational Intelligen
Page 3 and 4:
Aleksander Nawrat · Zygmunt Kuś E
Page 5 and 6:
“In God We Trust, All others we o
Page 7 and 8:
VIII Preface valuable information i
Page 9 and 10:
Contents Part I: Design of Object D
Page 11 and 12:
Contents XIII Technology Developmen
Page 13 and 14:
XVI List of Contributors Adam Czorn
Page 15 and 16:
XVIII List of Contributors Henryk M
Page 17 and 18:
XX List of Contributors Józef Wron
Page 19 and 20:
2 Design of Object Detection, Recog
Page 21 and 22:
4 A. Babiarz et al. 1 Introduction
Page 23 and 24:
6 A. Babiarz et al. The description
Page 25 and 26:
8 A. Babiarz et al. a) b) Fig. 5. T
Page 27 and 28:
10 A. Babiarz et al. o o o “micvo
Page 29 and 30:
12 A. Babiarz et al. a) b) Fig. 7.
Page 31 and 32:
14 A. Babiarz et al. • The camera
Page 33 and 34:
16 A. Babiarz et al. the camera ser
Page 35 and 36:
18 A. Babiarz et al. a) b) Fig. 11.
Page 37 and 38:
20 A. Babiarz et al. patrol point i
Page 39 and 40:
22 A. Babiarz et al. particular ang
Page 41 and 42:
24 A. Babiarz et al. The sample cod
Page 43 and 44:
Recognition and Location of Objects
Page 45 and 46:
Page 47 and 48:
Page 49 and 50:
Page 51 and 52:
Page 53 and 54:
Page 55 and 56:
Page 57 and 58:
Page 59 and 60:
Page 61 and 62:
Page 63 and 64:
48 P. Demski et al. Fig. 1. Automat
Page 65 and 66:
50 P. Demski et al. E-Grip firing m
Page 67 and 68:
52 P. Demski et al. 4 Automatic Tar
Page 69 and 70:
54 P. Demski et al. implementing su
Page 71 and 72:
Object Tracking for Rapid Camera Mo
Page 73 and 74:
Page 75 and 76:
Page 77 and 78:
Page 79 and 80:
Page 81 and 82:
Page 83 and 84:
Page 85 and 86:
Page 87 and 88:
Page 89 and 90:
Page 91 and 92:
Object Tracking in a Picture during
Page 93 and 94:
Page 95 and 96:
Page 97 and 98:
Page 99 and 100:
Page 101 and 102:
Page 103 and 104:
Page 105 and 106:
Page 107 and 108:
94 Construction of Image Acquisitio
Page 109 and 110:
96 G. Bieszczad et al. effectivenes
Page 111 and 112:
98 G. Bieszczad et al. such a way t
Page 113 and 114:
100 G. Bieszczad et al. Table 1. No
Page 115 and 116:
102 G. Bieszczad et al. Fig. 7. Pri
Page 117 and 118:
104 G. Bieszczad et al. Fig. 10. Re
Page 119 and 120:
106 G. Bieszczad et al. Fig. 13. Ti
Page 121 and 122:
108 G. Bieszczad et al. Fig. 16. Op
Page 123 and 124:
110 G. Bieszczad et al. has to make
Page 125 and 126:
112 G. Bieszczad et al. MTF functio
Page 127 and 128:
114 G. Bieszczad et al. [11] Krupi
Page 129 and 130:
116 D. Bereska et al. Presented in
Page 131 and 132:
118 D. Bereska et al. Fig. 2. Image
Page 133 and 134:
120 D. Bereska et al. Fig. 4. Ortho
Page 135 and 136:
Omnidirectional Video Acquisition D
Page 137 and 138:
Page 139 and 140:
Page 141 and 142:
Page 143 and 144:
Page 145 and 146:
Page 147 and 148:
Page 149 and 150:
Part III Design of Vision Based Con
Page 151 and 152:
Vision System for Group of Mobile R
Page 153 and 154:
Page 155 and 156:
Page 157 and 158:
Page 159 and 160:
Page 161 and 162:
Page 163 and 164:
Page 165 and 166:
Page 167 and 168:
Page 169 and 170:
A Distributed Control Group of Mobi
Page 171 and 172:
Page 173 and 174:
Page 175 and 176:
Page 177 and 178: A Distributed Control Group of Mobi
Page 189 and 190: 178 D. Bereska et al. The aim of th
Page 191 and 192: 180 D. Bereska et al. 3 Control Alg
Page 193 and 194: 182 D. Bereska et al. Fig. 5. Schem
Page 195 and 196: 184 D. Bereska et al. 4.1 Mechanica
Page 197 and 198: 186 D. Bereska et al. 4.1.3 Open-Lo
Page 199 and 200: 188 D. Bereska et al. Fig. 16. Comp
Page 201 and 202: Probabilistic Approach to Planning
Page 215 and 216: 206 Practical Applications of Class
Page 217 and 218: 208 M. Mellado and K. Skrzypczyk an
Page 219 and 220: 210 M. Mellado and K. Skrzypczyk a
Page 221 and 222: 212 M. Mellado and K. Skrzypczyk wh
Page 223 and 224: 214 M. Mellado and K. Skrzypczyk Fi
Page 225 and 226: 216 M. Mellado and K. Skrzypczyk 6
Page 227: Prototyping the Autonomous Flight A
Page 231 and 232: Prototyping the Autonomous Flight A
Page 241 and 242: Feature Extraction and HMM-Based Cl
Page 255 and 256: 248 K. Daniec et al. In this articl
Page 257 and 258: 250 K. Daniec et al. Fig. 2. Applic
Page 259 and 260: 252 K. Daniec et al. by the ability
Page 261 and 262: 254 K. Daniec et al. The idea of us
Page 263 and 264: Selection of Individual Gait Featur
Page 279 and 280:
274 A. Nawrat et al. alia weather c
Page 281 and 282:
276 A. Nawrat et al. have an embedd
Page 283 and 284:
278 A. Nawrat et al. particular cha
Page 285 and 286:
280 A. Nawrat et al. • 1-phase el
Page 287 and 288:
282 A. Nawrat et al. The phase cond
Page 289 and 290:
284 A. Nawrat et al. Table 1. Compa
Page 291 and 292:
286 A. Nawrat et al. P3 P3 P3 71101
Page 293 and 294:
288 A. Nawrat et al. S, VA 4,510 4,
Page 295 and 296:
290 A. Nawrat et al. Comparison dif
Page 297 and 298:
Technology Development of Military
Page 299 and 300:
Page 301 and 302:
Page 303 and 304:
Page 305 and 306:
Page 307 and 308:
Page 309 and 310:
Page 311 and 312:
Page 313 and 314:
Page 315 and 316:
312 A. Czornik, A. Nawrat, and M. N
Page 317 and 318:
Page 319 and 320:
Page 321 and 322:
Page 323 and 324:
Page 325 and 326:
Page 327 and 328:
Page 329 and 330:
Page 331 and 332:
328 M. Koźlak, A. Kurzeja, and A.
Page 333 and 334:
Page 335 and 336:
Page 337 and 338:
Page 339 and 340:
Page 341 and 342:
Page 343 and 344:
Page 345 and 346:
Page 347 and 348:
344 Conclusions Control algorithms
show all

[Studies in Computational Intelligence 481] Artur Babiarz, Robert Bieda, Karol Jędrasiak, Aleksander Nawrat (auth.), Aleksander Nawrat, Zygmunt Kuś (eds.) - Vision Based Systemsfor UAV Applications (2013, Sprin

Create successful ePaper yourself

Delete template?

Save as template?