[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

Information Fusion in Multi-agent System Based on Reliability Criterion Martin Mellado and Krzysztof Skrzypczyk * Abstract. The paper addresses the problem of information fusion in Multi-Agent System. Since the knowledge of the process state is distributed between agents, the efficiency of the task performance depends on a proper information fusion technique applied to the agents. In this paper we study the case in which each agent has its own sensing device and is able to collect information with some certainty. Since the same information can be detected by multiple agents, the global certainty about the given fact derives from the fusion of information exchanged by interconnecting agents. The key issue in the method proposed, is an assumption that each agent is able to asses its own reliability during the task performance. The method is illustrated by the pick-up-and-collection task example. The effectiveness of the method proposed is evaluated using relevant simulation experiments. 1 Introduction In Multi-Agent Systems (MAS), a primary task the system is intended to perform is distributed between a number of entities (agents). The beginnings of this branch of science were inspired by Artificial Intelligence in the 1950's. It was recognized that there are a number of tasks that MAS can perform more efficiently than centralized, single unit systems. Thanks to one of the features of MAS - modularity, even the domain of complex problems which are sometimes unpredictable can be solved by a number of simple entities, specialized in solving a particular part of the primary problem [12]. The benefits that these systems can offer are obvious Martin Mellado Instituto de Automatica e Informatica Industrial, Universidad Politecnica de Valencia e-mail: krzysztof.skrzypczyk@polsl.pl, martin@ai2.upv.es Krzysztof Skrzypczyk Silesian University of Technology, Institute of Computer Science, Akademicka 16, 44-101 Gliwice, Poland A. Nawrat and Z. Kuś (Eds.): Vision Based Systems for UAV Applications, SCI 481, pp. 207–217. DOI: 10.1007/978-3-319-00369-6_13 © Springer International Publishing Switzerland 2013
208 M. Mellado and K. Skrzypczyk and compelling. On the other hand, there are a lot of challenges that must be met in order to design effective and robust systems that are able to solve problems or execute tasks. These challenges were discussed in [12] and it is enough to point out the problems like coordination [2], [4],[5], [13], [14] task division [3], [7], [10],[17], cooperation [1], [7], [8], [15] etc. The potential advantages of MAS were quickly noticed by researchers who deal with problems related to Robotics. They have discovered that some specific tasks whose execution requires building and operating complex, powerful robotic units, can be performed more effectively by simpler, highly specialized robots. There are a number of problems that have become benchmarks and test-beds for the quality assessment and analysis of Multi-Robot Systems (MRS) functioning. This paper addresses one of the most important issues related to MAS which is fusion of information provided by multiple agents. The knowledge of the process state is distributed between agents, the efficiency of the task performance depends on a proper information fusion technique applied to the agents. In this paper we study the case in which each agent has its own sensing device and is able to collect information with some certainty. Since the same information can be detected by multiple agents, the global certainty about the given fact derives from the fusion of information exchanged by interconnecting agents. The key issue in the method proposed, is an assumption that each agent is able to assess its own reliability during the task performance. The method is illustrated by the pick-up-and-collection task example. However an application of the fusion technique discussed in this paper is not narrowed down only to the problem defined. It can be used in any system that is able to assess its own performance quality. A good example is the system of detecting and recognition objects by a number of cameras observing the same scene. Each of camera is usually mounted in an opto-electronic gimbal [16]. The effectiveness of the method proposed is evaluated using relevant simulation experiments. 2 System Description The subject of this paper is the information fusion method in MAS. In order to present our approach we use the framework of the system dedicated for performing pick-up-and-collection task which consists of collecting a number of objects scattered inside a workspace of limited area. Figure 1 illustrates the principal idea of organizing the system. As was mentioned, there is an unknown number of objects to be collected, scattered over an area being the workspace of the system. The system consists of a specified number of robotic-units also called agents. Each agent can be defined by the following features [12]: • limited sensing abilities, • possibility of communication with limited number of other agents, • a set of actions that an agent is able to take, • reasoning about those actions based on its own knowledge.
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: Vision System for Group of Mobile R
Page 167 and 168: Vision System for Group of Mobile R
Page 169 and 170: 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: 206 Practical Applications of Class
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 and 228: 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 265 and 266: Selection of Individual Gait Featur
Page 267 and 268:
Selection of Individual Gait Featur
Page 269 and 270:
Page 271 and 272:
Page 273 and 274:
Page 275 and 276:
Page 277 and 278:
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

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?