[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

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Information Fusion in Multi-agent System Based on Reliability Criterion 209 Since agents can communicate with each other they can exchange information about objects that are detected by their sensing devices and this way increase their knowledge of the number and location of objects sensed by other team mates. The subject of the paper is the method of agents sensing fusion which results in improvement of the process global knowledge. Fig. 1. Diagram that illustrated organization of the MAS being the subject of the discussion 2.1 Workspace The workspace is a constrained, unstructured, flat area where a number of objects the robots are intended to collect are scattered. The objects are defined by a set of indices: , 1,2,…, (1) where is the total number of objects located inside the workspace. It has to be stressed that this number is not known to any agent. The location of each object is defined by a pair of coordinates , , , . No data of these positions are provided to particular agents. 2.2 Agents Description The system consists of robotic agents that are equipped with a sensory system that allows them to detect objects and determine the location of detected objects. Let us define the team of the robotic agents as a set: , 1,2,…, (2)
210 M. Mellado and K. Skrzypczyk a pose of each agent is determined by a triple , , , ,Θ , , where the elements denote coordinates of the center and heading of the agent defined in the workspace coordinate frame. 2.3 Sensing Abilities Each robotic agent is provided with perception devices that allow it to detect objects in its vicinity. Therefore, let us define a set of objects detected by the agent as a set of indices: , , (3) The perception of the agent is limited by the range of its sensors. The range of the sensors is denoted by , , which means that only those objects can be detected by the agents satisfying the following: , , , , , , (4) Each agent is able to assess its detection quality. It is defined by certainty factor which varies in range from 0 to 1. The design of detection model and certainty evaluation is not the subject of this paper. For the purpose of the study we assume that detection certainty of the agent is influenced by two factors. The first one is related to its perception abilities which (it this study) decreases with the distance , between the agent and object. The second factor influencing the certainty is a random process related to the sensing devices inaccuracy, noise etc. The influence level of particular factors is weighted by coefficients and the sum of which is equal to one. Using those coefficients we can simulate the sensing reliability of the given agent. In the study presented the detection certainty of the object by the agent is modeled as: ̂, , , 12 , (5) where 1 and 0,1 is a random number that characterize the influence of inaccuracy and uncertainty. The component , is related to perception abilities of the agent and is defined as: , 1 1 1 , , (6) Balancing the influence of the random and detection part of the model we can simulate reliability of particular agents. 2.4 Communication Agents are provided with communication devices that allow the exchange of information and coordination of mutual actions. It is also assumed that the
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Studies in Computational Intelligen
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Aleksander Nawrat · Zygmunt Kuś E
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“In God We Trust, All others we o
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VIII Preface valuable information i
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Contents Part I: Design of Object D
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Contents XIII Technology Developmen
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XVI List of Contributors Adam Czorn
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XVIII List of Contributors Henryk M
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XX List of Contributors Józef Wron
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2 Design of Object Detection, Recog
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4 A. Babiarz et al. 1 Introduction
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6 A. Babiarz et al. The description
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8 A. Babiarz et al. a) b) Fig. 5. T
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10 A. Babiarz et al. o o o “micvo
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12 A. Babiarz et al. a) b) Fig. 7.
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14 A. Babiarz et al. • The camera
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16 A. Babiarz et al. the camera ser
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18 A. Babiarz et al. a) b) Fig. 11.
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20 A. Babiarz et al. patrol point i
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22 A. Babiarz et al. particular ang
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24 A. Babiarz et al. The sample cod
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Recognition and Location of Objects
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48 P. Demski et al. Fig. 1. Automat
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50 P. Demski et al. E-Grip firing m
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52 P. Demski et al. 4 Automatic Tar
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54 P. Demski et al. implementing su
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Object Tracking for Rapid Camera Mo
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Object Tracking in a Picture during
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94 Construction of Image Acquisitio
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96 G. Bieszczad et al. effectivenes
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98 G. Bieszczad et al. such a way t
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100 G. Bieszczad et al. Table 1. No
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102 G. Bieszczad et al. Fig. 7. Pri
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108 G. Bieszczad et al. Fig. 16. Op
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110 G. Bieszczad et al. has to make
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112 G. Bieszczad et al. MTF functio
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114 G. Bieszczad et al. [11] Krupi
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116 D. Bereska et al. Presented in
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118 D. Bereska et al. Fig. 2. Image
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120 D. Bereska et al. Fig. 4. Ortho
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Omnidirectional Video Acquisition D
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Part III Design of Vision Based Con
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Vision System for Group of Mobile R
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274 A. Nawrat et al. alia weather c
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276 A. Nawrat et al. have an embedd
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278 A. Nawrat et al. particular cha
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280 A. Nawrat et al. • 1-phase el
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282 A. Nawrat et al. The phase cond
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284 A. Nawrat et al. Table 1. Compa
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286 A. Nawrat et al. P3 P3 P3 71101
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288 A. Nawrat et al. S, VA 4,510 4,
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290 A. Nawrat et al. Comparison dif
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Technology Development of Military
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344 Conclusions Control algorithms
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[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

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