[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 211 communication abilities of agents are limited. The limitations for the purpose of this study are narrowed down to the range of the communication devices. That means the given agent is able to communicate only with those agents that are closer than some threshold value. Let us define the set, which determines the those agents that agent is able to communicate with: Φ , (7) It must be stressed that two agents are in communication only when the transmission of data can be made in both directions. The agent can receive information and its transmission can be received by the other one. This happens only if the following is satisfied: jΦ , , (8) where / denotes the range of communication devices of the and agent, while the value , in (8) is the distance between the and agent. 3 Agent Reliability Assessment As was mentioned before, the information fusion idea is based on the assumption that each agent is able to evaluate (during the task performance) its reliability related to the acquired sensory data correctness. In this section the method of reliability assessment will be presented. Let us first distinguish the discrete moment in the MAS operation and let us denote it by 0,1,2, … .In the beginning of the MAS operation ( 0) the reliability of agent is equal to some predefined value: 0 0,1 (9) Let us assume that agent in the moment detects the set of objects defined by (3). We will refer to this set hereafter as: , , (10) After detecting the objects defined by (10), the certainty factor is determined for each object detected according to the function (5), let us denote this estimated value as: ̂,, (11) While performing the task and picking up the objects (10) the agent is able to confront the real state of the process with its predictions. Let us introduce the factor that defines an error of the certainty assessment done by the agent with respect to the object detected: , ̂, , (12)
212 M. Mellado and K. Skrzypczyk where , 0,1 in (12) denotes a detection correctness of the object. If the object was detected correctly , takes the value 1 and 0 otherwise. Using the value of (12) we can determine the agent reliability based on a single picking up operation. The reliability value is calculated as a function of the (12). In this research an sigmoid function was applied to assign the value of reliability on the basis of the detection correctness error. Hence the reliability factor defined for agent using the picking up experiment is defined as: , 1 1 1 , where parameter tunes the steepness of the curve around the threshold . This parameter defines the maximal acceptable error (12), by which it is possible to set the agent reliability at the level of 1. Applying the equation (13) to each object from the set (10) it is possible to determine the reliability of the agent at the stage of the system operation. In this study it is obtained by averaging results: 1 ,, The detection and recognition process is performed on the basis of the data collected in the single operation cycle of the sensory system. Therefore calculating the agent reliability as the average given by (14) seems to be valid approach. Finally, the reliability of the agent at the stage of the process can be evaluated as an exponential moving average with a time window of length . It can be determined recursively using the following formula: (13) (14) 0 1 1, 0 (15) where is a constant smoothing factor which value can be from 0 to 1. It represents the weighting factors applied to the values calculated in the past. The higher value of the faster discounting older values is. In this study this coefficient is expressed in terms of the time window length: 2 (16) 1 Applying the above reasoning to every agent, we can assess their reliability at each stage of the process. 4 Data Fusion As was stated in the previous sections in the MAS information about the process performed is distributed among multiple agents. Due to different perception
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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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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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24 A. Babiarz et al. The sample cod
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Recognition and Location of Objects
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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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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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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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