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International Journal of Intelligent Unmanned Systems
Number of Followers: 4  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 2049-6427
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  • Two-agent cooperative search model with Petri nets
    • Pages: 162 - 173
      Abstract: International Journal of Intelligent Unmanned Systems, Volume 6, Issue 4, Page 162-173, October 2018.
      Purpose The purpose of this paper is to propose a model for a two-agent multi-target-searching scenario in a two-dimensional region, where some places of the region have limited resource capacity in terms of the number of agents that can simultaneously pass through those places and few places of the region are unreachable that expand with time. The proposed cooperative search model and Petri net model facilitate the search operation considering the constraints mentioned in the paper. The Petri net model graphically illustrates different scenarios and helps the agents to validate the strategies. Design/methodology/approach In this paper, the authors have applied an optimization approach to determine the optimal locations of base stations, a cooperative search model, inclusion–exclusion principle, Cartesian product to optimize the search operation and a Petri net model to validate the search technique. Findings The proposed approach finds the optimal locations of the base stations in the region. The proposed cooperative search model allows various constraints such as resource capacity, time-dependent unreachable places/obstacles, fuel capacities of the agents, two types of targets assigned to two agents and limited sortie lengths. On the other hand, a Petri net model graphically represents whether collisions/deadlocks between the two agents are possible or not for a particular combination of paths as well as effect of time-dependent unreachable places for different combination of paths are also illustrated. Practical implications The problem addressed in this paper is similar to various real-time problems such as rescue operations during/after flood, landslide, earthquake, accident, patrolling in urban areas, international borders, forests, etc. Thus, the proposed model can benefit various organizations and departments such as rescue operation authorities, defense organizations, police departments, etc. Originality/value To the best of the authors’ knowledge, the problem addressed in this paper has not been completely explored, and the proposed cooperative search model to conduct the search operation considering the above-mentioned constraints is new. To the best of the authors’ knowledge, no paper has modeled time-dependent unreachable places with the help of Petri net.
      Citation: International Journal of Intelligent Unmanned Systems
      PubDate: 2018-10-22T01:24:30Z
      DOI: 10.1108/IJIUS-01-2018-0001
       
  • The conceptual view of unmanned aerial vehicle implementation as a mobile
           communication node of active data transmission network
    • Pages: 174 - 183
      Abstract: International Journal of Intelligent Unmanned Systems, Volume 6, Issue 4, Page 174-183, October 2018.
      Purpose The purpose of this paper is to propose the basis for the unification of unmanned aerial vehicle (UAV) group control protocols for the fast deployment of communication network on territories unsuitable for stationary nodes placement. Design/methodology/approach The paper proposes the application of active data (AD) conception in which the data exist in a form of executable code allowing data packets to control its own propagation through network. The implementation is illustrated for some scenarios of UAV data communication network deployment, i.e., transmission of the AD using navigation functions and dynamic reconfiguration of the nodes. Findings The conception of AD expands the range of possible UAV group operations due to on-the-fly adaptation abilities to changes in existing or forthcoming group behavior protocols. This allows the real-time change of data transmission formats, frequency ranges, modulation types, radio network topologies which, in turn, provides the ability to dynamically form the special data transmission networks from a general purpose device temporarily reconfiguring them for data transmission task between transmitter and receiver beyond radio visibility range. Practical implications The paper includes use cases for some situation of UAV data communication network deployment. Originality/value The paper aims to expand the UAV group control principles by implementing by rapid adaptation to changes in existing or forthcoming group behavior protocols.
      Citation: International Journal of Intelligent Unmanned Systems
      PubDate: 2018-10-22T01:24:29Z
      DOI: 10.1108/IJIUS-04-2018-0010
       
  • Unmanned machine vision system for automated recognition of mechanical
           parts
    • Pages: 184 - 196
      Abstract: International Journal of Intelligent Unmanned Systems, Volume 6, Issue 4, Page 184-196, October 2018.
      Purpose The field of machine vision, or computer vision, has been growing at fast pace. The growth in this field, unlike most established fields, has been both in breadth and depth of concepts and techniques. Machine vision techniques are being applied in areas ranging from medical imaging to remote sensing, industrial inspection to document processing and nanotechnology to multimedia databases. The goal of a machine vision system is to create a model of the real world from images. Computer vision recognition has attracted the attention of researchers in many application areas and has been used to solve many ranges of problems. The purpose of this paper is to consider recognition of objects manufactured in mechanical industry. Mechanically manufactured parts have recognition difficulties due to manufacturing process including machine malfunctioning, tool wear and variations in raw material. This paper considers the problem of recognizing and classifying the objects of such parts. RGB images of five objects are used as an input. The Fourier descriptor technique is used for recognition of objects. Artificial neural network (ANN) is used for classification of five different objects. These objects are kept in different orientations for invariant rotation, translation and scaling. The feed forward neural network with back-propagation learning algorithm is used to train the network. This paper shows the effect of different network architecture and numbers of hidden nodes on the classification accuracy of objects. Design/methodology/approach The overall goal of this research is to develop algorithms for feature-based recognition of 2D parts from intensity images. Most present industrial vision systems are custom-designed systems, which can only handle a specific application. This is not surprising, since different applications have different geometry, different reflectance properties of the parts. Findings Classification accuracy is affected by the changing network architecture. ANN is computationally demanding and slow. A total of 20 hidden nodes network structure produced the best results at 500 iterations (90 percent accuracy based on overall accuracy and 87.50 percent based on κ coefficient). So, 20 hidden nodes are selected for further analysis. The learning rate is set to 0.1, and momentum term used is 0.2 that give the best results architectures. The confusion matrix also shows the accuracy of the classifier. Hence, with these results the proposed system can be used efficiently for more objects. Originality/value After calculating the variation of overall accuracy with different network architectures, the results of different configuration of the sample size of 50 testing images are taken. Table II shows the results of the confusion matrix obtained on these testing samples of objects.
      Citation: International Journal of Intelligent Unmanned Systems
      PubDate: 2018-10-22T01:24:33Z
      DOI: 10.1108/IJIUS-03-2018-0008
       
  • Evolutionary-learning framework: improving automatic swarm robotics design
    • Pages: 197 - 215
      Abstract: International Journal of Intelligent Unmanned Systems, Volume 6, Issue 4, Page 197-215, October 2018.
      Purpose The purpose of this paper is to review the current state of proceedings in the research area of automatic swarm design and discusses possible solutions to advance swarm robotics research. Design/methodology/approach First, this paper begins by reviewing the current state of proceedings in the field of automatic swarm design to provide a basic understanding of the field. This should lead to the identification of which issues need to be resolved in order to move forward swarm robotics research. Then, some possible solutions to the challenges are discussed to identify future directions and how the proposed idea of incorporating learning mechanism could benefit swarm robotics design. Lastly, a novel evolutionary-learning framework for swarms based on epigenetic function is proposed with a discussion of its merits and suggestions for future research directions. Findings The discussion shows that main challenge which is needed to be resolved is the presence of dynamic environment which is mainly caused by agent-to-agent and agent-to-environment interactions. A possible solution to tackle the challenge is by incorporating learning capability to the swarm to tackle dynamic environment. Originality/value This paper gives a new perspective on how to improve automatic swarm design in order to move forward swarm robotics research. Along with the discussion, this paper also proposes a novel framework to incorporate learning mechanism into evolutionary swarm using epigenetic function.
      Citation: International Journal of Intelligent Unmanned Systems
      PubDate: 2018-10-22T01:24:34Z
      DOI: 10.1108/IJIUS-06-2018-0016
       
 
 
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