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Cost-Based Target Selection Techniques Towards Full Space Exploration and Coverage for USAR Applications in a Priori Unknown Environments

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Abstract

Full coverage and exploration of an environment is essential in robot rescue operations where victim identification is required. Three methods of target selection towards full exploration and coverage of an unknown space oriented for Urban Search and Rescue (USAR) applications have been developed. These are the Selection of the closest topological node, the Selection of the minimum cost topological node and the Selection of the minimum cost sub-graph. All methods employ a topological graph extracted from the Generalized Voronoi Diagram (GVD), in order to select the next best target during exploration. The first method utilizes a distance metric for determining the next best target whereas the Selection of the minimum cost topological node method assigns four different weights on the graph’s nodes, based on certain environmental attributes. The Selection of the minimum cost sub-graph uses a similar technique, but instead of single nodes, sets of graph nodes are examined. In addition, a modification of A* algorithm for biased path creation towards uncovered areas, aiming at a faster spatial coverage, is introduced. The proposed methods’ performance is verified by experiments conducted in two heterogeneous simulated environments. Finally, the results are compared with two common exploration methods.

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Authors and Affiliations

  1. Faculty of Engineering, Department of Electrical and Computer Engineering, Division of Electronics and Computer Engineering, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece

    E. G. Tsardoulias, A. Iliakopoulou, A. Kargakos & L. Petrou

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  1. E. G. Tsardoulias
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  2. A. Iliakopoulou
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  3. A. Kargakos
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  4. L. Petrou
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Correspondence to E. G. Tsardoulias.

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Tsardoulias, E.G., Iliakopoulou, A., Kargakos, A. et al. Cost-Based Target Selection Techniques Towards Full Space Exploration and Coverage for USAR Applications in a Priori Unknown Environments. J Intell Robot Syst 87, 313–340 (2017). https://doi.org/10.1007/s10846-016-0434-0

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  • DOI: https://doi.org/10.1007/s10846-016-0434-0

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