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Path planning algorithm ensuring accurate localization of radiation sources


An autonomous search for sources of gamma radiation in an outdoor environment is a domain suitable for the deployment of a heterogeneous robotic team, consisting of an Unmanned Aerial (UAV) and an Unmanned Ground (UGV) Vehicle. The UAV is convenient for fast mapping of the area and identifying regions of interest, whereas the UGV can perform highly accurate localization. It is assumed that the regions of interest are identified by the UAV during an initial reconnaissance, while performing a simple motion pattern. This paper proposes a path planning algorithm for the UGV, which guarantees accurate source localization in multiple preselected regions and minimizes the total path length. The problem is formulated as the Generalized Travelling Salesman Problem (GTSP) defined for discrete sets of suitable maneuvers (circular arcs), ensuring source localization in the given regions. The problem is successfully solved by a modified version of the state of the art GTSP solver, Generalized Large Neighborhood Search with Arcs (GLNSarc). Apart from adapting the GLNS, other aspects of the planning task are addressed: problem discretization and informed sampling of valid circular arcs, variants of weighting the nonrestricted trajectory segments between the arcs and postprocessing of the discretely planned trajectory in the continuous domain.

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This work has been supported by the European Regional Development Fund under the project Robotics for Industry 4.0 (registration no. CZ.02.1.01/0.0/0.0/15 003/0000470). The work of David Woller has been also supported by the Grant Agency of the Czech Technical University in Prague, grant SGS18/206/OHK3/3T /37.

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Correspondence to David Woller.

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Woller, D., Kulich, M. Path planning algorithm ensuring accurate localization of radiation sources. Appl Intell 52, 9574–9596 (2022).

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  • Search for radiation sources
  • Combinatorial optimization
  • Generalized Large Neighborhood Search
  • Generalized Travelling Salesman Problem
  • Heuristics
  • Metaheuristics