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Adaptive target tracking with a mixed team of static and mobile guards: deployment and activation strategies

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Abstract

This work explores a variation of the art gallery problem in which a team of static and mobile guards track a mobile intruder with unknown maximum speed. We consider the special case when the mobile guards are restricted to move along the diagonals of a polygonal environment. First, we present an algorithm to identify candidate vertices in a polygon at which either static guards can be placed or they can serve as an endpoint of the segment on which mobile guards move. Next, we present a technique to partition the environment based on the triangulation of the environment, and allocate guards to each partition to track the intruder. The allocation strategy leads to a classification of the mobile guards based on their task and coordination requirements. Finally, we present a strategy to activate/deactivate static guards based on the speed of the intruder. Simulation results are presented to validate the efficacy of the proposed techniques.

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Notes

  1. In a triangulation graph all the faces are triangles.

  2. A triangulation graph G is said to be dominated by a set of vertices\(S_c\) if at least one vertex of each triangle in T(G) is a vertex in \(S_c\).

  3. A triangulation graph G is said to be dominated by a set of diagonals\(S_h\) if at least one vertex of each triangle in T(G) is an endpoint of a diagonal in \(S_h\).

  4. A diagonal is said to be incident to a vertex if the vertex is an endpoint of the diagonal.

  5. We say that a guard \(g_k \in S_g\backslash \{g_i\}\) is a neighbor of \(g_i\) if \(T(i) \cap T(k) \ne \emptyset \).

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Acknowledgements

This work was supported by the NSF Grant IIS-1816343.

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Correspondence to Sourabh Bhattacharya.

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This is one of the several papers published in Autonomous Robots comprising the Special Issue on Multi-Robot and Multi-Agent Systems.

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Laguna, G.J., Bhattacharya, S. Adaptive target tracking with a mixed team of static and mobile guards: deployment and activation strategies. Auton Robot 44, 691–703 (2020). https://doi.org/10.1007/s10514-019-09833-8

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