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Multi-robot, dynamic task allocation: a case study

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Abstract

This article presents a subgrouping approach to the multi-robot, dynamic multi-task allocation problem. It utilizes the percentile values of the distributional information of the tasks to reduce the task space into a number of subgroups that are equal to the number of robotic agents. The subgrouping procedure takes place at run-time and at every designated decision-cycle to update the elements of these subgroups using the relocation information of the elements of the task space. Furthermore, it reduces the complexity of the decision-making process proportional to the number of agents via introduction of the virtual representatives for these subgroups. The coordination strategy then uses the votes of the robotic agents for these virtual representatives to allocate the available subgroups. We use the elapsed time, the distance traveled, and the frequency of the decision-cycle as metrics to analyze the performance of this strategy in contrast to the prioritization, the instantaneous, and the time-extended coordination strategies.

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Notes

  1. These representatives are not the actual subtasks. However, it is possible that the location of a representative coincides with a subtask at a given execution cycle.

  2. The formulation of the decision engine along with its analysis (both, theoretical and numerical) are provided in Keshmiri and Payandeh [54] and hence are not included in this article to avoid repetition.

  3. There are 24 permutations of the votes.

  4. Liu and Shell [56] introduce an interval-based version of the Hungarian algorithm. However, we do not use the interval-based version of the Hungarian algorithm in the context of the analysis of this article.

  5. The prioritization does not involve any decision-making. It coordinates the allocation of the subgroups to the robotic agents at the commencement of a mission preemptively.

  6. The upper boundary \(O(n^4)\) corresponds to the scenarios where \(m=n\).

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Authors and Affiliations

  1. Experimental Robotics Laboratory, School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada

    Soheil Keshmiri & Shahram Payandeh

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  1. Soheil Keshmiri
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  2. Shahram Payandeh
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Correspondence to Soheil Keshmiri.

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Keshmiri, S., Payandeh, S. Multi-robot, dynamic task allocation: a case study. Intel Serv Robotics 6, 137–154 (2013). https://doi.org/10.1007/s11370-013-0130-x

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