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A Cross-Landscape Evaluation of Multi-robot Team Performance in Static Task-Allocation Domains

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Towards Autonomous Robotic Systems (TAROS 2019)

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Abstract

The performance of a multi-robot team varies when certain environmental parameters change. The study presented here examines the performance of four task allocation mechanisms, compared across a mission landscape that is defined by a set of environmental conditions. The landscape is categorised by three dimensions: (1) single-robot versus multi-robot tasks; (2) independent versus constrained task correspondence; and (3) static versus dynamic allocation of tasks with respect to mission execution. Two different task scenarios and two different starting formations were implemented with each environmental condition. Experiments were conducted on teams of simulated and physical robots, to demonstrate the portability of the results. This paper investigates the “static allocation” portion of the mission landscape, filling in a gap that has not been investigated previously. Experimental results are presented which confirm that the previous conclusion still holds: there is no single task allocation mechanism that consistently ranks best in performance when tasks are executed.

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Notes

  1. 1.

    http://www.robotis.us/turtlebot-3/.

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Authors and Affiliations

  1. Depaerment of Informatics, King’s College London, London, UK

    Dingdian Zhang, Eric Schneider & Elizabeth Sklar

Authors
  1. Dingdian Zhang
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  2. Eric Schneider
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  3. Elizabeth Sklar
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Correspondence to Dingdian Zhang .

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Editors and Affiliations

  1. Queen Mary University of London, London, UK

    Kaspar Althoefer

  2. Queen Mary University of London, London, UK

    Jelizaveta Konstantinova

  3. Queen Mary University of London, London, UK

    Ketao Zhang

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Zhang, D., Schneider, E., Sklar, E. (2019). A Cross-Landscape Evaluation of Multi-robot Team Performance in Static Task-Allocation Domains. In: Althoefer, K., Konstantinova, J., Zhang, K. (eds) Towards Autonomous Robotic Systems. TAROS 2019. Lecture Notes in Computer Science(), vol 11650. Springer, Cham. https://doi.org/10.1007/978-3-030-25332-5_23

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  • DOI: https://doi.org/10.1007/978-3-030-25332-5_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-25331-8

  • Online ISBN: 978-3-030-25332-5

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