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Towards Long-Term Autonomy Based on Temporal Planning

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Towards Autonomous Robotic Systems (TAROS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11650))

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Abstract

This paper investigates the application of temporal planning to multiple robots in long-term missions, using the OPTIC and POPF temporal planners. We design a new planning domain, motivated by a realistic indoor-outdoor scenario. In particular, we investigate plan concurrency, makespan and plan generation time in the multi-robot problem and propose a schema which has been shown to improve plan quality while significantly reducing planning time for the multi-agent problem. Experiments are done in simulation using ROS and Gazebo, and demonstrated in missions with concurrent actions. The ROSPlan framework is also extended to work with multiple robots and used to integrate the planners in ROS. OPTIC provides the best overall solution considering the domain complexity and mission execution in the environment.

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Notes

  1. 1.

    Domain and problem examples are available at https://github.com/MA-TemporalP.

  2. 2.

    In the following, we will use ?r to denote a parameter of type robot, ?wp a parameter of type waypoint, ?o a parameter of type observation_point, and ?s a parameter of type sensors.

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Authors and Affiliations

  1. Edinburgh Centre for Robotics, Heriot-Watt University, Edinburgh, UK

    Yaniel Carreno, Ronald P. A. Petrick & Yvan Petillot

Authors
  1. Yaniel Carreno
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  2. Ronald P. A. Petrick
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  3. Yvan Petillot
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Corresponding author

Correspondence to Yaniel Carreno .

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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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Carreno, Y., Petrick, R.P.A., Petillot, Y. (2019). Towards Long-Term Autonomy Based on Temporal Planning. 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_13

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  • DOI: https://doi.org/10.1007/978-3-030-25332-5_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-25331-8

  • Online ISBN: 978-3-030-25332-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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