A Decentralised Strategy for Heterogeneous AUV Missions via Goal Distribution and Temporal Planning

Yaniel Carreno, Èric Pairet, Yvan Petillot, Ronald P. A. Petrick

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Heterogeneous multi-robot systems offer the potential to support complex missions, such as those needed for persistent autonomy in underwater domains. Such systems enable each robot to be optimised for specific tasks to better manage dynamic situations. In this context, temporal planning can generate plans to support the execution of multi-robot missions. However, the task distribution quality in the generated plans is often poor due to the strategies that existing planners employ to search for suitable actions, which do not tend to optimise task allocation. In this paper, we propose a new algorithm called the Decentralised Heterogeneous Robot Task Allocator (DHRTA) which enhances goal distribution by considering task spatial distribution, execution time, and the capabilities of the available robots. DHRTA is the first phase of our decentralised planning strategy which supports individual robot plan generation using temporal planners. Experiments illustrate the robustness of the approach and indicate improvements in plan quality by reducing the planning time, mission time and the rate of mission failures.
Original languageEnglish
Title of host publicationProceedings of the Thirtieth International Conference on Automated Planning and Scheduling
PublisherAAAI Press
Pages431-439
Number of pages9
Publication statusPublished - 1 Jun 2020

Publication series

NameProceedings of the International Conference on Automated Planning and Scheduling
Volume30
ISSN (Electronic)2334-0843

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems and Management

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    Carreno, Y., Pairet, È., Petillot, Y., & Petrick, R. P. A. (2020). A Decentralised Strategy for Heterogeneous AUV Missions via Goal Distribution and Temporal Planning. In Proceedings of the Thirtieth International Conference on Automated Planning and Scheduling (pp. 431-439). (Proceedings of the International Conference on Automated Planning and Scheduling; Vol. 30). AAAI Press. https://www.aaai.org/ojs/index.php/ICAPS/article/download/6738/6592/