Task allocation with executable coalitions in multirobot tasks

Yu Zhang, Lynne E. Parker

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

20 Scopus citations

Abstract

In our prior work, we proposed the IQ-ASyMTRe architecture with a measure of information quality to reason about forming coalitions in multirobot tasks. The formed coalitions are guaranteed to be executable, given the current configurations of the robots and environment. A cost and a quality measure are associated with each coalition to further determine its utility for the task. In this paper, we show that IQ-ASyMTRe-like architectures can be utilized to significantly reduce the overall complexity of task allocation by considering only executable coalitions. For implementation, we apply a layering technique such that most existing methods for task allocation can be easily incorporated. Furthermore, we introduce a general process to address situations in which no executable coalitions are available for certain tasks, and integrate it with IQ-ASyMTRe to achieve more autonomy. Such an approach is able to autonomously decompose unsatisfied preconditions of the required task behaviors into satisfiable components, in order to generate partial order plans for them accordingly. We show how this process can be implemented using a market-based approach. Simulation results are provided to demonstrate these techniques.

Original languageEnglish (US)
Title of host publication2012 IEEE International Conference on Robotics and Automation, ICRA 2012
Pages3307-3314
Number of pages8
DOIs
StatePublished - 2012
Externally publishedYes

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ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

Zhang, Y., & Parker, L. E. (2012). Task allocation with executable coalitions in multirobot tasks. In 2012 IEEE International Conference on Robotics and Automation, ICRA 2012 (pp. 3307-3314). [6224910] https://doi.org/10.1109/ICRA.2012.6224910