Integrating State-Based Multi-Agent Task Allocation and Physical Simulators

Daniel Rivas*, Lluís Ribas-Xirgo

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review


The multi-robot task allocation (MRTA) systems face the challenge of adapting to dynamic environments where new tasks and communication errors might appear during execution. This paper presents a framework to run agent-based MRTA within a physical simulator to test different algorithms and/or setups. Agents are modeled by a specific type of state machines able to represent deliberative behaviors as well as reactivity. While this adds formality and simplifies implementation, execution of state machines within a physical simulator requires decoupling transitions that imply the passing of time from those occurring instantly. The result framework includes a state machine execution engine that synchronizes with the simulator’s engine. Experiments using an auction-based MRTA for an example plant show not only the capability of the framework for modeling a wide range of systems but also that the MRTA method works with on-the-fly task inclusions, varying number of active robots and error occurrences.

Original languageEnglish
Pages (from-to)576-587
Number of pages12
JournalLecture Notes in Networks and Systems
Publication statusPublished - 19 Nov 2022


  • Computational modeling
  • Control systems
  • Mobile robots
  • State machines
  • State-based programming
  • Time modeling


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