A Distributed Framework for Dynamic Task Allocation of Multi-Robot Symbolic Motion Planning

Abstract

This paper presents an automatic task allocation framework for multi-robot systems (MRS) based on automaton parallel decomposition techniques. Given a synthesized global task automaton for a MRS, an iterative parallel decomposition framework is developed by decomposing this automaton projecting this automaton into a set of parallel decomposable event sets; thus into a set of smallest parallel subtask automata. Furthermore, an enhanced parallel decomposition strategy is presented by extracting the strictly decomposable automaton from a more general task automaton. Next, a task allocation automaton is synthesized for each subtask automaton to determine the robot assignment to tasks in a consecutive way. Through parallel executions of all these subtask allocation automata, a parallel task allocation automaton is obtained, which guarantees the completeness of the solution while reducing the search space. An optimal task allocation solution can be found from this parallel task allocation automaton by taking into account both concurrency and costs of multi-robot tasking. After the task allocation, symbolic motion planning (SMP) is performed for each individual robot. When intermittent communications exist among neighboring robots, task redecomposition and reallocation are triggered to update the optimal task allocation and SMP. This process continues until all the events in each subtask automaton are completed. The overall strategy is demonstrated by a simulation.