Hierarchical Multi-robot Strategies Synthesis and Optimization under Individual and Collaborative Temporal Logic Specifications

TL;DR

This paper introduces a hierarchical framework for multi-robot task planning that ensures the satisfaction of both individual and collaborative temporal logic specifications, optimizing execution strategies through decomposition and distributed adjustments.

Contribution

It proposes a novel hierarchical approach combining automaton decomposition, subtask allocation, and distributed strategy adjustment for multi-robot temporal task planning.

Findings

Framework is complete under certain assumptions.

Method improves time efficiency and reduces synchronization wait times.

Simulation results demonstrate scalability and optimization effectiveness.

Abstract

This paper presents a hierarchical framework to solve the multi-robot temporal task planning problem. We assume that each robot has its individual task specification and the robots have to jointly satisfy a global collaborative task specification, both described in linear temporal logic. Specifically, a central server firstly extracts and decomposes a collaborative task sequence from the automaton corresponding to the collaborative task specification, and allocates the subtasks in the sequence to robots. The robots can then synthesize their initial execution strategies based on locally constructed product automatons, combining the assigned collaborative tasks and their individual task specifications. Furthermore, we propose a distributed execution strategy adjusting mechanism to iteratively improve the time efficiency, by reducing wait time in collaborations caused by potential synchronization constraints. We prove the completeness of the proposed framework under assumptions, and analyze its time complexity and optimality. Extensive simulation results verify the scalability and optimization efficiency of the proposed method.