Decomposition-based Hierarchical Task Allocation and Planning for Multi-Robots under Hierarchical Temporal Logic Specifications

TL;DR

This paper introduces a hierarchical, decomposition-based framework for multi-robot task allocation and planning under complex temporal logic specifications, improving interpretability and computational efficiency.

Contribution

It presents a novel hierarchical decomposition approach that handles multiple temporal logic specifications and complex temporal constraints in multi-robot planning.

Findings

Achieved better solutions with reduced runtimes in navigation and manipulation domains.

Effectively decomposed complex specifications into atomic sub-tasks and inferred their temporal relations.

Utilized Mixed Integer Linear Programming for efficient task assignment.

Abstract

Past research into robotic planning with temporal logic specifications, notably Linear Temporal Logic (LTL), was largely based on a single formula for individual or groups of robots. But with increasing task complexity, LTL formulas unavoidably grow lengthy, complicating interpretation and specification generation, and straining the computational capacities of the planners. A recent development has been the hierarchical representation of LTL~\cite{luo2024simultaneous} that contains multiple temporal logic specifications, providing a more interpretable framework. However, the proposed planning algorithm assumes the independence of robots within each specification, limiting their application to multi-robot coordination with complex temporal constraints. In this work, we formulated a decomposition-based hierarchical framework. At the high level, each specification is first decomposed into a set of atomic sub-tasks. We further infer the temporal relations among the sub-tasks of different specifications to construct a task network. Subsequently, a Mixed Integer Linear Program is used to assign sub-tasks to various robots. At the lower level, domain-specific controllers are employed to execute sub-tasks. Our approach was experimentally applied to domains of navigation and manipulation. The simulation demonstrated that our approach can find better solutions using less runtimes.