An Interleaved Approach to Trait-Based Task Allocation and Scheduling

TL;DR

This paper introduces ITAGS, a novel interleaved framework for trait-based task allocation, scheduling, and motion planning in heterogeneous multi-robot systems, improving efficiency over sequential methods.

Contribution

The paper presents ITAGS, a search-based interleaved approach that simultaneously addresses task allocation, scheduling, and motion planning, unlike prior sequential methods.

Findings

ITAGS outperforms state-of-the-art algorithms in simulated emergency response tasks.

Interleaving tasks improves efficiency and solution quality.

A convex combination of heuristics enhances search effectiveness.

Abstract

To realize effective heterogeneous multi-robot teams, researchers must leverage individual robots' relative strengths and coordinate their individual behaviors. Specifically, heterogeneous multi-robot systems must answer three important questions: \textit{who} (task allocation), \textit{when} (scheduling), and \textit{how} (motion planning). While specific variants of each of these problems are known to be NP-Hard, their interdependence only exacerbates the challenges involved in solving them together. In this paper, we present a novel framework that interleaves task allocation, scheduling, and motion planning. We introduce a search-based approach for trait-based time-extended task allocation named Incremental Task Allocation Graph Search (ITAGS). In contrast to approaches that solve the three problems in sequence, ITAGS's interleaved approach enables efficient search for allocations while simultaneously satisfying scheduling constraints and accounting for the time taken to execute motion plans. To enable effective interleaving, we develop a convex combination of two search heuristics that optimizes the satisfaction of task requirements as well as the makespan of the associated schedule. We demonstrate the efficacy of ITAGS using detailed ablation studies and comparisons against two state-of-the-art algorithms in a simulated emergency response domain.