Multi-Robot Task Assignment and Path Finding for Time-Sensitive Missions with Online Task Generation

TL;DR

This paper introduces TSOTAN, a framework for real-time multi-robot task assignment and path planning that efficiently incorporates online task generation while ensuring bounded suboptimality in mission completion time.

Contribution

The paper presents TSOTAN, a novel framework that balances computational efficiency and solution quality for online multi-robot task assignment and path finding.

Findings

TSOTAN achieves near-optimal makespans with fast computation times.

The framework effectively incorporates online generated tasks in real-time scenarios.

Experimental results demonstrate suitability for online multi-robot missions.

Abstract

Executing time-sensitive multi-robot missions involves two distinct problems: Multi-Robot Task Assignment (MRTA) and Multi-Agent Path Finding (MAPF). Computing safe paths that complete every task and minimize the time to mission completion, or makespan, is a significant computational challenge even for small teams. In many missions, tasks can be generated during execution which is typically handled by either recomputing task assignments and paths from scratch, or by modifying existing plans using approximate approaches. While performing task reassignment and path finding from scratch produces theoretically optimal results, the computational load makes it too expensive for online implementation. In this work, we present Time-Sensitive Online Task Assignment and Navigation (TSOTAN), a framework which can quickly incorporate online generated tasks while guaranteeing bounded suboptimal task assignment makespans. It does this by assessing the quality of partial task reassignments and only performing a complete reoptimization when the makespan exceeds a user specified suboptimality bound. Through experiments in 2D environments we demonstrate TSOTAN's ability to produce quality solutions with computation times suitable for online implementation.