Designing Heterogeneous Robot Fleets for Task Allocation and Sequencing

TL;DR

This paper addresses the complex problem of assembling a heterogeneous robot fleet to efficiently service spatial tasks within time constraints, proposing a MILP formulation and a scalable heuristic solution that outperforms greedy algorithms in simulations.

Contribution

It introduces a novel approach to select and sequence heterogeneous robots for task allocation under budget constraints, including a MILP model and a Large Neighbourhood Search heuristic.

Findings

The heuristic requires significantly lower budgets than greedy algorithms.

The problem's complexity is characterized and addressed with a new MILP formulation.

Simulations demonstrate the effectiveness of the proposed method.

Abstract

We study the problem of selecting a fleet of robots to service spatially distributed tasks with diverse requirements within time-windows. The problem of allocating tasks to a fleet of potentially heterogeneous robots and finding an optimal sequence for each robot is known as multi-robot task assignment (MRTA). Most state-of-the-art methods focus on the problem when the fleet of robots is fixed. In contrast, we consider that we are given a set of available robot types and requested tasks, and need to assemble a fleet that optimally services the tasks while the cost of the fleet remains under a budget limit. We characterize the complexity of the problem and provide a Mixed-Integer Linear Program (MILP) formulation. Due to poor scalability of the MILP, we propose a heuristic solution based on a Large Neighbourhood Search (LNS). In simulations, we demonstrate that the proposed method requires substantially lower budgets than a greedy algorithm to service all tasks.