A Global Games-Inspired Approach to Multi-Robot Task Allocation for Heterogeneous Teams

TL;DR

This paper introduces a game-theoretic method inspired by global games for efficiently allocating multiple heterogeneous robots to tasks, balancing progress and urgency through a probabilistic approach.

Contribution

It presents a novel multi-robot task allocation algorithm based on global games, with a linear objective function and conditions for mixed Nash equilibria.

Findings

Algorithm effectively balances robot assignments based on task urgency.

Simulation results demonstrate robust performance in diverse scenarios.

Provides a matrix inversion method for probabilistic task allocation.

Abstract

In this article we propose a game-theoretic approach to the multi-robot task allocation problem using the framework of global games. Each task is associated with a global signal, a real-valued number that captures the task execution progress and/or urgency. We propose a linear objective function for each robot in the system, which, for each task, increases with global signal and decreases with the number assigned robots. We provide conditions on the objective function hyperparameters to induce a mixed Nash equilibrium, i.e., solutions where all robots are not assigned to a single task. The resulting algorithm only requires the inversion of a matrix to determine a probability distribution over the robot assignments. We demonstrate the performance of our algorithm in simulation and provide direction for applications and future work.