Bayesian Optimization Framework for Efficient Fleet Design in Autonomous Multi-Robot Exploration

TL;DR

This paper introduces BOFD, a Bayesian Optimization framework that efficiently designs heterogeneous multi-robot fleets for exploration by balancing performance and cost, reducing the need for exhaustive evaluations.

Contribution

The paper presents a novel Bayesian Optimization approach tailored for multi-robot fleet design, incorporating multi-objective optimization and regret bounds for robustness.

Findings

BOFD outperforms existing methods in synthetic environments.

It achieves efficient fleet designs with fewer evaluations.

The framework effectively balances performance and costs.

Abstract

This study addresses the challenge of fleet design optimization in the context of heterogeneous multi-robot fleets, aiming to obtain feasible designs that balance performance and costs. In the domain of autonomous multi-robot exploration, reinforcement learning agents play a central role, offering adaptability to complex terrains and facilitating collaboration among robots. However, modifying the fleet composition results in changes in the learned behavior, and training multi-robot systems using multi-agent reinforcement learning is expensive. Therefore, an exhaustive evaluation of each potential fleet design is infeasible. To tackle these hurdles, we introduce Bayesian Optimization for Fleet Design (BOFD), a framework leveraging multi-objective Bayesian Optimization to explore fleets on the Pareto front of performance and cost while accounting for uncertainty in the design space. Moreover, we establish a sub-linear bound for cumulative regret, supporting BOFD's robustness and efficacy. Extensive benchmark experiments in synthetic and simulated environments demonstrate the superiority of our framework over state-of-the-art methods, achieving efficient fleet designs with minimal fleet evaluations.