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

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 a...

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Bibliographic Details
Published in:arXiv.org 2024-08
Main Authors: David Molina Concha, Li, Jiping, Yin, Haoran, Park, Kyeonghyeon, Hyun-Rok, Lee, Lee, Taesik, Sirohi, Dhruv, Lee, Chi-Guhn
Format: Article
Language:English
Subjects:
Bayesian analysis
Decision theory
Design analysis
Design optimization
Multiagent systems
Multiple objective analysis
Multiple robots
Robots
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Summary: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.
ISSN:2331-8422