Decision-making under uncertainty for multi-robot systems

Journal article

In this overview paper, we present the work of the Goal-Oriented Long-Lived Systems Lab on multi-robot systems. We address multi-robot systems from a decision-making under uncertainty perspective, proposing approaches that explicitly reason about the inherent uncertainty of action execution, and how such stochasticity affects multi-robot coordination. To develop effective decision-making approaches, we take a special focus on (i) temporal uncertainty, in particular of action execution; (ii) the ability to provide rich guarantees of performance, both at a local (robot) level and at a global (team) level; and (iii) scaling up to systems with real-world impact. We summarise several pieces of work and highlight how they address the challenges above, and also hint at future research directions.

Authors: Lacerda, B; Gautier, A; Rutherford, A; Stephens, A; Street, C

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IOS Press
• Journal: AI Communications
• Volume: 35
• Issue: 4
• Pages: 433-441
• Publication date: 2022-09-20
• Acceptance date: 2022-09-01
• DOI: 10.3233/AIC-220118
• EISSN: 1875-8452
• ISSN: 0921-7126
• Language: English
• Keywords: Markov models; multi-robot systems; decision-making under uncertainty; formal methods; asynchronous execution; FFR