2015/02/20 by Shayegan Omidshafiei, Ali–akbar Agha–mohammadi, Omidshafiei, Shayegan +5 · 1 citation
Computer Science · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Formal Methods in Verification #Logic, Reasoning, and Knowledge #Multiagent Systems (cs.MA) #Robotics (cs.RO)
paper · pdf · doi:10.48550/arxiv.1502.06030
openalex publication_date 2015/02/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31
The focus of this paper is on solving multi-robot planning problems in continuous spaces with partial observability. Decentralized partially observable Markov decision processes (Dec-POMDPs) are general models for multi-robot coordination problems, but representing and solving Dec-POMDPs is often intractable for large problems. To allow for a high-level representation that is natural for multi-robot problems and scalable to large discrete and continuous problems, this paper extends the Dec-POMDP model to the decentralized partially observable semi-Markov decision process (Dec-POSMDP). The Dec-POSMDP formulation allows asynchronous decision-making by the robots, which is crucial in multi-robot domains. We also present an algorithm for solving this Dec-POSMDP which is much more scalable than previous methods since it can incorporate closed-loop belief space macro-actions in planning. These macro-actions are automatically constructed to produce robust solutions. The proposed method's performance is evaluated on a complex multi-robot package delivery problem under uncertainty, showing that our approach can naturally represent multi-robot problems and provide high-quality solutions for large-scale problems.