Multi-robot LTL planning under uncertainty
Paper in proceeding, 2018

Robot applications are increasingly based on teams of robots that collaborate to perform a desired mission. Such applications ask for decentralized techniques that allow for tractable automated planning. Another aspect that current robot applications must consider is partial knowledge about the environment in which the robots are operating and the uncertainty associated with the outcome of the robots’ actions. Current planning techniques used for teams of robots that perform complex missions do not systematically address these challenges: (1) they are either based on centralized solutions and hence not scalable, (2) they consider rather simple missions, such as A-to-B travel, (3) they do not work in partially known environments. We present a planning solution that decomposes the team of robots into subclasses, considers missions given in temporal logic, and at the same time works when only partial knowledge of the environment is available. We prove the correctness of the solution and evaluate its effectiveness on a set of realistic examples.


Claudio Menghi

Chalmers, Computer Science and Engineering (Chalmers), Software Engineering (Chalmers)

Sergio Garcia

University of Gothenburg

Patrizio Pelliccione

University of Gothenburg

Jana Tumova

Royal Institute of Technology (KTH)

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

03029743 (ISSN) 16113349 (eISSN)

Vol. 10951 LNCS 399-417

22nd International Symposium on Formal Methods, FM 2018 Held as Part of the Federated Logic Conference, FloC 2018
Oxford, United Kingdom,

Areas of Advance

Information and Communication Technology

Subject Categories

Interaction Technologies


Computer Science



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