Multi-robot LTL planning under uncertainty
Paper i 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.

Författare

Claudio Menghi

Chalmers, Data- och informationsteknik, Software Engineering, Software Engineering for Cyber Psysical Systems

Sergio Garcia

Chalmers, Data- och informationsteknik, Software Engineering, Software Engineering for Cyber Psysical Systems

Patrizio Pelliccione

Chalmers, Data- och informationsteknik, Software Engineering, Software Engineering for Cyber Psysical Systems

Jana Tumova

Kungliga Tekniska Högskolan (KTH)

Lecture Notes in Computer Science

0302-9743 (ISSN)

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,

Styrkeområden

Informations- och kommunikationsteknik

Ämneskategorier

Interaktionsteknik

Robotteknik och automation

Datavetenskap (datalogi)

DOI

10.1007/978-3-319-95582-7_24

Mer information

Senast uppdaterat

2018-08-15