MAPmAKER: Performing multi-robot LTL planning under uncertainty
Paper i proceeding, 2019

Robot applications are being increasingly used in real life to help humans performing dangerous, heavy, and/or monotonous tasks. They usually rely on planners that given a robot or a team of robots compute plans that specify how the robot(s) can fulfill their missions. Current robot applications ask for planners that make automated planning possible even when only partial knowledge about the environment in which the robots are deployed is available. To tackle such challenges we developed MAPmAKER, which provides a decentralized planning solution and is able to work in partially known environments. Decentralization is realized by decomposing the robotic team into subteams based on their missions, and then by running a classical planning algorithm. Partial knowledge is handled by calling several times a classical planning algorithm. Demo video available at:


Multi robots



Sergio García Gonzalo

Göteborgs universitet

Claudio Menghi

Université du Luxembourg

Patrizio Pelliccione

Göteborgs universitet

Universita degli Studi dell'Aquila

Proceedings - 2019 IEEE/ACM 2nd International Workshop on Robotics Software Engineering, RoSE 2019

1-4 8823723
978-1-7281-2249-6 (ISBN)

2nd IEEE/ACM International Workshop on Robotics Software Engineering, RoSE 2019
Montreal, Canada,



Robotteknik och automation

Datavetenskap (datalogi)



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