Adaptive heterogeneous multi-robot collaboration from formal task specifications
Artikel i vetenskaplig tidskrift, 2021
Efficiently coordinating different types of robots is an important enabler for many commercial and industrial automation tasks. Here, we present a distributed framework that enables a team of heterogeneous robots to dynamically generate actions from a common, user-defined goal specification. In particular, we discuss the integration of various robotic capabilities into a common task allocation and planning formalism, as well as the specification of expressive, temporally-extended goals by non -expert users. Models for task allocation and execution both consider non-deterministic outcomes of actions and thus, are suitable for a wide range of real-world tasks including formally specified reactions to online observations. One main focus of our paper is to evaluate the framework and its integration of software modules through a number of experiments. These experiments comprise industry-inspired scenarios as motivated by future real-world applications. Finally, we discuss the results and learnings for motivating practically relevant, future research questions. (C) 2021 Elsevier B.V. All rights reserved.
Författare
Philipp Schillinger
Bosch Center for Artificial Intelligence
Sergio Garcia
Göteborgs universitet
Alexandros Makris
Idryma Technologias kai Erevnas (FORTH)
Konstantinos Roditakis
Idryma Technologias kai Erevnas (FORTH)
Michalis Logothetis
National Technical University of Athens (NTUA)
Konstantinos Alevizos
National Technical University of Athens (NTUA)
Wei Ren
Kungliga Tekniska Högskolan (KTH)
Pouria Tajvar
Kungliga Tekniska Högskolan (KTH)
Patrizio Pelliccione
Göteborgs universitet
Software Engineering 1
Gran Sasso Science Institute (GSSI)
Antonis Argyros
Idryma Technologias kai Erevnas (FORTH)
Kostas Kyriakopoulos
National Technical University of Athens (NTUA)
Dimos V. Dimarogonas
Kungliga Tekniska Högskolan (KTH)
Robotics and Autonomous Systems
0921-8890 (ISSN)
Vol. 145 103866Ämneskategorier (SSIF 2025)
Robotik och automation
Datavetenskap (datalogi)
DOI
10.1016/j.robot.2021.103866