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Sampling-based path planning for multi-robot systems with co-safe linear temporal logic specifications

Montana, F.J., Liu, J. and DODD, Tony (2017) Sampling-based path planning for multi-robot systems with co-safe linear temporal logic specifications. In: ritical Systems: Formal Methods and Automated Verification. Joint 22nd International Workshop on Formal Methods for Industrial Critical Systems and 17th International Workshop on Automated Verification of Critical Systems, FMICS-AVoCS 2017,. Lecture Notes in Computer Science, 10471 . Springer Verlag, Turin, Italy. 18-20 September 2017, pp. 150-164. ISBN 9783319671123

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Abstract or description

© 2017, Springer International Publishing AG. This paper addresses the problem of path planning for multiple robots under high-level specifications given as syntactically co-safe linear temporal logic formulae. Most of the existing solutions use the notion of abstraction to obtain a discrete transition system that simulates the dynamics of the robot. Nevertheless, these solutions have poor scalability with the dimension of the configuration space of the robots. For problems with a single robot, sampling-based methods have been presented as a solution to alleviate this limitation. The proposed solution extends the idea of sampling methods to the multiple robot case. The method samples the configuration space of the robots to incrementally constructs a transition system that models the motion of all the robots as a group. This transition system is then combined with a Büchi automaton, representing the specification, in a Cartesian product. The product is updated with each expansion of the transition system until a solution is found. We also present a new algorithm that improves the performance of the proposed method by guiding the expansion of the transition system. The method is demonstrated with examples considering different number of robots and specifications.

Item Type: Book Chapter, Section or Conference Proceeding
Additional Information: © 2017 Springer Verlag.
Faculty: School of Creative Arts and Engineering > Engineering
Depositing User: Library STORE team
Date Deposited: 15 Jul 2020 14:50
Last Modified: 24 Feb 2023 13:58
URI: https://eprints.staffs.ac.uk/id/eprint/6235

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