Sampling-based control synthesis for multi-robot systems under global temporal specifications

This paper proposes a sampling-based algorithm for multi-robot control synthesis under global Linear Temporal Logic (LTL) formulas. Robot mobility is captured by transition systems whose states represent regions in the environment that satisfy atomic propositions. Existing planning approaches under...

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Veröffentlicht in:2017 ACM IEEE 8th International Conference on Cyber Physical Systems (ICCPS) S. 3 - 13
Hauptverfasser: Kantaros, Yiannis, Zavlanos, Michael M.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: New York, NY, USA ACM 18.04.2017
Schriftenreihe:ACM Other Conferences
Schlagworte:
Approximation algorithms
Automata
Computational modeling
Computing methodologies > Artificial intelligence > Control methods > Motion path planning
Computing methodologies > Artificial intelligence > Distributed artificial intelligence
Computing methodologies > Artificial intelligence > Planning and scheduling
Computing methodologies > Artificial intelligence > Planning and scheduling > Multi-agent planning
Computing methodologies > Artificial intelligence > Planning and scheduling > Robotic planning
Memory management
Numerical models
Planning
Theory of computation > Formal languages and automata theory
linear temporal logic
multi-agent systems
motion planning
control synthesis
ISBN:9781450349659, 145034965X
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Zusammenfassung:This paper proposes a sampling-based algorithm for multi-robot control synthesis under global Linear Temporal Logic (LTL) formulas. Robot mobility is captured by transition systems whose states represent regions in the environment that satisfy atomic propositions. Existing planning approaches under global temporal goals rely on graph search techniques applied to a synchronous product automaton constructed among the robots. As the number of robots increases, the state-space of the product automaton grows exponentially and, as a result, graph search techniques become intractable. In this paper, we propose a new sampling-based algorithm that builds incrementally a directed tree that approximates the state-space and transitions of the synchronous product automaton. By approximating the product automaton by a tree rather than representing it explicitly, we require much fewer resources to store it and motion plans can be found by tracing the sequence of parent nodes from the leaves back to the root without the need for sophisticated graph search techniques. This significantly increases scalability of our algorithm compared to existing model-checking methods. We also show that our algorithm is probabilistically complete and asymptotically optimal and present numerical experiments that show that it can be used to model-check product automata with billions of states, which was not possible using an off-the-shelf model checker.
ISBN:9781450349659
145034965X
DOI:10.1145/3055004.3055027