An Abstraction-Free Method for Multirobot Temporal Logic Optimal Control Synthesis

The majority of existing linear temporal logic (LTL) planning methods rely on the construction of a discrete product automaton, which combines a discrete abstraction of robot mobility and a Büchi automaton that captures the LTL specification. Representing this product automaton as a graph and using...

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Veröffentlicht in:	IEEE transactions on robotics Jg. 37; H. 5; S. 1487 - 1507
Hauptverfasser:	Luo, Xusheng, Kantaros, Yiannis, Zavlanos, Michael M.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.10.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Acceleration Algorithms Automata Bias Data storage Formal methods in robotics and automation Graphical representations Heuristic algorithms motion and path planning Multiple robots Navigation Optimal control optimization and optimal control path planning for multiple mobile robots or agents Planning Sampling Sampling methods Shortest-path problems Synthesis Task analysis Temporal logic Trees
ISSN:	1552-3098, 1941-0468
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Abstract	The majority of existing linear temporal logic (LTL) planning methods rely on the construction of a discrete product automaton, which combines a discrete abstraction of robot mobility and a Büchi automaton that captures the LTL specification. Representing this product automaton as a graph and using graph search techniques, optimal plans that satisfy the LTL task can be synthesized. However, constructing expressive discrete abstractions makes the synthesis problem computationally intractable. In this article, we propose a new sampling-based LTL planning algorithm that does not require any discrete abstraction of robot mobility. Instead, it incrementally builds trees that explore the product state-space, until a maximum number of iterations is reached or a feasible plan is found. The use of trees makes data storage and graph search tractable, which significantly increases the scalability of our algorithm. To accelerate the construction of feasible plans, we introduce bias in the sampling process, which is guided by transitions in the Büchi automaton that belong to the shortest path to the accepting states. We show that our planning algorithm, with and without bias, is probabilistically complete and asymptotically optimal. Finally, we present numerical experiments showing that our method outperforms relevant temporal logic planning methods.
AbstractList	The majority of existing linear temporal logic (LTL) planning methods rely on the construction of a discrete product automaton, which combines a discrete abstraction of robot mobility and a Büchi automaton that captures the LTL specification. Representing this product automaton as a graph and using graph search techniques, optimal plans that satisfy the LTL task can be synthesized. However, constructing expressive discrete abstractions makes the synthesis problem computationally intractable. In this article, we propose a new sampling-based LTL planning algorithm that does not require any discrete abstraction of robot mobility. Instead, it incrementally builds trees that explore the product state-space, until a maximum number of iterations is reached or a feasible plan is found. The use of trees makes data storage and graph search tractable, which significantly increases the scalability of our algorithm. To accelerate the construction of feasible plans, we introduce bias in the sampling process, which is guided by transitions in the Büchi automaton that belong to the shortest path to the accepting states. We show that our planning algorithm, with and without bias, is probabilistically complete and asymptotically optimal. Finally, we present numerical experiments showing that our method outperforms relevant temporal logic planning methods.
Author	Kantaros, Yiannis Luo, Xusheng Zavlanos, Michael M.
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SubjectTerms	Acceleration Algorithms Automata Bias Data storage Formal methods in robotics and automation Graphical representations Heuristic algorithms motion and path planning Multiple robots Navigation Optimal control optimization and optimal control path planning for multiple mobile robots or agents Planning Sampling Sampling methods Shortest-path problems Synthesis Task analysis Temporal logic Trees
Title	An Abstraction-Free Method for Multirobot Temporal Logic Optimal Control Synthesis
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