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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Vydáno v:IEEE transactions on robotics Ročník 37; číslo 5; s. 1487 - 1507
Hlavní autoři: Luo, Xusheng, Kantaros, Yiannis, Zavlanos, Michael M.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.10.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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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Shrnutí: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.
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ISSN:1552-3098
1941-0468
DOI:10.1109/TRO.2021.3061983