Communication-Constrained STL Task Decomposition through Convex Optimization
<|reference_start|>Communication-Constrained STL Task Decomposition through Convex Optimization: In this work, we propose a method to decompose signal temporal logic (STL) tasks for multi-agent systems subject to constraints imposed by the communication graph. Specifically, we propose to decompose tasks defined over multiple agents which require multi-hop communication, by a set of sub-tasks defined over the states of agents with 1-hop distance over the communication graph. To this end, we parameterize the predicates of the tasks to be decomposed as suitable hyper-rectangles. Then, we show that by solving a constrained convex optimization, optimal parameters maximising the volume of the predicate's super-level sets can be computed for the decomposed tasks. In addition, we provide a formal definition of conflicting conjunctions of tasks for the considered STL fragment and a formal procedure to exclude such conjunctions from the solution set of possible decompositions. The proposed approach is demonstrated through simulations.<|reference_end|>
@article{marchesini2024communication-constrained,
    title={Communication-Constrained STL Task Decomposition through Convex
  Optimization},
    author={Gregorio Marchesini, Siyuan Liu, Lars Lindemann, Dimos V. Dimarogonas},
    journal={arXiv preprint arXiv:2402.17585},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.17585},
    primaryClass={eess.SY cs.SY}
}