Communication-Constrained STL Task Decomposition through Convex Optimization
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910345653649408 |
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| author | Marchesini, Gregorio Liu, Siyuan Lindemann, Lars Dimarogonas, Dimos V. |
| author_facet | Marchesini, Gregorio Liu, Siyuan Lindemann, Lars Dimarogonas, Dimos V. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_17585 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Communication-Constrained STL Task Decomposition through Convex Optimization Marchesini, Gregorio Liu, Siyuan Lindemann, Lars Dimarogonas, Dimos V. Systems and Control 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. |
| title | Communication-Constrained STL Task Decomposition through Convex Optimization |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2402.17585 |