MAPS <sup>2</sup> : Multi-robot autonomous motion planning under signal temporal logic specifications

This article presents MAPS 2 : a distributed algorithm that allows multi-robot systems to deliver coupled tasks expressed as Signal Temporal Logic (STL) constraints. Classical control theoretical tools addressing STL constraints either adopt a limited fragment of the STL formula or require approximations of min/max operators. Meanwhile, works maximising robustness through optimisation-based methods often suffer from local minima, thus relaxing any completeness arguments due to the NP-hard nature of the problem. Endowed with probabilistic guarantees, MAPS 2 provides an autonomous algorithm that iteratively improves the robots’ trajectories. The algorithm selectively imposes spatial constraints by taking advantage of the temporal properties of the STL. The algorithm is distributed in the sense that each robot calculates its trajectory by communicating only with its immediate neighbours as defined via a communication graph. We illustrate the efficiency of MAPS 2 by conducting extensive simulation and experimental studies, verifying the generation of STL satisfying trajectories.

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Publication Details

Journal
The International Journal of Robotics Research
Published
2025-12-03
DOI
https://doi.org/10.1177/02783649251393343
Primary Topic
Formal Methods in Verification
Type
article
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article

MAPS 2 : Multi-robot autonomous motion planning under signal temporal logic specifications

Christos K. Verginis, Mayank Sewlia, Dimos V. Dimarogonas
The International Journal of Robotics Research
Formal Methods in Verification
article

MAPS 2 : Multi-robot autonomous motion planning under signal temporal logic specifications

Christos K. Verginis, Mayank Sewlia, Dimos V. Dimarogonas
article en

Abstract

This article presents MAPS 2 : a distributed algorithm that allows multi-robot systems to deliver coupled tasks expressed as Signal Temporal Logic (STL) constraints. Classical control theoretical tools addressing STL constraints either adopt a limited fragment of the STL formula or require approximations of min/max operators. Meanwhile, works maximising robustness through optimisation-based methods often suffer from local minima, thus relaxing any completeness arguments due to the NP-hard nature of the problem. Endowed with probabilistic guarantees, MAPS 2 provides an autonomous algorithm that iteratively improves the robots’ trajectories. The algorithm selectively imposes spatial constraints by taking advantage of the temporal properties of the STL. The algorithm is distributed in the sense that each robot calculates its trajectory by communicating only with its immediate neighbours as defined via a communication graph. We illustrate the efficiency of MAPS 2 by conducting extensive simulation and experimental studies, verifying the generation of STL satisfying trajectories.

The International Journal of Robotics Research
Uppsala University (SE), KTH Royal Institute of Technology (SE)
Openalex Percentile: Top 62%
Formal Methods in Verification
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MAPS <sup>2</sup> : Multi-robot autonomous motion planning under signal temporal logic specifications — Christos K. Verginis, Mayank Sewlia, et al. · The International Journal of Robotics Research (2025) | TGRS Research Map | TGRS