Risk-Aware Real-Time Task Allocation for Stochastic Multi-Agent Systems under STL Specifications
<|reference_start|>Risk-Aware Real-Time Task Allocation for Stochastic Multi-Agent Systems under STL Specifications: This paper addresses the control synthesis of heterogeneous stochastic linear multi-agent systems with real-time allocation of signal temporal logic (STL) specifications. Based on previous work, we decompose specifications into sub-specifications on the individual agent level. To leverage the efficiency of task allocation, a heuristic filter evaluates potential task allocation based on STL robustness, and subsequently, an auctioning algorithm determines the definitive allocation of specifications. Finally, a control strategy is synthesized for each agent-specification pair using tube-based model predictive control (MPC), ensuring provable probabilistic satisfaction. We demonstrate the efficacy of the proposed methods using a multi-shuttle scenario that highlights a promising extension to automated driving applications like vehicle routing.<|reference_end|>
@article{engelaar2024risk-aware,
    title={Risk-Aware Real-Time Task Allocation for Stochastic Multi-Agent Systems
  under STL Specifications},
    author={Maico H. W. Engelaar, Zengjie Zhang, Eleftherios E. Vlahakis, Dimos V.
  Dimarogonas, Mircea Lazar, Sofie Haesaert},
    journal={arXiv preprint arXiv:2404.02111},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.02111},
    primaryClass={eess.SY cs.SY}
}