A Multi-Level Corroborative Approach for Verification and Validation of Autonomous Robotic Swarms
<|reference_start|>A Multi-Level Corroborative Approach for Verification and Validation of Autonomous Robotic Swarms: Modelling and characterizing emergent behaviour within a swarm can pose significant challenges in terms of 'assurance'. Assurance tasks encompass adherence to standards, certification processes, and the execution of verification and validation (V&V) methods, such as model checking. In this study, we propose a holistic, multi-level modelling approach for formally verifying and validating autonomous robotic swarms, which are defined at the macroscopic formal modelling, low-fidelity simulation, high-fidelity simulation, and real-robot levels. Our formal macroscopic models, used for verification, are characterized by data derived from actual simulations, ensuring both accuracy and traceability across different system models. Furthermore, our work combines formal verification with experimental validation involving real robots. In this way, our corroborative approach for V&V seeks to enhance confidence in the evidence, in contrast to employing these methods separately. We explore our approach through a case study focused on a swarm of robots operating within a public cloakroom.<|reference_end|>
@article{abeywickrama2024a,
    title={A Multi-Level Corroborative Approach for Verification and Validation of
  Autonomous Robotic Swarms},
    author={Dhaminda B. Abeywickrama, Suet Lee, Chris Bennett, Razanne Abu-Aisheh,
  Tom Didiot-Cook, Simon Jones, Sabine Hauert, Kerstin Eder},
    journal={arXiv preprint arXiv:2407.15475},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.15475},
    primaryClass={cs.RO cs.AI}
}