English

Autonomous Robotic Swarms: A Corroborative Approach for Verification and Validation

Robotics 2025-03-04 v2 Artificial Intelligence

Abstract

The emergent behaviour of autonomous robotic swarms poses a significant challenge to their safety 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 corroborative 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 characterised by data derived from actual simulations to ensure both accuracy and traceability across different swarm system models. Furthermore, our work combines formal verification with simulations and experimental validation using 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.

Keywords

Cite

@article{arxiv.2407.15475,
  title  = {Autonomous Robotic Swarms: A Corroborative Approach for Verification and Validation},
  author = {Dhaminda B. Abeywickrama and Suet Lee and Chris Bennett and Razanne Abu-Aisheh and Tom Didiot-Cook and Simon Jones and Sabine Hauert and Kerstin Eder},
  journal= {arXiv preprint arXiv:2407.15475},
  year   = {2025}
}

Comments

Updated Data Repository and Abstract. 15 pages, 11 figures

R2 v1 2026-06-28T17:49:16.334Z