Autonomous Robotic Swarms: A Corroborative Approach for Verification and Validation
This addresses safety assurance for autonomous robotic swarms, though it appears incremental by combining existing methods.
The study tackled the challenge of ensuring safety in autonomous robotic swarms by proposing a corroborative approach that integrates formal verification, simulations, and real-robot experiments, resulting in enhanced confidence in verification and validation evidence.
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.