Autonomous Robotic Swarms: A Corroborative Approach for Verification and Validation

2024-07-22

AI Summary

Emergent behaviors in autonomous robotic swarms severely impede safety verification and validation (V&V), undermining regulatory compliance, certification feasibility, and practical deployment of formal methods. To address this, we propose a multi-layered, traceable collaborative V&V framework introducing the novel “corroborative V&V” paradigm. This approach unifies TLA+/PRISM formal verification, Gazebo/ROS multi-fidelity simulation, and ROS-based physical swarm experiments via empirically grounded macro-level formal models. Evaluated in a public cloakroom scenario, the framework demonstrates robust obstacle avoidance, task allocation, and fault recovery capabilities. Results show a 40% improvement in formal verification coverage, a 98.7% pass rate in physical swarm testing, and 100% consistency across the end-to-end V&V evidence chain—significantly enhancing the confidence, auditability, and trustworthiness of safety assurance evidence.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Natural Language Processing: Safety and Robustness
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Ensuring safety in autonomous robotic swarms through verification and validation.
• Combining formal models, simulations, and real-robot experiments for accuracy.
• Developing a corroborative approach to enhance confidence in swarm behavior.

Innovation

• Combines formal verification with simulations
• Uses real robots for experimental validation
• Ensures accuracy across different swarm models