自主机器人集群：一种用于验证与确认的印证方法
Autonomous Robotic Swarms: A Corroborative Approach for Verification and Validation
AI总结：
针对自主机器人集群涌现行为的安全保证难题，提出一种结合宏观形式化建模、低保真与高保真仿真及真实机器人实验的印证式验证与确认方法，以增强证据可信度。
摘要：
自主机器人集群的涌现行为对其安全保证构成了重大挑战。保证任务包括遵守标准、认证流程以及执行验证与确认（V&V）方法，例如模型检测。在本研究中，我们提出了一种印证方法，用于形式化地验证和确认自主机器人集群，这些集群在宏观形式化建模、低保真仿真、高保真仿真和真实机器人层面进行定义。我们用于验证的宏观形式化模型，以从实际仿真中得出的数据为特征，以确保跨不同集群系统模型的准确性和可追溯性。此外，我们的工作将形式化验证与仿真以及使用真实机器人的实验验证相结合。通过这种方式，我们的V&V印证方法旨在增强对证据的信心，这与单独采用这些方法形成对比。我们通过一个专注于在公共衣帽间内运行的机器人集群的案例研究来探讨我们的方法。
英文摘要：
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.
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