用数字孪生实时监控机器人，提升不确定环境下的安全与性能。
Digital Twin Enabled Runtime Verification for Autonomous Mobile Robots under Uncertainty
- 通过数字孪生+TeSSLa构建运行时监控器，实时验证机器人行为。
- 在不确定性环境下，实测速度偏差降低41%，显著提升控制精度。
- 适合关注机器人安全验证与高可靠部署的研究者与工程师。
随着自主机器人在复杂多变环境中应用日益广泛，其在不确定性条件下的可靠行为成为关键挑战。本文提出一种基于数字孪生的运行时验证方法，用于缓解部署环境中的不确定性影响。通过TeSSLa将安全与性能属性形式化并生成运行时监控器，结合通过MQTT协议集成的可执行数字孪生，实现对机器人行为的实时持续监控与验证。研究分析了传感器噪声和环境变化等不确定性来源对机器人安全与性能的影响。具备高计算资源的云端数字孪生作为监视模型，能够估计实际状态、校验动作一致性，并在安全或性能属性即将被违反时主动干预，覆盖不当动作。实验表明，该方法显著提升了自主机器人在不确定环境下的可靠性与鲁棒性，实际与预期速度差异较默认导航控制减少高达41%。
原文摘要 · Abstract (English)
As autonomous robots increasingly navigate complex and unpredictable environments, ensuring their reliable behavior under uncertainty becomes a critical challenge. This paper introduces a digital twin-based runtime verification for an autonomous mobile robot to mitigate the impact posed by uncertainty in the deployment environment. The safety and performance properties are specified and synthesized as runtime monitors using TeSSLa. The integration of the executable digital twin, via the MQTT protocol, enables continuous monitoring and validation of the robot's behavior in real-time. We explore the sources of uncertainties, including sensor noise and environment variations, and analyze their impact on the robot safety and performance. Equipped with high computation resources, the cloud-located digital twin serves as a watch-dog model to estimate the actual state, check the consistency of the robot's actuations and intervene to override such actuations if a safety or performance property is about to be violated. The experimental analysis demonstrated high efficiency of the proposed approach in ensuring the reliability and robustness of the autonomous robot behavior in uncertain environments and securing high alignment between the actual and expected speeds where the difference is reduced by up to 41\% compared to the default robot navigation control.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。