Identifying Uncertainty in Self-Adaptive Robotics with Large Language Models

Future self-adaptive robots are expected to operate in highly dynamic environments while effectively managing uncertainties. However, identifying the sources and impacts of uncertainties in such robotic systems and defining appropriate mitigation strategies is challenging due to the inherent complexity of self-adaptive robots and the lack of comprehensive knowledge about the various factors influencing uncertainty. Hence, practitioners often rely on intuition and past experiences from similar systems to address uncertainties. In this article, we evaluate the potential of large language models (LLMs) in enabling a systematic and automated approach to identify uncertainties in self-adaptive robotics throughout the software engineering lifecycle. For this evaluation, we analyzed 10 advanced LLMs with varying capabilities across four industrial-sized robotics case studies, gathering the practitioners' perspectives on the LLM-generated responses related to uncertainties. Results showed that practitioners agreed with 63-88% of the LLM responses and expressed strong interest in the practicality of LLMs for this purpose.

Digital Twin Enabled Runtime Verification for Autonomous Mobile Robots under Uncertainty

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.