Autonomous mobile robots are increasingly deployed in various application domains, often operating in environments with uncertain conditions. Such robots rely on the state and performance assessments at runtime to autonomously control the robot functionality. However, uncertainty can significantly impact the robot sensors and actuators making it challenging to assess the robot state and quantify its performance reliably. This paper proposes a digital twin (DT) asset for the runtime estimation and validation of state and performance for a mobile autonomous robot "Turtlebot3" (TB3) operating under uncertainties, namely Lidar sensor obstruction and unknown floor friction and density. The proposed DT setup enables real-time state synthesis post-uncertainty, so that to estimate the performance and validate it using TeSSLa monitors, and compute mitigation actions. To maintain the robot autonomy, our DT intervenes only when an uncertainty is identified. The experimental results demonstrate that our DT enables to eliminate 70% of the related uncertainty while it mostly maintains the real-time synchronization with the physical TB3 robot operating a frequency of 0.2s.

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 as a service approach to enable runtime monitoring and verification of an autonomous mobile robot and 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 different sources of uncertainties 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, checking the consistency of the robot’s actuations and approving or denying such actuations depending on the safety and performance properties. The experimental analysis demonstrated high efficiency of the proposed approach in ensuring the reliability and robustness of the autonomous robot behavior in uncertain environments by 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.

The development of cyber-physical systems is heavily relying on model-driven approaches. After deployment, these models can be utilised in a Digital Twin setting, acting as virtual replicas of the physical components and reflecting the behaviour of the running system in real-time. Complex systems often consist of numerous models interacting with each other and individual models may need to be updated after deployment. This means that new models need to be integrated and swapped during runtime without interrupting the running system. In this paper, we propose an approach for model-based Digital Twins to replace individual models without stopping or halting the operation of a cyber-physical system. Furthermore, our approach allows to replace not only individual models, but also update the overall structure of the interaction of models in the Digital Twin setting. The use of the proposed mechanism is illustrated through two case-studies with an agricultural robot prototype.

This paper demonstrates a step on the transition towards a digital twin for a desktop version of an agricultural robot. This includes implementation of motor control and in-door localisation capabilities for the robot. Data from the physical twin is streamed to a co-simulation based on the Functional-Mockup Interface, in which a digital twin simulates the robot’s movement. Safety constraints are established inside the digital twin and a proof of concept communication is enabled when such constraints are violated.

When multiple robots are required to collaborate in order to accomplish a specific task, they need to be coordinated in order to operate efficiently. To allow for scalability and robustness, we pro ...

Adaptive autonomy (AA) is a behavior that allows agents to change their autonomy levels by reasoning on their circumstances. Previous work has modeled AA through the willingness to interact, compos ...

Adaptive autonomous (AA) agents are able to make their own decisions on when and with whom to share their autonomy based on their states.Whereas dependability gives evidence on whether a system, (e.g. an agent team), and its provided services are to be trusted.In this paper, an initial analysis on AA agents with respect to dependability is conducted.Firstly, AA is modeled through a pairwise relationship called willingness of agents to interact, i.e. to ask for and give assistance.Secondly, dependability is evaluated by considering solely the reliability attribute, which presents the continuity of correct services.The failure analysis is realized by modeling the agents through Petri Nets.Simulation results indicate that agents drop slightly more tasks when they are more willing to interact than otherwise, especially when the fail-rate of individual agents increases.Conclusively, the willingness should be tweaked such that there is compromise between performance and helpfulness.

Adaptive autonomy enables agents operating in an environment to change, or adapt, their autonomy levels by relying on tasks executed by others. Moreover, tasks could be delegated between agents, an ...