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Resource-constrained mobile robots that lack the capability to be completelyautonomous can rely on a human or AI supervisor acting at a remote site (e.g.,control station or cloud) for their control. Such a supervised autonomy orcloud-based control of a robot poses high networking and computing capabilitiesrequirements at both sites, which are not easy to achieve. This paperintroduces and analyzes a new analog twin framework by synchronizing mobilitybetween two mobile robots, where one robot acts as an analog twin to the otherrobot. We devise a novel priority-based supervised bilateral teleoperationstrategy for goal navigation tasks to validate the proposed framework. Thepractical implementation of a supervised control strategy on this frameworkentails a mobile robot system divided into a Master-Client scheme over acommunication channel where the Client robot resides on the site of operationguided by the Master robot through an agent (human or AI) from a remotelocation. The Master robot controls the Client robot with its autonomousnavigation algorithm, which reacts to the predictive force received from theClient robot. We analyze the proposed strategy in terms of network performance(throughput and delay), task performance (tracking error and goal reachaccuracy), and computing efficiency (memory and CPU utilization). Extensivesimulations and real-world experiments demonstrate the method's novelty,flexibility, and versatility in realizing reactive planning applications withremote computational offloading capabilities compared to conventionaloffloading schemes.

Analog Twin Framework for Human and AI Supervisory Control and Teleoperation of Robots