  Resource-constrained mobile robots that lack the capability to be completely
autonomous 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 or
cloud-based control of a robot poses high networking and computing capabilities
requirements at both sites, which are not easy to achieve. This paper
introduces and analyzes a new analog twin framework by synchronizing mobility
between two mobile robots, where one robot acts as an analog twin to the other
robot. We devise a novel priority-based supervised bilateral teleoperation
strategy for goal navigation tasks to validate the proposed framework. The
practical implementation of a supervised control strategy on this framework
entails a mobile robot system divided into a Master-Client scheme over a
communication channel where the Client robot resides on the site of operation
guided by the Master robot through an agent (human or AI) from a remote
location. The Master robot controls the Client robot with its autonomous
navigation algorithm, which reacts to the predictive force received from the
Client robot. We analyze the proposed strategy in terms of network performance
(throughput and delay), task performance (tracking error and goal reach
accuracy), and computing efficiency (memory and CPU utilization). Extensive
simulations and real-world experiments demonstrate the method's novelty,
flexibility, and versatility in realizing reactive planning applications with
remote computational offloading capabilities compared to conventional
offloading schemes.
