  When a mobile robot lacks high onboard computing or networking capabilities,
it can rely on remote computing architecture for its control and autonomy. This
paper introduces a novel collaborative Simulation Twin (ST) strategy for
control and autonomy on resource-constrained robots. The practical
implementation of such a strategy entails a mobile robot system divided into a
cyber (simulated) and physical (real) space separated over a communication
channel where the physical robot resides on the site of operation guided by a
simulated autonomous agent from a remote location maintained over a network.
Building on top of the digital twin concept, our collaborative twin is capable
of autonomous navigation through an advanced SLAM-based path planning
algorithm, while the physical robot is capable of tracking the Simulated twin's
velocity and communicating feedback generated through interaction with its
environment. We proposed a prioritized path planning application to the test in
a collaborative teleoperation system of a physical robot guided by ST's
autonomous navigation. We examine the performance of a physical robot led by
autonomous navigation from the Collaborative Twin and assisted by a predicted
force received from the physical robot. The experimental findings indicate the
practicality of the proposed simulation-physical twinning approach and provide
computational and network performance improvements compared to typical remote
computing (or offloading), and digital twin approaches.
