  Autonomous ground vehicle (UGV) navigation has the potential to revolutionize
the transportation system by increasing accessibility to disabled people,
ensure safety and convenience of use. However, UGV requires extensive and
efficient testing and evaluation to ensure its acceptance for public use. This
testing are mostly done in a simulator which result to sim2real transfer gap.
In this paper, we propose a digital twin perception awareness approach for the
control of robot navigation without prior creation of the virtual environment
(VT) environment state. To achieve this, we develop a twin delayed deep
deterministic policy gradient (TD3) algorithm that ensures collision avoidance
and goal-based path planning. We demonstrate the performance of our approach on
different environment dynamics. We show that our approach is capable of
efficiently avoiding collision with obstacles and navigating to its desired
destination, while at the same time safely avoids obstacles using the
information received from the LIDAR sensor mounted on the robot. Our approach
bridges the gap between sim-to-real transfer and contributes to the adoption of
UGVs in real world. We validate our approach in simulation and a real-world
application in an office space.
