A Twin Delayed Deep Deterministic Policy Gradient Algorithm for Autonomous Ground Vehicle Navigation via Digital Twin Perception Awareness
<|reference_start|>A Twin Delayed Deep Deterministic Policy Gradient Algorithm for Autonomous Ground Vehicle Navigation via Digital Twin Perception Awareness: 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.<|reference_end|>
@article{olayemi2024a,
    title={A Twin Delayed Deep Deterministic Policy Gradient Algorithm for
  Autonomous Ground Vehicle Navigation via Digital Twin Perception Awareness},
    author={Kabirat Olayemi, Mien Van, Sean McLoone, Yuzhu Sun, Jack Close, Nguyen
  Minh Nhat, and Stephen McIlvanna},
    journal={arXiv preprint arXiv:2403.15067},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.15067},
    primaryClass={cs.RO cs.SY eess.SY}
}