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.

翻译：自主地面车辆（UGV）导航有潜力通过提高残障人士的可达性、确保使用安全性与便利性，从而彻底改变交通系统。然而，UGV需要广泛且高效的测试与评估，以确保其能被公众接受。这类测试多在模拟器中进行，由此导致模拟到现实（sim2real）的迁移误差。本文提出一种数字孪生感知意识方法，用于机器人导航控制，无需预先建立虚拟环境（VT）状态。为此，我们开发了一种双延迟深度确定性策略梯度（TD3）算法，该算法确保避障及基于目标点的路径规划。我们在不同环境动态下展示了该方法的表现。结果表明，该方法能够高效避开障碍物并导航至目标位置，同时利用机器人上搭载的激光雷达（LIDAR）传感器接收的信息安全规避障碍。我们的方法弥合了模拟到现实迁移的差距，并有助于UGV在实际场景中的应用。我们通过仿真模拟以及真实办公室环境的实际应用验证了该方法的有效性。