A Twin Delayed Deep Deterministic Policy Gradient Algorithm for Autonomous Ground Vehicle Navigation via Digital Twin Perception Awareness
On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control
Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 14 of its 39 references that are in the map. First posted on arXiv 22 Mar 2024.
Builds on
14 of 39 references.
References · 14
- 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
- 2017CARLA: An Open Urban Driving Simulatorcited by 2,449
- 2017AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehiclescited by 523
- 2020Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Surveycited by 413
- 2020LGSVL Simulator: A High Fidelity Simulator for Autonomous Drivingcited by 121
- 2023How Simulation Helps Autonomous Driving:A Survey of Sim2real, Digital Twins, and Parallel Intelligencecited by 58
- 2021KITTI-CARLA: a KITTI-like dataset generated by CARLA Simulatorcited by 34
- 2019Generalization through Simulation: Integrating Simulated and Real Data into Deep Reinforcement Learning for Vision-Based Autonomous Flightcited by 28
- 2021A Survey on Simulators for Testing Self-Driving Carscited by 27
- 2022Reinforcement Learning from Simulation to Real World Autonomous Driving using Digital Twincited by 10
- 2020Comparative Analysis of Control Barrier Functions and Artificial Potential Fields for Obstacle Avoidancecited by 9
- 2023Sequential Neural Barriers for Scalable Dynamic Obstacle Avoidancecited by 9
Built on by
Citing papers per year: 2024: 1; 2025: 1.
2 papers.
Similar, not cited
Close in wording, no citation either way
- 2024TD3 Based Collision Free Motion Planning for Robot Navigation
- 2023Deep Reinforcement Learning for Autonomous Vehicle Intersection Navigation
- 2024Towards Validation of Autonomous Vehicles Across Scales using an Integrated Digital Twin Framework
- 2023Combining Policy Gradient and Safety-Based Control for Autonomous Driving
- 2026A Risk-Field Enhanced Closed-Loop Digital Twin Framework for Autonomous Driving Safety Validation
- 2026Latency-Aware Digital Twin-Assisted Cooperative Perception for Autonomous Vehicles
- 2025Digital Twin-based Cooperative Autonomous Driving in Smart Intersections: A Multi-Agent Reinforcement Learning Approach
- 2026Path Planning Using Deep Deterministic Policy Gradient: A Reinforcement Learning Approach