Path Planning for Lunar Rovers in Dynamic Environments: An Autonomous Navigation Framework Enhanced by Digital Twin-Based A*-D3QN

Path Planning for Lunar Rovers in Dynamic Environments: An Autonomous Navigation Framework Enhanced by Digital Twin-Based A*-D3QN

In lunar exploration missions, rovers must navigate multiple waypoints within strict time constraints while avoiding dynamic obstacles, demanding real-time, collision-free path planning. This paper proposes a digital twin-enhanced hierarchical planning method, A*-D3QN-Opt (A-Star-Dueling Double Deep...

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Bibliographic Details
Main Authors: Wei Liu, Gang Wan, Jia Liu, Dianwei Cong
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Aerospace
Subjects:
lunar rover path planning
digital twin
deep reinforcement learning
Online Access:https://www.mdpi.com/2226-4310/12/6/517
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Summary:In lunar exploration missions, rovers must navigate multiple waypoints within strict time constraints while avoiding dynamic obstacles, demanding real-time, collision-free path planning. This paper proposes a digital twin-enhanced hierarchical planning method, A*-D3QN-Opt (A-Star-Dueling Double Deep Q-Network-Optimized). The framework combines the A* algorithm for global optimal paths in static environments with an improved D3QN (Dueling Double Deep Q-Network) for dynamic obstacle avoidance. A multi-dimensional reward function balances path efficiency, safety, energy, and time, while priority experience replay accelerates training convergence. A high-fidelity digital twin simulation environment integrates a YOLOv5-based multimodal perception system for real-time obstacle detection and distance estimation. Experimental validation across low-, medium-, and high-complexity scenarios demonstrates superior performance: the method achieves shorter paths, zero collisions in dynamic settings, and 30% faster convergence than baseline D3QN. Results confirm its ability to harmonize optimality, safety, and real-time adaptability under dynamic constraints, offering critical support for autonomous navigation in lunar missions like Chang’e and future deep space exploration, thereby reducing operational risks and enhancing mission efficiency.
ISSN:2226-4310

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