ArticleSensors (Basel, Switzerland)2021
Learning-Based End-to-End Path Planning for Lunar Rovers with Safety Constraints.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- An Enhanced Artificial Lemming Algorithm and Its Application in UAV Path Planning.Biomimetics (Basel, Switzerland) · 2025Article
- Path Planning of Quadrupedal Robot Based on Improved RRT-Connect Algorithm.Sensors (Basel, Switzerland) · 2025Article
- Pseudo-Spectral Spatial Feature Extraction and Enhanced Fusion Image for Efficient Meter-Sized Lunar Impact Crater Automatic Detection in Digital Orthophoto Map.Sensors (Basel, Switzerland) · 2024Article
- A Deep Learning Approach to Lunar Rover Global Path Planning Using Environmental Constraints and the Rover Internal Resource Status.Sensors (Basel, Switzerland) · 2024Article
- Slip considered path planning of a novel angled-spoke based robot in a terrain mixture of granular media and rigid support.Scientific reports · 2023Article
- Path Planning for Autonomous Mobile Robots: A Review.Sensors (Basel, Switzerland) · 2021Review
- A Grain-Scale Study of Mojave Mars Simulant (MMS-1).Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Path planning is an essential technology for lunar rover to achieve safe and efficient autonomous exploration mission, this paper proposes a learning-based end-to-end path planning algorithm for lunar rovers with safety constraints. Firstly, a training environment integrating real lunar surface terrain data was built using the Gazebo simulation environment and a lunar rover simulator was created in it to simulate the real lunar surface environment and the lunar rover system. Then an end-to-end path planning algorithm based on deep reinforcement learning method is designed, including state space, action space, network structure, reward function considering slip behavior, and training method based on proximal policy optimization. In addition, to improve the generalization ability to different lunar surface topography and different scale environments, a variety of training scenarios were set up to train the network model using the idea of curriculum learning. The simulation results show that the proposed planning algorithm can successfully achieve the end-to-end path planning of the lunar rover, and the path generated by the proposed algorithm has a higher safety guarantee compared with the classical path planning algorithm.
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Registered trials
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