Large-scale Autonomous Navigation and Path Planning of Lunar Rover via Deep Reinforcement Learning

Hu, Tao; Cao, Tao; Zheng, Bo; Zhang, Hanmo; Ni, Mengying

doi:10.1109/cac53003.2021.9728075

Cited by 4 publications

(1 citation statement)

References 4 publications

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“…Park et al [12] apply the DRL on failure-safe motion planning for four-wheeled two-steering lunar rovers, although its efficacy in long-term planning remains questionable. Hu et al [13] integrated the DRL with a long-short time memory(LSTM) network for obstacle avoidance, yet this approach was not extended to scenarios involving multiple rovers. Wei et al [14] utilize multi-robots for environmental data collection, whose paths are generated by Independent Q-Learning.…”

Section: Introductionmentioning

confidence: 99%

Lunar Rover Collaborated Path Planning with Artificial Potential Field-Based Heuristic on Deep Reinforcement Learning

Lu,

Xu,

et al. 2024

Aerospace

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The International Lunar Research Station, to be established around 2030, will equip lunar rovers with robotic arms as constructors. Construction requires lunar soil and lunar rovers, for which rovers must go toward different waypoints without encountering obstacles in a limited time due to the short day, especially near the south pole. Traditional planning methods, such as uploading instructions from the ground, can hardly handle many rovers moving on the moon simultaneously with high efficiency. Therefore, we propose a new collaborative path-planning method based on deep reinforcement learning, where the heuristics are demonstrated by both the target and the obstacles in the artificial potential field. Environments have been randomly generated where small and large obstacles and different waypoints are created to collect resources, train the deep reinforcement learning agent to propose actions, and lead the rovers to move without obstacles, finish rovers’ tasks, and reach different targets. The artificial potential field created by obstacles and other rovers in every step affects the action choice of the rover. Information from the artificial potential field would be transformed into rewards in deep reinforcement learning that helps keep distance and safety. Experiments demonstrate that our method can guide rovers moving more safely without turning into nearby large obstacles or collision with other rovers as well as consuming less energy compared with the multi-agent A-Star path-planning algorithm with improved obstacle avoidance method.

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Section: Introductionmentioning

confidence: 99%

Lunar Rover Collaborated Path Planning with Artificial Potential Field-Based Heuristic on Deep Reinforcement Learning

Lu,

Xu,

et al. 2024

Aerospace

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A Comprehensive Review of Mobile Robot Navigation Using Deep Reinforcement Learning Algorithms in Crowded Environments

Le,

Saeedvand,

Hsu

2024

J Intell Robot Syst

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Navigation is a crucial challenge for mobile robots. Currently, deep reinforcement learning has attracted considerable attention and has witnessed substantial development owing to its robust performance and learning capabilities in real-world scenarios. Scientists leverage the advantages of deep neural networks, such as long short-term memory, recurrent neural networks, and convolutional neural networks, to integrate them into mobile robot navigation based on deep reinforcement learning. This integration aims to enhance the robot's motion control performance in both static and dynamic environments. This paper illustrates a comprehensive survey of deep reinforcement learning methods applied to mobile robot navigation systems in crowded environments, exploring various navigation frameworks based on deep reinforcement learning and their benefits over traditional simultaneous localization and mapping-based frameworks. Subsequently, we comprehensively compare and analyze the relationships and differences among three types of navigation: autonomous-based navigation, navigation based on simultaneous localization and mapping, and planning-based navigation. Moreover, the crowded environment includes static, dynamic, and a combination of obstacles in different typical application scenarios. Finally, we offer insights into the evolution of navigation based on deep reinforcement learning, addressing the problems and providing potential solutions associated with this emerging field.

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Research on System Design and Motion Control of Biped Wheeled Legged Robot with Multi-terrain Adaptation in Lunar

Chen,

Wang

2024

2024 5th International Conference on Artificial Intelligence and Electromechanical Automation (AIEA)

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Large-scale Autonomous Navigation and Path Planning of Lunar Rover via Deep Reinforcement Learning

Cited by 4 publications

References 4 publications

Lunar Rover Collaborated Path Planning with Artificial Potential Field-Based Heuristic on Deep Reinforcement Learning

Lunar Rover Collaborated Path Planning with Artificial Potential Field-Based Heuristic on Deep Reinforcement Learning

A Comprehensive Review of Mobile Robot Navigation Using Deep Reinforcement Learning Algorithms in Crowded Environments

Research on System Design and Motion Control of Biped Wheeled Legged Robot with Multi-terrain Adaptation in Lunar

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