Dynamic Obstacle Avoidance and Path Planning through Reinforcement Learning
The use of reinforcement learning (RL) for dynamic obstacle avoidance (DOA) algorithms and path planning (PP) has become increasingly popular in recent years. Despite the importance of RL in this growing technological era, few studies have systematically reviewed this research concept. Therefore, th...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/14/8174 |
| _version_ | 1797590430633689088 |
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| author | Khawla Almazrouei Ibrahim Kamel Tamer Rabie |
| author_facet | Khawla Almazrouei Ibrahim Kamel Tamer Rabie |
| author_sort | Khawla Almazrouei |
| collection | DOAJ |
| description | The use of reinforcement learning (RL) for dynamic obstacle avoidance (DOA) algorithms and path planning (PP) has become increasingly popular in recent years. Despite the importance of RL in this growing technological era, few studies have systematically reviewed this research concept. Therefore, this study provides a comprehensive review of the literature on dynamic reinforcement learning-based path planning and obstacle avoidance. Furthermore, this research reviews publications from the last 5 years (2018–2022) to include 34 studies to evaluate the latest trends in autonomous mobile robot development with RL. In the end, this review shed light on dynamic obstacle avoidance in reinforcement learning. Likewise, the propagation model and performance evaluation metrics and approaches that have been employed in previous research were synthesized by this study. Ultimately, this article’s major objective is to aid scholars in their understanding of the present and future applications of deep reinforcement learning for dynamic obstacle avoidance. |
| first_indexed | 2024-03-11T01:20:21Z |
| format | Article |
| id | doaj.art-2356be24ebcb452694d574e8e730d399 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T01:20:21Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-2356be24ebcb452694d574e8e730d3992023-11-18T18:09:20ZengMDPI AGApplied Sciences2076-34172023-07-011314817410.3390/app13148174Dynamic Obstacle Avoidance and Path Planning through Reinforcement LearningKhawla Almazrouei0Ibrahim Kamel1Tamer Rabie2College of Computing and Informatics, University of Sharjah, Sharjah P.O. Box 27272, United Arab EmiratesCollege of Computing and Informatics, University of Sharjah, Sharjah P.O. Box 27272, United Arab EmiratesCollege of Computing and Informatics, University of Sharjah, Sharjah P.O. Box 27272, United Arab EmiratesThe use of reinforcement learning (RL) for dynamic obstacle avoidance (DOA) algorithms and path planning (PP) has become increasingly popular in recent years. Despite the importance of RL in this growing technological era, few studies have systematically reviewed this research concept. Therefore, this study provides a comprehensive review of the literature on dynamic reinforcement learning-based path planning and obstacle avoidance. Furthermore, this research reviews publications from the last 5 years (2018–2022) to include 34 studies to evaluate the latest trends in autonomous mobile robot development with RL. In the end, this review shed light on dynamic obstacle avoidance in reinforcement learning. Likewise, the propagation model and performance evaluation metrics and approaches that have been employed in previous research were synthesized by this study. Ultimately, this article’s major objective is to aid scholars in their understanding of the present and future applications of deep reinforcement learning for dynamic obstacle avoidance.https://www.mdpi.com/2076-3417/13/14/8174machine learningdeep learningmobile robotnavigationcollision avoidance |
| spellingShingle | Khawla Almazrouei Ibrahim Kamel Tamer Rabie Dynamic Obstacle Avoidance and Path Planning through Reinforcement Learning Applied Sciences machine learning deep learning mobile robot navigation collision avoidance |
| title | Dynamic Obstacle Avoidance and Path Planning through Reinforcement Learning |
| title_full | Dynamic Obstacle Avoidance and Path Planning through Reinforcement Learning |
| title_fullStr | Dynamic Obstacle Avoidance and Path Planning through Reinforcement Learning |
| title_full_unstemmed | Dynamic Obstacle Avoidance and Path Planning through Reinforcement Learning |
| title_short | Dynamic Obstacle Avoidance and Path Planning through Reinforcement Learning |
| title_sort | dynamic obstacle avoidance and path planning through reinforcement learning |
| topic | machine learning deep learning mobile robot navigation collision avoidance |
| url | https://www.mdpi.com/2076-3417/13/14/8174 |
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