RL-QPSO net: deep reinforcement learning-enhanced QPSO for efficient mobile robot path planning
IntroductionPath planning in complex and dynamic environments poses a significant challenge in the field of mobile robotics. Traditional path planning methods such as genetic algorithms, Dijkstra's algorithm, and Floyd's algorithm typically rely on deterministic search strategies, which ca...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Neurorobotics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnbot.2024.1464572/full |
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| author | Yang Jing Li Weiya |
| author_facet | Yang Jing Li Weiya |
| author_sort | Yang Jing |
| collection | DOAJ |
| description | IntroductionPath planning in complex and dynamic environments poses a significant challenge in the field of mobile robotics. Traditional path planning methods such as genetic algorithms, Dijkstra's algorithm, and Floyd's algorithm typically rely on deterministic search strategies, which can lead to local optima and lack global search capabilities in dynamic settings. These methods have high computational costs and are not efficient for real-time applications.MethodsTo address these issues, this paper presents a Quantum-behaved Particle Swarm Optimization model enhanced by deep reinforcement learning (RL-QPSO Net) aimed at improving global optimality and adaptability in path planning. The RL-QPSO Net combines quantum-inspired particle swarm optimization (QPSO) and deep reinforcement learning (DRL) modules through a dual control mechanism to achieve path optimization and environmental adaptation. The QPSO module is responsible for global path optimization, using quantum mechanics to avoid local optima, while the DRL module adjusts strategies in real-time based on environmental feedback, thus enhancing decision-making capabilities in complex high-dimensional scenarios.Results and discussionExperiments were conducted on multiple datasets, including Cityscapes, NYU Depth V2, Mapillary Vistas, and ApolloScape, and the results showed that RL-QPSO Net outperforms traditional methods in terms of accuracy, computational efficiency, and model complexity. This method demonstrated significant improvements in accuracy and computational efficiency, providing an effective path planning solution for real-time applications in complex environments for mobile robots. In the future, this method could be further extended to resource-limited environments to achieve broader practical applications. |
| format | Article |
| id | doaj-art-6dad171749cd427fa903edabfe673a36 |
| institution | Kabale University |
| issn | 1662-5218 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurorobotics |
| spelling | doaj-art-6dad171749cd427fa903edabfe673a362025-01-08T06:11:51ZengFrontiers Media S.A.Frontiers in Neurorobotics1662-52182025-01-011810.3389/fnbot.2024.14645721464572RL-QPSO net: deep reinforcement learning-enhanced QPSO for efficient mobile robot path planningYang JingLi WeiyaIntroductionPath planning in complex and dynamic environments poses a significant challenge in the field of mobile robotics. Traditional path planning methods such as genetic algorithms, Dijkstra's algorithm, and Floyd's algorithm typically rely on deterministic search strategies, which can lead to local optima and lack global search capabilities in dynamic settings. These methods have high computational costs and are not efficient for real-time applications.MethodsTo address these issues, this paper presents a Quantum-behaved Particle Swarm Optimization model enhanced by deep reinforcement learning (RL-QPSO Net) aimed at improving global optimality and adaptability in path planning. The RL-QPSO Net combines quantum-inspired particle swarm optimization (QPSO) and deep reinforcement learning (DRL) modules through a dual control mechanism to achieve path optimization and environmental adaptation. The QPSO module is responsible for global path optimization, using quantum mechanics to avoid local optima, while the DRL module adjusts strategies in real-time based on environmental feedback, thus enhancing decision-making capabilities in complex high-dimensional scenarios.Results and discussionExperiments were conducted on multiple datasets, including Cityscapes, NYU Depth V2, Mapillary Vistas, and ApolloScape, and the results showed that RL-QPSO Net outperforms traditional methods in terms of accuracy, computational efficiency, and model complexity. This method demonstrated significant improvements in accuracy and computational efficiency, providing an effective path planning solution for real-time applications in complex environments for mobile robots. In the future, this method could be further extended to resource-limited environments to achieve broader practical applications.https://www.frontiersin.org/articles/10.3389/fnbot.2024.1464572/fullpath planningQuantum-behaved Particle Swarm Optimizationdeep reinforcement learningmobile roboticscomplex environments |
| spellingShingle | Yang Jing Li Weiya RL-QPSO net: deep reinforcement learning-enhanced QPSO for efficient mobile robot path planning Frontiers in Neurorobotics path planning Quantum-behaved Particle Swarm Optimization deep reinforcement learning mobile robotics complex environments |
| title | RL-QPSO net: deep reinforcement learning-enhanced QPSO for efficient mobile robot path planning |
| title_full | RL-QPSO net: deep reinforcement learning-enhanced QPSO for efficient mobile robot path planning |
| title_fullStr | RL-QPSO net: deep reinforcement learning-enhanced QPSO for efficient mobile robot path planning |
| title_full_unstemmed | RL-QPSO net: deep reinforcement learning-enhanced QPSO for efficient mobile robot path planning |
| title_short | RL-QPSO net: deep reinforcement learning-enhanced QPSO for efficient mobile robot path planning |
| title_sort | rl qpso net deep reinforcement learning enhanced qpso for efficient mobile robot path planning |
| topic | path planning Quantum-behaved Particle Swarm Optimization deep reinforcement learning mobile robotics complex environments |
| url | https://www.frontiersin.org/articles/10.3389/fnbot.2024.1464572/full |
| work_keys_str_mv | AT yangjing rlqpsonetdeepreinforcementlearningenhancedqpsoforefficientmobilerobotpathplanning AT liweiya rlqpsonetdeepreinforcementlearningenhancedqpsoforefficientmobilerobotpathplanning |