An Improved HM-SAC-CA Algorithm for Mobile Robot Path Planning in Unknown Complex Environments
Path planning and its optimization is a critical and difficult task for a mobile robot in a complex and unknown environment. To tackle this problem, we propose an improved SAC (HM-SAC-CA) algorithm for path planning in unknown complex environments. First, based on the SAC maximum entropy framework,...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10856113/ |
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| author | Ting Jiao Conglin Hu Lingxin Kong Xihao Zhao Zhongbao Wang |
| author_facet | Ting Jiao Conglin Hu Lingxin Kong Xihao Zhao Zhongbao Wang |
| author_sort | Ting Jiao |
| collection | DOAJ |
| description | Path planning and its optimization is a critical and difficult task for a mobile robot in a complex and unknown environment. To tackle this problem, we propose an improved SAC (HM-SAC-CA) algorithm for path planning in unknown complex environments. First, based on the SAC maximum entropy framework, a deep reinforcement learning algorithm with clipped automatic entropy adjustment is proposed to improve the quality of policy learning by suppressing entropy evaluation. Second, an innovative hierarchical experience storage structure is constructed during experience replay, and the overfitting phenomenon caused by using good experiences is eliminated by a bias-free sampling strategy. Finally, a posture reward function and a staged incentive mechanism are proposed. The staged incentive mechanism uses both the sparse reward function and the posture reward function in stages to reduce the blindness of exploration during training and accelerate the training learning process. Experiments are conducted using a simulated Turtlebot3 and a real mobile robot and the results validate the performance of the proposed work. |
| format | Article |
| id | doaj-art-5bdd80514ff64cd7a3b72c8b0305995d |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-5bdd80514ff64cd7a3b72c8b0305995d2025-02-05T00:00:54ZengIEEEIEEE Access2169-35362025-01-0113211522116310.1109/ACCESS.2025.353572810856113An Improved HM-SAC-CA Algorithm for Mobile Robot Path Planning in Unknown Complex EnvironmentsTing Jiao0https://orcid.org/0000-0003-2343-9932Conglin Hu1https://orcid.org/0009-0002-1628-5998Lingxin Kong2https://orcid.org/0009-0001-8502-9069Xihao Zhao3Zhongbao Wang4School of Automation and Software Engineering, Shanxi University, Taiyuan, Shanxi, ChinaSchool of Automation and Software Engineering, Shanxi University, Taiyuan, Shanxi, ChinaSchool of Automation and Software Engineering, Shanxi University, Taiyuan, Shanxi, ChinaSchool of Automation and Software Engineering, Shanxi University, Taiyuan, Shanxi, ChinaSchool of Automation and Software Engineering, Shanxi University, Taiyuan, Shanxi, ChinaPath planning and its optimization is a critical and difficult task for a mobile robot in a complex and unknown environment. To tackle this problem, we propose an improved SAC (HM-SAC-CA) algorithm for path planning in unknown complex environments. First, based on the SAC maximum entropy framework, a deep reinforcement learning algorithm with clipped automatic entropy adjustment is proposed to improve the quality of policy learning by suppressing entropy evaluation. Second, an innovative hierarchical experience storage structure is constructed during experience replay, and the overfitting phenomenon caused by using good experiences is eliminated by a bias-free sampling strategy. Finally, a posture reward function and a staged incentive mechanism are proposed. The staged incentive mechanism uses both the sparse reward function and the posture reward function in stages to reduce the blindness of exploration during training and accelerate the training learning process. Experiments are conducted using a simulated Turtlebot3 and a real mobile robot and the results validate the performance of the proposed work.https://ieeexplore.ieee.org/document/10856113/Complex environmentdeep reinforcement learningmobile robotpath planning |
| spellingShingle | Ting Jiao Conglin Hu Lingxin Kong Xihao Zhao Zhongbao Wang An Improved HM-SAC-CA Algorithm for Mobile Robot Path Planning in Unknown Complex Environments IEEE Access Complex environment deep reinforcement learning mobile robot path planning |
| title | An Improved HM-SAC-CA Algorithm for Mobile Robot Path Planning in Unknown Complex Environments |
| title_full | An Improved HM-SAC-CA Algorithm for Mobile Robot Path Planning in Unknown Complex Environments |
| title_fullStr | An Improved HM-SAC-CA Algorithm for Mobile Robot Path Planning in Unknown Complex Environments |
| title_full_unstemmed | An Improved HM-SAC-CA Algorithm for Mobile Robot Path Planning in Unknown Complex Environments |
| title_short | An Improved HM-SAC-CA Algorithm for Mobile Robot Path Planning in Unknown Complex Environments |
| title_sort | improved hm sac ca algorithm for mobile robot path planning in unknown complex environments |
| topic | Complex environment deep reinforcement learning mobile robot path planning |
| url | https://ieeexplore.ieee.org/document/10856113/ |
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