Deep reinforcement learning and robust SLAM based robotic control algorithm for self-driving path optimization
A reward shaping deep deterministic policy gradient (RS-DDPG) and simultaneous localization and mapping (SLAM) path tracking algorithm is proposed to address the issues of low accuracy and poor robustness of target path tracking for robotic control during maneuver. RS-DDPG algorithm is based on deep...
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| Format: | Article |
| Language: | English |
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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.2024.1428358/full |
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| author | Samiullah Khan Ashfaq Niaz Dou Yinke Muhammad Usman Shoukat Saqib Ali Nawaz |
| author_facet | Samiullah Khan Ashfaq Niaz Dou Yinke Muhammad Usman Shoukat Saqib Ali Nawaz |
| author_sort | Samiullah Khan |
| collection | DOAJ |
| description | A reward shaping deep deterministic policy gradient (RS-DDPG) and simultaneous localization and mapping (SLAM) path tracking algorithm is proposed to address the issues of low accuracy and poor robustness of target path tracking for robotic control during maneuver. RS-DDPG algorithm is based on deep reinforcement learning (DRL) and designs a reward function to optimize the parameters of DDPG to achieve the required tracking accuracy and stability. A visual SLAM algorithm based on semantic segmentation and geometric information is proposed to address the issues of poor robustness and susceptibility to interference from dynamic objects in dynamic scenes for SLAM based on visual sensors. Using the Apollo autonomous driving simulation platform, simulation experiments were conducted on the actual DDPG algorithm and the improved RS-DDPG path-tracking control algorithm. The research results indicate that the proposed RS-DDPG algorithm outperforms the DDPG algorithm in terms of path tracking accuracy and robustness. The results showed that it effectively improved the performance of visual SLAM systems in dynamic scenarios. |
| format | Article |
| id | doaj-art-4a9f0d536d35480684b9df2281421d6c |
| 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-4a9f0d536d35480684b9df2281421d6c2025-01-15T13:28:56ZengFrontiers Media S.A.Frontiers in Neurorobotics1662-52182025-01-011810.3389/fnbot.2024.14283581428358Deep reinforcement learning and robust SLAM based robotic control algorithm for self-driving path optimizationSamiullah Khan0Ashfaq Niaz1Dou Yinke2Muhammad Usman Shoukat3Saqib Ali Nawaz4College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan, ChinaCollege of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan, ChinaCollege of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan, ChinaHubei Key Laboratory of Advanced Technology for Automotive Components, School of Automotive Engineering, Wuhan University of Technology, Wuhan, ChinaSchool of Information and Communication Engineering, Hainan University, Haikou, ChinaA reward shaping deep deterministic policy gradient (RS-DDPG) and simultaneous localization and mapping (SLAM) path tracking algorithm is proposed to address the issues of low accuracy and poor robustness of target path tracking for robotic control during maneuver. RS-DDPG algorithm is based on deep reinforcement learning (DRL) and designs a reward function to optimize the parameters of DDPG to achieve the required tracking accuracy and stability. A visual SLAM algorithm based on semantic segmentation and geometric information is proposed to address the issues of poor robustness and susceptibility to interference from dynamic objects in dynamic scenes for SLAM based on visual sensors. Using the Apollo autonomous driving simulation platform, simulation experiments were conducted on the actual DDPG algorithm and the improved RS-DDPG path-tracking control algorithm. The research results indicate that the proposed RS-DDPG algorithm outperforms the DDPG algorithm in terms of path tracking accuracy and robustness. The results showed that it effectively improved the performance of visual SLAM systems in dynamic scenarios.https://www.frontiersin.org/articles/10.3389/fnbot.2024.1428358/fullautonomous navigationrobotic controlpath trackingdeep reinforcement learningSLAMRS-DDPG algorithm |
| spellingShingle | Samiullah Khan Ashfaq Niaz Dou Yinke Muhammad Usman Shoukat Saqib Ali Nawaz Deep reinforcement learning and robust SLAM based robotic control algorithm for self-driving path optimization Frontiers in Neurorobotics autonomous navigation robotic control path tracking deep reinforcement learning SLAM RS-DDPG algorithm |
| title | Deep reinforcement learning and robust SLAM based robotic control algorithm for self-driving path optimization |
| title_full | Deep reinforcement learning and robust SLAM based robotic control algorithm for self-driving path optimization |
| title_fullStr | Deep reinforcement learning and robust SLAM based robotic control algorithm for self-driving path optimization |
| title_full_unstemmed | Deep reinforcement learning and robust SLAM based robotic control algorithm for self-driving path optimization |
| title_short | Deep reinforcement learning and robust SLAM based robotic control algorithm for self-driving path optimization |
| title_sort | deep reinforcement learning and robust slam based robotic control algorithm for self driving path optimization |
| topic | autonomous navigation robotic control path tracking deep reinforcement learning SLAM RS-DDPG algorithm |
| url | https://www.frontiersin.org/articles/10.3389/fnbot.2024.1428358/full |
| work_keys_str_mv | AT samiullahkhan deepreinforcementlearningandrobustslambasedroboticcontrolalgorithmforselfdrivingpathoptimization AT ashfaqniaz deepreinforcementlearningandrobustslambasedroboticcontrolalgorithmforselfdrivingpathoptimization AT douyinke deepreinforcementlearningandrobustslambasedroboticcontrolalgorithmforselfdrivingpathoptimization AT muhammadusmanshoukat deepreinforcementlearningandrobustslambasedroboticcontrolalgorithmforselfdrivingpathoptimization AT saqibalinawaz deepreinforcementlearningandrobustslambasedroboticcontrolalgorithmforselfdrivingpathoptimization |