GTrXL-SAC-Based Path Planning and Obstacle-Aware Control Decision-Making for UAV Autonomous Control
Research on UAV (unmanned aerial vehicle) path planning and obstacle avoidance control based on DRL (deep reinforcement learning) still faces limitations, as previous studies primarily utilized current perceptual inputs while neglecting the continuity of flight processes, resulting in low early-stag...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Drones |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-446X/9/4/275 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850144858507837440 |
|---|---|
| author | Jingyi Huang Yujie Cui Guipeng Xi Shuangxia Bai Bo Li Geng Wang Evgeny Neretin |
| author_facet | Jingyi Huang Yujie Cui Guipeng Xi Shuangxia Bai Bo Li Geng Wang Evgeny Neretin |
| author_sort | Jingyi Huang |
| collection | DOAJ |
| description | Research on UAV (unmanned aerial vehicle) path planning and obstacle avoidance control based on DRL (deep reinforcement learning) still faces limitations, as previous studies primarily utilized current perceptual inputs while neglecting the continuity of flight processes, resulting in low early-stage learning efficiency. To address these issues, this paper integrates DRL with the Transformer architecture to propose the GTrXL-SAC (gated Transformer-XL soft actor critic) algorithm. The algorithm performs positional embedding on multimodal data combining visual and sensor information. Leveraging the self-attention mechanism of GTrXL, it effectively focuses on different segments of multimodal data for encoding while capturing sequential relationships, significantly improving obstacle recognition accuracy and enhancing both learning efficiency and sample efficiency. Additionally, the algorithm capitalizes on GTrXL’s memory characteristics to generate current drone control decisions through the combined analysis of historical experiences and present states, effectively mitigating long-term dependency issues. Experimental results in the AirSim drone simulation environment demonstrate that compared to PPO and SAC algorithms, GTrXL-SAC achieves more precise policy exploration and optimization, enabling superior control of drone velocity and attitude for stabilized flight while accelerating convergence speed by nearly 20%. |
| format | Article |
| id | doaj-art-2afe2c94a9034fa2aade6de7d4e8c949 |
| institution | OA Journals |
| issn | 2504-446X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-2afe2c94a9034fa2aade6de7d4e8c9492025-08-20T02:28:14ZengMDPI AGDrones2504-446X2025-04-019427510.3390/drones9040275GTrXL-SAC-Based Path Planning and Obstacle-Aware Control Decision-Making for UAV Autonomous ControlJingyi Huang0Yujie Cui1Guipeng Xi2Shuangxia Bai3Bo Li4Geng Wang5Evgeny Neretin6School of Electronics Information, Northwestern Polytechnical University, 127 Youyi West Road, Xi’an 710072, ChinaSchool of Electronics Information, Northwestern Polytechnical University, 127 Youyi West Road, Xi’an 710072, ChinaSchool of Electronics Information, Northwestern Polytechnical University, 127 Youyi West Road, Xi’an 710072, ChinaSchool of Computing, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon Tong, Hong Kong SAR, ChinaSchool of Electronics Information, Northwestern Polytechnical University, 127 Youyi West Road, Xi’an 710072, ChinaSchool of Electronics Information, Northwestern Polytechnical University, 127 Youyi West Road, Xi’an 710072, ChinaMoscow Aviation Institute, Moscow 125993, RussiaResearch on UAV (unmanned aerial vehicle) path planning and obstacle avoidance control based on DRL (deep reinforcement learning) still faces limitations, as previous studies primarily utilized current perceptual inputs while neglecting the continuity of flight processes, resulting in low early-stage learning efficiency. To address these issues, this paper integrates DRL with the Transformer architecture to propose the GTrXL-SAC (gated Transformer-XL soft actor critic) algorithm. The algorithm performs positional embedding on multimodal data combining visual and sensor information. Leveraging the self-attention mechanism of GTrXL, it effectively focuses on different segments of multimodal data for encoding while capturing sequential relationships, significantly improving obstacle recognition accuracy and enhancing both learning efficiency and sample efficiency. Additionally, the algorithm capitalizes on GTrXL’s memory characteristics to generate current drone control decisions through the combined analysis of historical experiences and present states, effectively mitigating long-term dependency issues. Experimental results in the AirSim drone simulation environment demonstrate that compared to PPO and SAC algorithms, GTrXL-SAC achieves more precise policy exploration and optimization, enabling superior control of drone velocity and attitude for stabilized flight while accelerating convergence speed by nearly 20%.https://www.mdpi.com/2504-446X/9/4/275SACself-attention mechanismTransformerUAV control decision-makingmultimodal data |
| spellingShingle | Jingyi Huang Yujie Cui Guipeng Xi Shuangxia Bai Bo Li Geng Wang Evgeny Neretin GTrXL-SAC-Based Path Planning and Obstacle-Aware Control Decision-Making for UAV Autonomous Control Drones SAC self-attention mechanism Transformer UAV control decision-making multimodal data |
| title | GTrXL-SAC-Based Path Planning and Obstacle-Aware Control Decision-Making for UAV Autonomous Control |
| title_full | GTrXL-SAC-Based Path Planning and Obstacle-Aware Control Decision-Making for UAV Autonomous Control |
| title_fullStr | GTrXL-SAC-Based Path Planning and Obstacle-Aware Control Decision-Making for UAV Autonomous Control |
| title_full_unstemmed | GTrXL-SAC-Based Path Planning and Obstacle-Aware Control Decision-Making for UAV Autonomous Control |
| title_short | GTrXL-SAC-Based Path Planning and Obstacle-Aware Control Decision-Making for UAV Autonomous Control |
| title_sort | gtrxl sac based path planning and obstacle aware control decision making for uav autonomous control |
| topic | SAC self-attention mechanism Transformer UAV control decision-making multimodal data |
| url | https://www.mdpi.com/2504-446X/9/4/275 |
| work_keys_str_mv | AT jingyihuang gtrxlsacbasedpathplanningandobstacleawarecontroldecisionmakingforuavautonomouscontrol AT yujiecui gtrxlsacbasedpathplanningandobstacleawarecontroldecisionmakingforuavautonomouscontrol AT guipengxi gtrxlsacbasedpathplanningandobstacleawarecontroldecisionmakingforuavautonomouscontrol AT shuangxiabai gtrxlsacbasedpathplanningandobstacleawarecontroldecisionmakingforuavautonomouscontrol AT boli gtrxlsacbasedpathplanningandobstacleawarecontroldecisionmakingforuavautonomouscontrol AT gengwang gtrxlsacbasedpathplanningandobstacleawarecontroldecisionmakingforuavautonomouscontrol AT evgenyneretin gtrxlsacbasedpathplanningandobstacleawarecontroldecisionmakingforuavautonomouscontrol |