A Q-Learning Crested Porcupine Optimizer for Adaptive UAV Path Planning
文献类型：期刊论文
| 作者 | Liu, Jiandong2; He, Yuejun2; Shen, Bing1; Wang, Jing1; Wang, Penggang2; Zhang, Guoqing2; Zhuang, Xiang2; Chen, Ran2; Luo, Wei3 |
| 刊名 | MACHINES
 |
| 出版日期 | 2025-06-30 |
| 卷号 | 13期号:7页码:566 |
| 关键词 | UAV path planning UAV inspection CPO optimization Q-learning algorithm fusion |
| DOI | 10.3390/machines13070566 |
| 产权排序 | 3 |
| 文献子类 | Article |
| 英文摘要 | Unmanned Aerial Vehicle (UAV) path planning is critical for ensuring flight safety and enhancing mission execution efficiency. This problem is typically formulated as a complex, multi-constrained, and nonlinear optimization task, often addressed using meta-heuristic algorithms. The Crested Porcupine Optimizer (CPO) has become an excellent method to solve this problem; however, the standard CPO has limitations, such as the lack of adaptive parameter tuning to adapt to complex environments, slow convergence, and the tendency to fall into local optimal solutions. To address these issues, this paper proposes an algorithm named QCPO, which integrates CPO with Q-learning to improve UAV path optimization performance. Q-learning is employed to adaptively adjust the key parameters of the CPO, thereby overcoming the limitations of traditional fixed-parameter settings. Inspired by the porcupine's defense mechanisms, a novel audiovisual coordination strategy is introduced to balance visual and auditory responses, accelerating convergence in the early optimization stages. A refined position update mechanism is designed to prevent excessive step sizes and boundary violations, enhancing the algorithm's global search capability. A B-spline-based trajectory smoothing method is also incorporated to improve the feasibility and smoothness of the planned paths. In this paper, we compare QCPO with four outstanding heuristics, and QCPO achieves the lowest path cost in all three test scenarios, with path cost reductions of 30.23%, 26.41%, and 33.47%, respectively, compared to standard CPO. The experimental results confirm that QCPO offers an efficient and safe solution for UAV path planning. |
| URL标识 | 查看原文 |
| WOS关键词 | ALGORITHM ; SEARCH |
| WOS研究方向 | Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001536087300001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/215523]  |
| 专题 | 陆地表层格局与模拟院重点实验室_外文论文 |
| 通讯作者 | He, Yuejun |
| 作者单位 | 1.Beijing Inst Tracking & Telecommun Technol, Beijing 100005, Peoples R China; 2.North China Inst Aerosp Engn, Langfang 065000, Peoples R China; 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Jiandong,He, Yuejun,Shen, Bing,et al. A Q-Learning Crested Porcupine Optimizer for Adaptive UAV Path Planning[J]. MACHINES,2025,13(7):566. |
| APA | Liu, Jiandong.,He, Yuejun.,Shen, Bing.,Wang, Jing.,Wang, Penggang.,...&Luo, Wei.(2025).A Q-Learning Crested Porcupine Optimizer for Adaptive UAV Path Planning.MACHINES,13(7),566. |
| MLA | Liu, Jiandong,et al."A Q-Learning Crested Porcupine Optimizer for Adaptive UAV Path Planning".MACHINES 13.7(2025):566. |
入库方式： OAI收割
来源：地理科学与资源研究所
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