• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

基于人机混合增强智能的无人直升机路径规划。

Path Planning of Unmanned Autonomous Helicopter Based on Human-Computer Hybrid Augmented Intelligence.

机构信息

College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.

Science and Technology on Electro-optic Control Laboratory, Luoyang Institute of Electro-Optical Equipment of Avic, Luoyang 471000, China.

出版信息

Neural Plast. 2021 Jan 13;2021:6639664. doi: 10.1155/2021/6639664. eCollection 2021.

DOI:10.1155/2021/6639664
PMID:33519928
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7817272/
Abstract

Unmanned autonomous helicopter (UAH) path planning problem is an important component of the UAH mission planning system. The performance of the automatic path planner determines the quality of the UAH flight path. Aiming to produce a high-quality flight path, a path planning system is designed based on human-computer hybrid augmented intelligence framework for the UAH in this paper. Firstly, an improved artificial bee colony (I-ABC) algorithm is proposed based on the dynamic evaluation selection strategy and the complex optimization method. In the I-ABC algorithm, the following way of on-looker bees and the update strategy of nectar source are optimized to accelerate the convergence rate and retain the exploration ability of the population. In addition, a space clipping operation is proposed based on the attention mechanism for constructing a new spatial search area. The search time can be further reduced by the space clipping operation under the path planning result within acceptable changes. Moreover, the entire optimization process and results can be feeded back to the knowledge database by the human-computer hybrid augmented intelligence framework to guide subsequent path planning issues. Finally, the simulation results confirm that a feasible and effective flight path can be quickly generated by the UAH path planning system based on human-computer hybrid augmented intelligence.

摘要

无人自主直升机 (UAH) 路径规划问题是 UAH 任务规划系统的重要组成部分。自动路径规划器的性能决定了 UAH 飞行路径的质量。针对这一问题，本文基于人机混合增强智能框架，为 UAH 设计了一种基于人机混合增强智能框架的路径规划系统。首先，提出了一种基于动态评价选择策略和复杂优化方法的改进人工蜂群 (I-ABC) 算法。在 I-ABC 算法中，对侦察蜂和蜜源更新策略进行了优化，以提高收敛速度并保留种群的探索能力。此外，基于注意力机制提出了一种空间裁剪操作，用于构建新的空间搜索区域。通过空间裁剪操作，可以在可接受的变化范围内进一步减少路径规划结果的搜索时间。此外，整个优化过程和结果可以通过人机混合增强智能框架反馈到知识库中，以指导后续的路径规划问题。最后，仿真结果证实，基于人机混合增强智能的 UAH 路径规划系统可以快速生成可行有效的飞行路径。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/244c84234bf3/NP2021-6639664.alg.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/fa0ae4957751/NP2021-6639664.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/95a8b469187c/NP2021-6639664.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/6578092b3ebb/NP2021-6639664.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/4820868a5b8b/NP2021-6639664.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/0b2c93e5de51/NP2021-6639664.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/d4e0bb54ecfa/NP2021-6639664.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/bc2591c149f8/NP2021-6639664.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/f3ea5ebd6b50/NP2021-6639664.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/03d10565459b/NP2021-6639664.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/21bbe48d1a4c/NP2021-6639664.010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/b244e4854bc7/NP2021-6639664.011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/c2310663e77e/NP2021-6639664.012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/6c9add85e551/NP2021-6639664.013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/7cc6568ca489/NP2021-6639664.014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/102eb097432a/NP2021-6639664.015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/e1d045186dde/NP2021-6639664.016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/a0036282ed79/NP2021-6639664.017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/e86c927059b0/NP2021-6639664.018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/fcba807af317/NP2021-6639664.019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/d509e442b2f9/NP2021-6639664.020.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/f6df8aaaca0d/NP2021-6639664.021.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/444818232572/NP2021-6639664.022.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/d09023fd923e/NP2021-6639664.023.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/5a1912f43405/NP2021-6639664.024.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/f589db604d31/NP2021-6639664.025.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/a72154feec6f/NP2021-6639664.026.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/244c84234bf3/NP2021-6639664.alg.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/fa0ae4957751/NP2021-6639664.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/95a8b469187c/NP2021-6639664.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/6578092b3ebb/NP2021-6639664.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/4820868a5b8b/NP2021-6639664.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/0b2c93e5de51/NP2021-6639664.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/d4e0bb54ecfa/NP2021-6639664.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/bc2591c149f8/NP2021-6639664.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/f3ea5ebd6b50/NP2021-6639664.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/03d10565459b/NP2021-6639664.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/21bbe48d1a4c/NP2021-6639664.010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/b244e4854bc7/NP2021-6639664.011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/c2310663e77e/NP2021-6639664.012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/6c9add85e551/NP2021-6639664.013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/7cc6568ca489/NP2021-6639664.014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/102eb097432a/NP2021-6639664.015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/e1d045186dde/NP2021-6639664.016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/a0036282ed79/NP2021-6639664.017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/e86c927059b0/NP2021-6639664.018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/fcba807af317/NP2021-6639664.019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/d509e442b2f9/NP2021-6639664.020.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/f6df8aaaca0d/NP2021-6639664.021.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/444818232572/NP2021-6639664.022.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/d09023fd923e/NP2021-6639664.023.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/5a1912f43405/NP2021-6639664.024.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/f589db604d31/NP2021-6639664.025.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/a72154feec6f/NP2021-6639664.026.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7817272/244c84234bf3/NP2021-6639664.alg.001.jpg

相似文献

1
Path Planning of Unmanned Autonomous Helicopter Based on Human-Computer Hybrid Augmented Intelligence.基于人机混合增强智能的无人直升机路径规划。
Neural Plast. 2021 Jan 13;2021:6639664. doi: 10.1155/2021/6639664. eCollection 2021.
2
Three-Dimensional Path Planning for Uninhabited Combat Aerial Vehicle Based on Predator-Prey Pigeon-Inspired Optimization in Dynamic Environment.基于捕食-被捕食鸽启发式优化的动态环境下无人作战飞行器三维路径规划
IEEE/ACM Trans Comput Biol Bioinform. 2017 Jan-Feb;14(1):97-107. doi: 10.1109/TCBB.2015.2443789.
3
A hybrid artificial bee colony optimization and quantum evolutionary algorithm for continuous optimization problems.混合人工蜂群优化和量子进化算法求解连续优化问题
Int J Neural Syst. 2010 Feb;20(1):39-50. doi: 10.1142/S012906571000222X.
4
UAV Path Planning Algorithm Based on Improved Harris Hawks Optimization.基于改进哈里斯鹰优化算法的无人机路径规划
Sensors (Basel). 2022 Jul 13;22(14):5232. doi: 10.3390/s22145232.
5
Multi-objective path planning for mobile robot with an improved artificial bee colony algorithm.多目标移动机器人路径规划的改进人工蜂群算法。
Math Biosci Eng. 2023 Jan;20(2):2501-2529. doi: 10.3934/mbe.2023117. Epub 2022 Nov 23.
6
A Novel Teaching-Learning-Based Optimization with Error Correction and Cauchy Distribution for Path Planning of Unmanned Air Vehicle.一种基于误差校正和柯西分布的新型教与学优化算法在无人机路径规划中的应用。
Comput Intell Neurosci. 2018 Aug 1;2018:5671709. doi: 10.1155/2018/5671709. eCollection 2018.
7
An improved artificial bee colony algorithm based on balance-evolution strategy for unmanned combat aerial vehicle path planning.一种基于平衡进化策略的改进人工蜂群算法用于无人机路径规划
ScientificWorldJournal. 2014 Mar 20;2014:232704. doi: 10.1155/2014/232704. eCollection 2014.
8
Grid-Based Mobile Robot Path Planning Using Aging-Based Ant Colony Optimization Algorithm in Static and Dynamic Environments.基于栅格的移动机器人路径规划在静态和动态环境中使用基于老化的蚁群优化算法。
Sensors (Basel). 2020 Mar 28;20(7):1880. doi: 10.3390/s20071880.
9
A Hybrid Multi-Target Path Planning Algorithm for Unmanned Cruise Ship in an Unknown Obstacle Environment.一种用于未知障碍物环境下的无人巡航船的混合多目标路径规划算法。
Sensors (Basel). 2022 Mar 22;22(7):2429. doi: 10.3390/s22072429.
10
A self adaptive hybrid enhanced artificial bee colony algorithm for continuous optimization problems.一种用于连续优化问题的自适应混合增强人工蜂群算法。
Biosystems. 2015 Jun;132-133:43-53. doi: 10.1016/j.biosystems.2015.05.002. Epub 2015 May 14.

本文引用的文献

1
Detecting prostate cancer using deep learning convolution neural network with transfer learning approach.使用具有迁移学习方法的深度学习卷积神经网络检测前列腺癌。
Cogn Neurodyn. 2020 Aug;14(4):523-533. doi: 10.1007/s11571-020-09587-5. Epub 2020 Apr 11.
2
Classification of myocardial infarction based on hybrid feature extraction and artificial intelligence tools by adopting tunable-Q wavelet transform (TQWT), variational mode decomposition (VMD) and neural networks.基于可调 Q 小波变换（TQWT）、变分模态分解（VMD）和神经网络的混合特征提取和人工智能工具的心肌梗死分类。
Artif Intell Med. 2020 Jun;106:101848. doi: 10.1016/j.artmed.2020.101848. Epub 2020 May 18.
3
Variation of functional brain connectivity in epileptic seizures: an EEG analysis with cross-frequency phase synchronization.
癫痫发作中功能性脑连接的变化：一项基于交叉频率相位同步的脑电图分析
Cogn Neurodyn. 2020 Feb;14(1):35-49. doi: 10.1007/s11571-019-09551-y. Epub 2019 Aug 12.
4
Automated detection of driver fatigue based on EEG signals using gradient boosting decision tree model.基于梯度提升决策树模型利用脑电图信号自动检测驾驶员疲劳。
Cogn Neurodyn. 2018 Aug;12(4):431-440. doi: 10.1007/s11571-018-9485-1. Epub 2018 Apr 16.
5
Investigating neural efficiency of elite karate athletes during a mental arithmetic task using EEG.使用脑电图研究精英空手道运动员在进行心算任务时的神经效率。
Cogn Neurodyn. 2018 Feb;12(1):95-102. doi: 10.1007/s11571-017-9464-y. Epub 2017 Dec 4.
6
Effect of education on ability of AI professionals and herd-owner inseminators to detect cows not in oestrus and its relation with progesterone concentration on day of re-insemination.教育对人工智能专业人员和养殖场人工授精员检测未发情母牛能力的影响及其与再次输精日孕酮浓度的关系。
Theriogenology. 2017 Oct 15;102:23-28. doi: 10.1016/j.theriogenology.2017.07.007. Epub 2017 Jul 13.
7
A multiuser detector based on artificial bee colony algorithm for DS-UWB systems.一种用于直接序列超宽带（DS-UWB）系统的基于人工蜂群算法的多用户检测器。
ScientificWorldJournal. 2013 Jul 31;2013:547656. doi: 10.1155/2013/547656. eCollection 2013.