[1]LOU Chuanwei,GE Quanbo,LIU Huaping,et al.Active perception method for UAV group target search[J].CAAI Transactions on Intelligent Systems,2021,16(3):575-583.[doi:10.11992/tis.202009012]
Copy

Active perception method for UAV group target search

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 3 Page number: 575-583 Column: 吴文俊人工智能科学技术奖论坛 Public date: 2021-05-05
Title:
Active perception method for UAV group target search
Author(s):
LOU Chuanwei1;  GE Quanbo2;  LIU Huaping3;  YUAN Xiaohu4
1. Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China;
2. School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China;
3. Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China;
4. Department of Automation, Tsinghua University, Beijing 100084, China
Keywords:
unmanned aerial vehicle; ant colony; without prior information of the target; an unsearched probability with exploration preference; active perception search framework; unknown region; motion mode selection mechanism; environmental information
CLC:
TP393
DOI:
10.11992/tis.202009012
Abstract:
To enhance the search efficiency of the ant colony algorithm for unknown targets in a large-scale grid environment, an active perception search framework based on the ant colony algorithm is proposed. In this framework, the unmanned aerial vehicle (UAV) motion mode was selected using the historical environment information. The new environment information was obtained from the motion mode and sensing domain information of the UAV to enhance the intelligent automatic search function of the UAV group. The new algorithm calculates an unsearched probability with exploration preference to carry out a UAV search with a bias towards the grid with the highest unsearched degree, which improves the algorithm’s searchability. Additionally, based on the unsearched probability and pheromone, a new motion mode selection mechanism was developed. This mechanism considers the possible known and unknown target regions for searching targets with no prior information. The simulation results showed that this algorithm has higher search efficiency and more comprehensive target distribution information than the existing algorithms used in large-scale grid environments.
References:
[1] 齐小刚, 李博, 范英盛, 等. 多约束下多无人机的任务规划研究综述[J]. 智能系统学报, 2020, 15(2):204-217
QI Xiaogang, LI Bo, FAN YingSheng, et al. A survey of mission planning on UAV system based on multi-constraints[J]. CAAI transactions on intelligent systems, 2020, 15(2):204-217
[2] 张松灿, 普杰信, 司彦娜, 等. 蚁群算法在移动机器人路径规划中的应用综述[J]. 计算机工程与应用, 2020, 56(8):10-19
ZHANG Songcan, PU Jiexin, SI Yanna, et al. Survey on application of ant colony algorithm in path planning of mobile robot[J]. Computer engineering and applications, 2020, 56(8):10-19
[3] 鲁亚飞, 吴岸平, 陈清阳. 无人机对地目标多帧融合定位与误差收敛特性分析[J]. 国防科技大学学报, 2021, 43(2):66-73
LU Yafei, WU Anping, CHEN Qingyang. Multi frame fusion positioning and error convergence analysis of UAV to ground target[J]. Journal of National University of Defense Science and Technology, 2021, 43(2):66-73
[4] 李坎, 李云鹏, 赵江波. 无人机集群自组织搜索仿真模型设计与实现[J/OL]. 系统仿真学报:1?14.[2021?06?16] http://kns.cnki.net/kcms/detail/11.3092.V.20210602.1421.004.html.
LI Kan, LI Yunpeng, ZHAO Jiangbo. Design and implementation of UAV swarm self-organizing search model[J/OL]. Journal of system simulation:1?14.[2020?06?16] http://kns.cnki.net/kcms/detail/11.3092.V.20210602.1421.004.html.
[5] 辛建霖, 左家亮, 岳龙飞, 等. 基于改进启发式蚁群算法的无人机自主航迹规划[J/OL]. 航空工程进展:1?8.[2021?06?16] http://kns.cnki.net/kcms/detail/61.1479.V.20210524.1220.010.html.
XIN Jianlin, ZUO Jialiang, YUE Longfei, et al. Autonomous path planning for unmanned aerial vehicle based on improved heuristic ant colony algorithm[J/OL]. Advances in aeronautical science and engineering:1?8.[2021?06?16] http://kns.cnki.net/kcms/detail/61.1479.V.20210524.1220.010.html.
[6] 樊娇, 雷涛, 韩伟, 等. 无人机航迹规划技术研究综述[J]. 郑州大学学报(工学版), 2021, 42(3):39-46
FAN Jiao, LEI Tao, HAN Wei, et al. A survey of UAV path planning[J]. Journal of Zhengzhou University (engineering science), 2021, 42(3):39-46
[7] 裴振兵, 陈雪. 波改进蚁群算法及其在机器人避障中的应用[J]. 智能系统学报, 2015, 10(1):90-96
PEI Zhenbing, CHEN Xuebo. Improved ant colony algorithm and its application in obstacle avoidance for robot[J]. CAAI transactions on intelligent systems, 2015, 10(1):90-96
[8] 曹建秋, 张广言, 徐鹏. A*初始化的变异灰狼优化的无人机路径规划[J/OL]. 计算机工程与应用:1?12.[2021?06?16] http://kns.cnki.net/kcms/detail/11.2127.TP.20201225.0932.014.html.
CAO Jianqiu, ZHANG Guangyan, XU Peng. A* initialization of varitated gray wolf optimized UAV path planning[J/OL]. Computer engineering and applications:1?12.[2021?06?16] http://kns.cnki.net/kcms/detail/11.2127.TP.20201225.0932.014.html.
[9] 莫宏伟, 马靖雯. 基于蚁群算法的四旋翼航迹规划[J]. 智能系统学报, 2016, 11(2):216-225
MO Hongwei, MA Jingwen. Four-rotor route planning based on the ant colony algorithm[J]. CAAI transactions on intelligent systems, 2016, 11(2):216-225
[10] 魏永超, 邓岚, 李涛, 等. 采用改进细菌觅食优化算法的无人机航迹规划[J]. 电讯技术, 2021, 61(5):560-566
WEI Yongchao, DENG Lan, LI Tao, et al. UAV track planning with improved bacterial foraging optimization algorithm[J]. Telecommunication engineering, 2021, 61(5):560-566
[11] 赵红超, 周洪庆, 王书湖. 无人机三维航迹规划的量子粒子群优化算法[J]. 航天控制, 2021, 39(1):40-45
ZHAO Hongchao, ZHOU Hongqing, WANG Shuhu. Quantum particle swarm optimization algorithm of three-dimensional path planning of unmanned aerial vehicle[J]. Aerospace control, 2021, 39(1):40-45
[12] MONTGOMERY J, RANDALL M. Anti-pheromone as a tool for better exploration of search space[C]//Proceedings of the 3rd International Workshop on Ant Algorithms. Brussels, Belgium:Springer, 2002.
[13] 王祝,徐广通,龙腾.基于定制内点法的多无人机协同轨迹规划[J/OL].自动化学报:1?12[2021?07?12] https://doi.org/10.16383/j.aas.c200361.
WANG Zhu, XU Guangtong, LONG Teng. Multi-UAV collaborative trajectory planning based on the custom in-point method[J/OL]. Journal of automation:1?12[2021?07?12] https://doi.org/10.16383/j.aas.c200361.
[14] 吴莹莹, 丁肇红, 刘华平, 等. 面向环境探测的多智能体自组织目标搜索算法[J]. 智能系统学报, 2020, 15(2):289-295
WU Yingying, DING Zhaohong, LIU Huaping, et al. Self-organizing target search algorithm of multi-agent system for envi-ronment detection[J]. CAAI transactions on intelligent systems, 2020, 15(2):289-295
[15] 杜永浩, 邢立宁, 蔡昭权. 无人飞行器集群智能调度技术综述[J]. 自动化学报, 2020, 46(2):222-241
DU Yonghao, XING Lining, CAI Zhaoquan. Survey on intelligent scheduling technologies for unmanned flying craft clusters[J]. Acta automatica sinica, 2020, 46(2):222-241
[16] PURBOLINGGA Y, JAZIDIE A, EFFENDI R. Modified ant colony algorithm for swarm multi agent exploration on target searching in unknown environment[C]//Proceedings of 2019 International Conference of Artificial Intelligence and Information Technology. Yogyakarta, Indonesia:IEEE, 2019.
[17] LUO Fuyu, WANG Wei, LI Zhe. Multi-unmanned vehicle for region traversal search based on ant colony algorithm[C]//Proceedings of the 3rd International Symposium on Autonomous Systems. Shanghai, China, 2019:329?334.
[18] YUE Wei, XI Yun, GUAN Xianhe. A new searching approach using improved multi-ant colony scheme for multi-UAVs in unknown environments[J]. IEEE access, 2019, 7:161094-161102.
[19] BAJCSY R. Active perception[J]. Proceedings of the IEEE, 1988, 76(8):966-1005.
[20] AKKA K, KHABER F. Mobile robot path planning using an improved ant colony optimization[J]. International journal of advanced robotic systems, 2018, 15(3):1-7.
[21] 时浩, 田聪玲, 任意, 等. 基于稀疏A*算法的微小型固定翼无人机航迹规划[J]. 兵工自动化, 2021, 40(3):14-18, 39
SHI Hao, TIAN Congling, REN Yi, et al. Route planning of small fixed-wing UAV based on sparse A* algorithm[J]. Ordnance industry automation, 2021, 40(3):14-18, 39
[22] LUO Qiang, WANG Haibao, ZHENG Yan, et al. Research on path planning of mobile robot based on improved ant colony algorithm[J]. Neural computing and applications, 2020, 32(6):1555-1566.
[23] 夏小云, 周育人. 蚁群优化算法的理论研究进展[J]. 智能系统学报, 2016, 11(1):27-36
XIA Xiaoyun, ZHOU Yuren. Advances in theoretical research of ant colony optimization[J]. CAAI transactions on intelligent systems, 2016, 11(1):27-36
[24] 薛政钢. 基于多群体蚁群算法的多无人机协同搜索方法研究[D]. 开封:河南大学, 2018.
XUE Zhenggang. Research on Multi-UAV cooperative search methods based on multi-colony ant colony algorithm[D]. Kaifeng:Henan University, 2018.
[25] 刘刚, 裴红蕾. 复合形引导蜂群寻优的无人机航迹多目标规划[J]. 机械设计与制造, 2020(4):253-257
LIU Gang, PEI Honglei. Unmanned air vehicle route multi-object planning based on bee colony algorithm guided by complex form[J]. Machinery design & manufacture, 2020(4):253-257
Similar References:

Memo

-

Last Update: 2021-06-25

Copyright © CAAI Transactions on Intelligent Systems