[1]楼传炜,葛泉波,刘华平,等.无人机群目标搜索的主动感知方法[J].智能系统学报,2021,16(3):575-583.[doi:10.11992/tis.202009012]
 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]
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无人机群目标搜索的主动感知方法(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第16卷
期数:
2021年3期
页码:
575-583
栏目:
吴文俊人工智能科学技术奖论坛
出版日期:
2021-05-05

文章信息/Info

Title:
Active perception method for UAV group target search
作者:
楼传炜1 葛泉波2 刘华平3 袁小虎4
1. 上海海事大学 物流工程学院,上海 201306;
2. 同济大学 电子与信息工程学院,上海 201804;
3. 清华大学 计算机科学与技术系,北京 100084;
4. 清华大学 自动化系,北京 100084
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
关键词:
无人机蚁群算法无目标先验条件具有探索偏好的搜索概率主动感知搜索框架未知区域运动方式选择机制环境信息
Keywords:
unmanned aerial vehicleant colonywithout prior information of the targetan unsearched probability with exploration preferenceactive perception search frameworkunknown regionmotion mode selection mechanismenvironmental information
分类号:
TP393
DOI:
10.11992/tis.202009012
摘要:
为提升蚁群搜索算法在规模大的栅格环境中对未知目标的搜索效率,提出基于蚁群算法的主动感知搜索框架。该框架通过应用历史环境信息来选择无人机的运动方式,并由无人机运动方式和感知域信息得到新的环境信息,从而实现无人机群的智能自动化搜索功能。新方法计算出一种具有探索偏好的未搜索概率,可使无人机搜索时偏向未搜索程度高的栅格,以此来提高算法的搜索能力。同时,以未搜索概率和信息素作为运动方式决策的依据来建立一种新的运动方式选择机制。该机制不仅考虑了目标可能出现的区域,又可兼顾未知区域,从而可实现无目标先验信息条件下的搜索过程。仿真结果表明,此算法在规模大的栅格环境中,与现有算法相比具有更高的搜索效率,并且得到的目标分布信息将更加全面。
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.

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备注/Memo

备注/Memo:
收稿日期:2020-09-10。
基金项目:国家自然科学基金项目(61773147,U1509203);浙江省自然科学基金项目(LR17F030005)
作者简介:楼传炜,硕士研究生,主要研究方向为多智能体系统;葛泉波,研究员,博士生导师,博士,主要研究方向为工程信息融合方法及应用、人机混合系统智能评估。发表学术论文100余篇;刘华平,副教授,博士生导师,国家杰出青年基金获得者、中国人工智能学会理事、中国人工智能学会认知系统与信息处理专业委员会秘书长,主要研究方向为机器人感知、学习与控制、多模态信息融合。主持国家自然科学基金重点项目2项。发表学术论文340余篇
通讯作者:刘华平.E-mail:hpliu@tsinghua.edu.cn
更新日期/Last Update: 2021-06-25