[1]王贺彬,葛泉波,刘华平,等.面向观测融合和吸引因子的多机器人主动SLAM[J].智能系统学报,2021,16(2):371-377.[doi:10.11992/tis.202006019]
 WANG Hebin,GE Quanbo,LIU Huaping,et al.Multi-robot active SLAM for observation fusion and attractor[J].CAAI Transactions on Intelligent Systems,2021,16(2):371-377.[doi:10.11992/tis.202006019]
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面向观测融合和吸引因子的多机器人主动SLAM(/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2期
页码:
371-377
栏目:
吴文俊人工智能科学技术奖论坛
出版日期:
2021-03-05

文章信息/Info

Title:
Multi-robot active SLAM for observation fusion and attractor
作者:
王贺彬1 葛泉波2 刘华平3 袁小虎4
1. 杭州电子科技大学 自动化学院,浙江 杭州 310018;
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):
WANG Hebin1 GE Quanbo2 LIU Huaping3 YUAN Xiaohu4
1. School of Automation, Hangzhou Dianzi University, Hangzhou 310018, 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:
主动同时定位与建图多机器人协作吸引因子凸组合融合扩展卡尔曼滤波器最优控制互信息多目标优化
Keywords:
active simultaneous location and mappingmulti-robot cooperationattractorconvex combination fusionextended Kalman filteroptimal controlmutual informationmulti-objective optimization
分类号:
TP510.80
DOI:
10.11992/tis.202006019
摘要:
针对未知环境下多机器人主动SLAM(simultaneous localization and mapping)存在不能完全遍历环境、定位精度不理想等问题,本文基于EKF-SLAM(extended Kalman filter-simultaneous localization and mapping)算法提出一种多机器人主动SLAM算法。通过引入吸引因子,增强多机器人系统之间的交流,提升机器人自身定位精度与环境建图精度,同时又引导多机器人团队进行探索环境。当同一地标被多个机器人观测到,采用凸组合融合方法融合各个机器人对地标的估计,从而降低被估计地标的不确定度。仿真结果表明,所提算法能够对环境进行覆盖遍历,提升对地标估计的定位精度。
Abstract:
Because multi-robot active SLAM cannot fully traverse an environment, and the localization accuracy is not ideal in an unknown environment, a new multi-robot active SLAM algorithm is proposed in this paper. By introducing attractors to enhance communication between multi-robot systems, the accuracy of robot localization and mapping is enhanced, and multi-robot teams are guided to explore the environment. When the same landmark is observed by multiple robots, convex combination fusion is used to fuse the estimate of the landmark by each robot, thereby reducing the uncertainty of the landmark. The simulation results show that the proposed algorithm can cover and traverse the environment and improve the localization accuracy of landmark estimation.

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备注/Memo

备注/Memo:
收稿日期:2020-06-12。
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:王贺彬,硕士研究生,主要研究方向为多智能体控制、非线性非高斯状态估计;葛泉波,研究员,博士生导师,主要研究方向为自适应Kalman滤波、工程化智能Kalman滤波方法、目标跟踪融合理论和能源互联网大数据分析。发表学术论文80余篇;刘华平,副教授,博士生导师,主要研究方向为机器人感知、学习与控制、多模态信息融合。国家杰出青年基金获得者,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: 2021-04-25