[1]张威,葛泉波,刘华平,等.室外未知环境下的AGV地貌主动探索感知[J].智能系统学报,2021,16(1):152-161.[doi:10.11992/tis.202007025]
 ZHANG Wei,GE Quanbo,LIU Huaping,et al.AGV active landform exploration and perception in an unknown outdoor environment[J].CAAI Transactions on Intelligent Systems,2021,16(1):152-161.[doi:10.11992/tis.202007025]
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室外未知环境下的AGV地貌主动探索感知(/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1期
页码:
152-161
栏目:
吴文俊人工智能科学技术奖论坛
出版日期:
2021-01-05

文章信息/Info

Title:
AGV active landform exploration and perception in an unknown outdoor environment
作者:
张威1 葛泉波2 刘华平3 孙富春3
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
Author(s):
ZHANG Wei1 GE Quanbo2 LIU Huaping3 SUN Fuchun3
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 Unive
关键词:
移动机器人运动方式贝叶斯框架主动感知被动感知地貌识别振动数据室外地貌
Keywords:
mobile robotmovement methodsbayesian frameworkactive perceptionpassive perceptiongeomorphic recognitionvibration dataoutdoor geomorphology
分类号:
TP391
DOI:
10.11992/tis.202007025
摘要:
智能机器人对复杂地貌环境的识别一直是机器人应用领域研究的前沿问题,移动机器人在不同的地貌上采取的运动方式并非一成不变,所以选择的运动方式对于迅速准确识别所处地貌的类型至关重要。针对该问题本文提出了一种基于贝叶斯框架的主动感知探索方法,使移动机器人能够主动探索有兴趣的运动方式并且感知识别和运动之间的匹配关系,可以优化在地貌识别之中的模糊不确定性;为了进一步验证实验的可靠性,还使用了被动感知策略来比较和分析不同策略之间的差异。实验结果表明:主动感知方法能够规划出有效的地貌识别动作序列,能够引导移动机器人主动感知目标地貌,该框架对于室外未知环境下主动感知后的地貌识别效果优于被动感知。
Abstract:
The recognition of a complex landform environment by an intelligent robot has been the frontier problem of research in the field of robotics applications. The motion modes adopted by mobile robots differ between landforms, so the selected motion mode is crucial for quickly and accurately identifying the type of landform. To solve this problem, an active perception exploration method is proposed in this paper based on a Bayesian framework. It enables mobile robots to actively explore interesting motion modes and recognize the matching relationship between landform and movement. It can optimize the fuzzy uncertainty in landform recognition. To further verify the reliability of the experiment, we also use a passive recognition strategy to compare and analyze the differences between different strategies. The experimental results show that the active perception method can plan effective landform recognition action sequences and guide mobile robots to actively perceive the target landform. The landform recognition effect of active perception is better than that of passive perception in an unknown outdoor environment.

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备注/Memo

备注/Memo:
收稿日期:2020-07-12。
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:张威,硕士研究生,主要研究方向为移动机器人控制、感知与学习;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。;孙富春,教授,博士生导师,中国人工智能学会副理事长,主要研究方向为智能控制与机器人、多模态数据感知、模式识别。国家杰出青年基金获得者,IEEE Fellow,国家863计划专家组成员,荣获吴文俊科学技术奖创新奖一等奖、吴文俊科学技术奖进步奖一等奖。发表学术论文200余篇,出版专著3部、译书1部出版专著3部,译书1部
通讯作者:刘华平. E-mail:hpliu@tsinghua.edu.cn
更新日期/Last Update: 2021-02-25