家庭智能空间下多领域知识的共享与重用方法

doi:10.13973/j.cnki.robot.180494

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张营, 田国会, 张森彦, 李泚泚. 家庭智能空间下多领域知识的共享与重用方法[J]. 机器人, 2019, 41(4): 507-518.DOI: 10.13973/j.cnki.robot.180494. ZHANG Ying, TIAN Guohui, ZHANG Senyan, LI Cici. Multi-domain Knowledge Sharing and Reuse in Home Intelligent Space. ROBOT, 2019, 41(4): 507-518. DOI: 10.13973/j.cnki.robot.180494.

摘要为充分利用家庭环境中的多领域知识和提高服务质量，基于家庭智能空间提出了一种多领域知识的共享与重用方法．首先，根据先验知识构建基于本体的多领域知识模型，在此基础上，结合软件智能体实现知识的自动表征，解决数据的异构性问题和促进知识的共享与重用．然后根据用户的日常行为活动，提出一种基于多传感器和活动模板的行为活动理解方法，实现用户意图的识别和服务的推理．为降低计算成本和提高服务质量，通过重用行为活动领域知识实现服务执行序列的获取和动作的引导，并融合仿真系统生成的服务执行策略和物品领域共享的物品知识高效地执行机器人服务，从而提升服务执行的智能化水平．最后，实验结果表明了方法的有效性和可行性．

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	张营
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关键词 ：智能空间, 知识表征, 服务机器人, 仿真系统, 知识共享与重用, 活动识别

Abstract：In order to make full use of multi-domain knowledge and improve the quality of service in the home environment, a multi-domain knowledge sharing and reuse method is proposed based on the home intelligent space. Firstly, an ontology-based multi-domain knowledge model is built in the light of the prior knowledge. On this basis, the software agent is used to achieve knowledge representation automatically, addressing the problem of data heterogeneity and facilitating the knowledge sharing and reuse. Then, according to the user's activities of daily living, an activity understanding approach is presented based on multiple sensors and an activity template to realize the user intention recognition and the service inference. Moreover, the acquisition of service sequences and the guidance of actions can be achieved by reusing the domain knowledge of human activity, and thus the calculation costs are reduced and the quality of service is improved. And the robot services are effectively executed through integrating the service execution strategy generated by the simulation system and the shared object knowledge in object domain, so as to improve the intelligence level of service execution. Finally, the experimental results are provided to show the effectiveness and feasibility of the proposed method.

Key words： intelligent space knowledge representation service robot simulation system knowledge sharing and reuse activity recognition

收稿日期: 2018-08-03 录用日期: 2018-09-27

TP273

基金资助:国家自然科学基金（61773239，61573216）；山东省自然科学基金（ZR2015FM007）；山东省泰山学者工程

通讯作者: 田国会,g.h.tian@sdu.edu.cn E-mail: g.h.tian@sdu.edu.cn

作者简介: 张营(1991-),男,博士生.研究领域:环境建模,知识表征,服务机器人.
田国会(1969-),男,博士,教授.研究领域:服务机器人,智能空间,云机器人,类脑智能机器人.
张森彦(1995-),男,硕士生.研究领域:机器学习,强化学习,物品抓取.

链接本文:

https://robot.sia.cn/CN/10.13973/j.cnki.robot.180494 或 https://robot.sia.cn/CN/Y2019/V41/I4/507

[1] Stankovic J A. Research directions for the internet of things[J]. IEEE Internet of Things Journal, 2014, 1(1):3-9.
[2] Niitsuma M, Hashimoto H, Hashimoto H. Spatial memory as an aid system for human activity in intelligent space[J]. IEEE Transactions on Industrial Electronics, 2007, 54(2):1122-1131.
[3] 田国会,李晓磊,赵守鹏,等.家庭服务机器人智能空间技术研究与进展[J].山东大学学报(工学版),2007,37(5):53-59.Tian G H, Li X L, Zhao S P, et al. Research and development of intelligent space technology for a home service robot[J]. Journal of Shandong University (Engineering Science), 2007, 37(5):53-59.
[4] Lee J H, Hashimoto H. Intelligent space-Concept and contents[J]. Advanced Robotics, 2002, 16(3):265-280.
[5] Morioka K, Lee J H, Hashimoto H. Human-following mobile robot in a distributed intelligent sensor network[J]. IEEE Transactions on Industrial Electronics, 2004, 51(1):229-237.
[6] Das S K, Cook D J, Battacharya A, et al. The role of prediction algorithms in the MavHome smart home architecture[J]. IEEE Wireless Communications, 2002, 9(6):77-84.
[7] Bian Z P, Hou J, Chau L P, et al. Fall detection based on body part tracking using a depth camera[J]. IEEE Journal of Biomedical and Health Informatics, 2015, 19(2):430-439.
[8] Wen J H, Wang Z Y. Learning general model for activity recognition with limited labelled data[J]. Expert Systems with Applications, 2017, 74:19-28.
[9] Wang J, Zhang Z Q, Li B, et al. An enhanced fall detection system for elderly person monitoring using consumer home networks[J]. IEEE Transactions on Consumer Electronics, 2014, 60(1):23-29.
[10] Roy P C, Abidi S R, Abidi S S R. Possibilistic activity recognition with uncertain observations to support medication adherence in an assisted ambient living setting[J]. Knowledge-Based Systems, 2017, 133:156-173.
[11] Aicha A N, Englebienne G, Kröse B. Unsupervised visit detection in smart homes[J]. Pervasive and Mobile Computing, 2017, 34:157-167.
[12] Chen L M, Nugent C D, Okeyo G. An ontology-based hybrid approach to activity modeling for smart homes[J]. IEEE Transactions on Human-Machine Systems, 2014, 44(1):92-105.
[13] Rafferty J, Nugent C D, Liu J, et al. From activity recognition to intention recognition for assisted living within smart homes[J]. IEEE Transactions on Human-Machine Systems, 2017, 47(3):368-379.
[14] Bae I H. An ontology-based approach to ADL recognition insmart homes[J]. Future Generation Computer Systems, 2014, 33:32-41.
[15] Tenorth M, Klank U, Pangercic D, et al. Web-enabled robots[J]. IEEE Robotics & Automation Magazine, 2011, 18(2):58-68.
[16] Tenorth M, Beetz M. Representations for robot knowledge in the KnowRob framework[J]. Artificial Intelligence, 2017, 247:151-169.
[17] 路飞,田国会,李擎.智能空间环境下基于本体的机器人服务自主认知及规划[J].机器人,2017,39(4):423-430.Lu F, Tian G H, Li Q. Autonomous cognition and planning of robot service based on ontology in intelligent space environment[J]. Robot, 2017, 39(4):423-430.
[18] 李泚泚,田国会,张梦洋,等.基于本体的物品属性类人认知及推理[J].浙江大学学报(工学版),2018,52(7):1231-1238.Li C C, Tian G H, Zhang M Y, et al. Ontology-based humanoid cognition and reasoning of object attributes[J]. Journal of Zhejiang University (Engineering Science), 2018, 52(7):1231-1238.
[19] 路飞,姜媛,田国会.基于情感-时空信息的机器人服务自主认知及个性化选择[J].机器人,2018,40(4):448-456.Lu F, Jiang Y, Tian G H. Autonomous cognition and personalized selection of robot services based on emotion-space-time information[J]. Robot, 2018, 40(4):448-456.
[20] Hui T K L, Sherratt R S, Sanchez D D. Major requirements for building Smart Homes in Smart Cities based on Internet of Things technologies[J]. Future Generation Computer Systems, 2017, 76:358-369.
[21] Cai H S, Zhang X Z, Zhang Y H, et al. A case-based reasoning model for depression based on three-electrode EEG data[J]. IEEE Transactions on Affective Computing, 2018. DOI:10.1109/TAFFC.2018.2801289.
[22] 张梦洋,田国会,龚京,等基于本体知识库的服务决策机制的设计方法[J].华中科技大学学报(自然科学版),2017,45(10):70-74.Zhang M Y, Tian G H, Gong J, et al. Design method of service decision mechanism based on ontology knowledge base[J]. Journal of Huazhong University of Science and Technology (Nature Science Edition), 2017, 45(10):70-74.
[23] Huang B, Tian G H, Wu H, et al. A method of abnormal habits recognition in intelligent space[J]. Engineering Applications of Artificial Intelligence, 2014, 29:125-133.
[24] Tenorth M, Perzylo A C, Lafrenz R, et al. Representation and exchange of knowledge about actions, objects, and environments in the ROBOEARTH framework[J]. IEEE Transactions on Automation Science and Engineering, 2013, 10(3):643-651.
[25] Lu S Y, Zhang Y, Su J J. Mobile robot for power substation inspection:A survey[J]. IEEE/CAA Journal of Automatica Sinica, 2017. DOI:10.1109/JAS.2017.7510364.
[26] Boularias A, Bagnell J A, Stentz A. Learning to manipulate unknown objects in clutter by reinforcement[C]//Twenty-Ninth AAAI Conference on Artificial Intelligence. Palo Alto, USA:AAAI, 2015:1336-1342.