Autonomous Cognition and Planning of Robot Service Based on Ontology in Intelligent Space Environment

被引：0
|
作者：
Lu F. [1 ]
Tian G. [1 ]
Li Q. [1 ]
机构：
[1] School of Control Science and Engineering, Shandong University, Jinan
来源：
Jiqiren/Robot | 2017年 / 39卷 / 04期
关键词：
Intelligent space; Ontology technology; Service cognition; Service robot; Task planning;
D O I：
10.13973/j.cnki.robot.2017.0423
中图分类号：
学科分类号：
摘要：
In order to improve the autonomy of robot service, an autonomous ontology-based cognition and planning method for robot service in dynamic home environments is presented. Firstly, the ontology model of the intelligent space is established based on ontology technology. And the user-centered adaptive data-concept conversion mechanism is established after the semantic rules are set up, to integrate the information of intelligent space. Based on these, the inference rule base for service task can be constructed to realize the expansion of the ontology model, and the intelligent space ontology updated in real time is matched with the rule base in order to generate the service sequence of the robot. Finally, the idea of hierarchical task network is used to realize the task planning in JSHOP2 planner. Results of the task planning experiment in intelligent space environment show that the service robot can realize the autonomous cognition in the service task according to the environment and the user's information, and provide personalized services for the user actively. The intelligence level of service can be significantly improved. © 2017, Science Press. All right reserved.
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页码：423 / 430
页数：7
相关论文
共 16 条
  • [1] Duncan K., Sarkar S., Alqasemi R., Et al., Scene-dependent intention recognition for task communication with reduced humanrobot interaction, Lecture Notes in Computer Science, 8927, pp. 730-745, (2014)
  • [2] Lu F., Li J., Tian G.H., Et al., An automatic approach for service composition of service robots based on parameter-driven mechanism, High Technology Letters, 24, 2, pp. 157-164, (2014)
  • [3] Horridge M., Bechhofer S., The OWL API: A Java API for OWL ontologies, Semantic Web, 2, 1, pp. 11-21, (2011)
  • [4] Zong X.Q., Fu H.G., Xia H.D., Et al., A hybrid cognitive assessment based on ontology knowledge map and skills, Knowledge-Based Systems, 73, pp. 52-60, (2014)
  • [5] Wang X.H., Dong J.S., Chin C.Y., Et al., Semantic space: An infrastructure for smart spaces, IEEE Pervasive Computing, 3, 3, pp. 32-39, (2004)
  • [6] Qin W.J., Ontology-based context model smart space, (2005)
  • [7] Yu J.S., Wu H., Tian G.H., Et al., Semantic database design and semantic map construction of robots based on the cloud, Robot, 38, 4, pp. 410-419, (2016)
  • [8] Zhu B., Dai X.Z., Li X.D., Et al., Experimental study on open interior-places perception of robot based on prototype, Robot, 35, 4, pp. 491-499, (2013)
  • [9] Hu M.D., Research on key technology of smart home based on ontology, (2014)
  • [10] Tenorth M., Perzylo A.C., Lafrenz R., Et al., Representation and exchange of knowledge about actions, objects, and environments in the RoboEarth framework, IEEE Transactions on Automation Science and Engineering, 10, 3, pp. 643-651, (2013)
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