Behavior modeling of transport robot using Petri nets

doi:10.11772/j.issn.1001-9081.2014.05.1360

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (5): 1360-1363.DOI: 10.11772/j.issn.1001-9081.2014.05.1360

• Artificial intelligence • Previous Articles Next Articles

Behavior modeling of transport robot using Petri nets

YUAN Jie¹,LI Wei²

1. School of Electrical Engineering, Xinjiang University, Urumqi Xinjiang 830047, China;
2. Department of Computer and Electrical Engineering, California State University, Bakersfield, CA 93311, USA

Received:2013-11-01 Revised:2013-12-25 Online:2014-05-30 Published:2014-05-01
Contact: YUAN Jie

运输机器人行为建模的Petri网方法

袁杰¹,李伟²

1. 新疆大学电气工程学院，乌鲁木齐 830047；
2. 美国加州州立大学贝克斯菲尔德分校计算机与电气工程系美国加州贝克斯菲尔德 93311

通讯作者: 袁杰
作者简介:袁杰（1975-），男，新疆乌鲁木齐人，副教授，博士，主要研究方向：智能控制、机器学习；李伟（1957-），男，美国加州人，教授，博士，主要研究方向：智能系统设计、化学羽流追踪、脑机接口仿人机器人控制系统。
基金资助:
国家自然科学基金资助项目;国家留学基金资助项目

Abstract

Abstract:

New difficulties are met when establishing accurate behavioral models of a transport robot. To solve this problem, behavioral models of a transport robot were built using Petri Nets (PN) with inhibitor arcs. There exist coupling, constraint, and asynchronization relationships among the behaviors of a transport robot. A Petri net metamodel with inhibitor arcs of interactive behaviors as well as a token flow control mechanism were utilized for modeling the behaviors of a transport robot. The Petri net models were converted into LabVIEW programs using LabVIEW2012 and the Robotics module. The robot behaviors were verified using a transport robot platform. The experimental results demonstrate that the transport robot's behaviors and interaction logic are achieved, and that the robot has behavioral identification, decision-making and implementation capabilities, and it is a suitable method model the behaviors of a transport robot using Petri nets with inhibitor arcs. The reference models of Petri nets are given for designing related behaviors of transport robots.

摘要：

针对建立运输机器人的精准行为模型所遇到的新困难，提出采用含禁止弧Petri网（PN）建立其行为模型。运输机器人行为有耦合、制约、异步等特点，采用含禁止弧的行为交互Petri网元模型以及token流动控制机制建模其行为。通过LabVIEW2012及Robotics模块,将Petri网模型转化为LabVIEW程序，在运输机器人平台进行行为验证。结果实现了运输机器人的行为与交互逻辑，具有行为辨识、决策与执行能力。验证了含禁止弧Petri网为运输机器人的行为建模提供了一种适用方法，建立的Petri网模型为运输机器人的相关行为设计提供模型参考。

CLC Number:

TP391.4

YUAN Jie LI Wei. Behavior modeling of transport robot using Petri nets[J]. Journal of Computer Applications, 2014, 34(5): 1360-1363.

袁杰李伟. 运输机器人行为建模的Petri网方法[J]. 计算机应用, 2014, 34(5): 1360-1363.

References

［1］HURINK J, KNUST S. Tabu search algorithms for job-shop problems with a single transport robot ［J］. European Journal of Operational Research, 2005,162(1): 99-111.
［2］LACOMME P, TCHERNEV N. Resolution of a job-shop problem with a single transport robot and buffer facilities ［C］// Proceedings of the 2006 International Conference on Service Systems and Service Management. Piscataway: IEEE Press, 2006:1108-1113.
［3］TAKAHASHI M, SUZUKI T, SHITAMOTO H, et al. Developing a mobile robot for transport applications in the hospital domain ［J］. Robotics and Autonomous Systems, 2010,58(7):889-899.
［4］DALLAL A H, DERBALA A S, TAHER M F. A mobile robot for hospitals controlled using WLAN ［C］// Proceedings of the 2012 Cairo International Biomedical Engineering Conference. Piscataway: IEEE Press, 2012:100-103.
［5］ZALAMA E, GOMEZ J, PAUL M, et al. Adaptive behavior navigation of a mobile robot ［J］. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 2002,32(1):160-169.
［6］FOX M, GHALLAB M, INFANTES G, et al. Robot introspection through learned hidden Markov models ［J］. Artificial Intelligence, 2006,170(2):59-113.
［7］PARHI D R, PRADHAN S K, PANDA A K, et al. The stable and precise motion control for multiple mobile robots ［J］. Applied Soft Computing, 2009,9(2):477-487.
［8］NAKANO M, HASEGAWA Y, FUNAKOSHI K, et al. A multi-expert model for dialogue and behavior control of conversational robots and Agents ［J］. Knowledge-based Systems, 2011,24(2):248-256.
［9］FAN G, YU H, CHEN L, et al. Fault diagnosis and handling for service composition based on Petri nets ［J］. Journal of Software, 2010,21(2):231-247.(范贵生,虞慧群,陈丽琼,等.基于Petri网的服务组合故障诊断与处理［J］.软件学报,2010,21(2):231-247.)
［10］LI J, FAN Y. Petri net based workflow model performance analysis method ［J］. Journal of Computer Applications, 2001,21(6):4-6.(李建强,范玉顺.基于Petri网的工作流模型性能分析方法［J］.计算机应用,2001,21(6):4-6.)
［11］HU H, ZHOU M, LI Z, et al. Deadlock-free control of automated manufacturing systems with flexible routes and assembly operations using Petri nets ［J］. IEEE Transactions on Industrial Informatics, 2013,9(1):109-121.
［12］JIANG Z. Petri nets and applications for manufacturing system modeling and control ［M］. Beijing: China Machine Press, 2004:39-44.(江志斌.Petri网及其在制造系统建模与控制中的应用［M］.北京: 机械工业出版社，2004:39-44.)

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Behavior modeling of transport robot using Petri nets

运输机器人行为建模的Petri网方法

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