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【讲座】Planning Mobile Robots using Petri net models

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时间：2016-06-02来源：作者：admin 点击数：

报告(课程)名称：
Title: Planning Mobile Robots using Petri net models
时间：
6月3号上午：10：30--11：30
地点：南校区大学生活动中心511

报告（授课）人：
Cristian Mahulea

报告（授课）人介绍：

Biography: Cristian Mahulea is currently an Assistant Professor at the Department of Computer Science and Systems Engineering of the University of Zaragoza, Spain. He received the B.S. and M.Sc. degrees in control engineering from the Technical University of Iasi, Romania, in 2001 and 2002, respectively, and the Ph.D. degree in systems engineering from the University of Zaragoza, Spain, in 2007.
His research interests include discrete event systems, hybrid systems, automated manufacturing, Petri nets, mobile robotics and healthcare systems. He has participated in the development and implementation of Petri Net Toolbox and SimHPN, two MATLAB software for simulation, analysis and synthesis of discrete-event systems modeled with Petri nets. He co-authored the book Applications of Petri Nets in Studying Discrete Event Systems in 2002. Cristian Mahulea participated in the development of RMTool, a collection of tools for modeling, path planning and motion control of mobile robots.
He has been a Visiting Professor at the University of Cagliari, Italy, and a Visiting Researcher at the University of Sheffield, U.K., and Boston University, MA, USA. Currently he is an Associate Editor of IEEE Transactions on Automation Science and Engineering journal
报告（课程）摘要（ABSTRACT）：

Abstract: Automatic strategies to control a team of identical robots that should satisfy a given task expressed as Boolean-based or syntactically co-safe Linear Temporal Logic (LTL) formulas over a set of regions of interest are presented. The team of robots is modeled by using a Petri net system with outputs (observations), thus avoiding the use of synchronized product of automatons. In the case of Boolean-based missions, the imposed specification is translated to a set of linear restrictions for some binary variables, the robot movement capabilities are formulated as linear constraints on PN markings, and the evaluations of the binary variables are linked with PN markings via linear inequalities. This allows us to solve a Mixed Integer Linear Programming problem whose solution yields robotic trajectories satisfying the task. For co-safe LTL formulas, the strategy starts by translating the task into a Buchi automaton and by choosing a finite string satisfying the formula. Three Integer Linear Programming (ILP) formulations are used to find in the Petri net model a sequence of reachable markings such that the generated observations produce the chosen string of the Buchi automaton. If the string is not correctly followed, another one is found and the procedure is reiterated.

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