{"title":"A Petri net modelling of an adaptive learning control applied to an electric wheelchair","authors":"A. Abellard, M. Khelifa, M. Bouchouicha","doi":"10.1109/CIRA.2005.1554309","DOIUrl":null,"url":null,"abstract":"The aim of this paper is to present a Petri net based approach of a codesign methodology, in order to obtain optimized hardware/software solutions. An example of application is given for a neural control lever of a wheelchair. The research of solutions to the problem of hardware/software codesign, is a major task in the definition of a unique, structured and automatic methodology, providing the acceleration of design process and the dynamic evaluation of different compromises. data flow Petri nets are an efficient solution to make it possible and the use of a hardware description language, allows their implementation on programmable chips. The example described in this paper deals with a wheelchair whose commands can adapt to handicap. For some handicapped people, the use of a wheelchair can be difficult, due to weak physical capacities or cognitive troubles. So, the human-machine interface must be modular, configurable and easy to implement. It must bring reliability and use non specific material as often as possible. Therefore, the FRACAH project (Fauteuil Roulant A Commande Adaptee au Handicap) has been developed in order to proceed to evaluations. For example, its lever is handled by an artificial neural network that records functional limitations of the hand, and then compensates them. Some experiments have been done with hand disabled students.","PeriodicalId":162553,"journal":{"name":"2005 International Symposium on Computational Intelligence in Robotics and Automation","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2005 International Symposium on Computational Intelligence in Robotics and Automation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIRA.2005.1554309","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
The aim of this paper is to present a Petri net based approach of a codesign methodology, in order to obtain optimized hardware/software solutions. An example of application is given for a neural control lever of a wheelchair. The research of solutions to the problem of hardware/software codesign, is a major task in the definition of a unique, structured and automatic methodology, providing the acceleration of design process and the dynamic evaluation of different compromises. data flow Petri nets are an efficient solution to make it possible and the use of a hardware description language, allows their implementation on programmable chips. The example described in this paper deals with a wheelchair whose commands can adapt to handicap. For some handicapped people, the use of a wheelchair can be difficult, due to weak physical capacities or cognitive troubles. So, the human-machine interface must be modular, configurable and easy to implement. It must bring reliability and use non specific material as often as possible. Therefore, the FRACAH project (Fauteuil Roulant A Commande Adaptee au Handicap) has been developed in order to proceed to evaluations. For example, its lever is handled by an artificial neural network that records functional limitations of the hand, and then compensates them. Some experiments have been done with hand disabled students.
本文的目的是提出一种基于Petri网的协同设计方法,以获得优化的硬件/软件解决方案。给出了一种轮椅神经控制杆的应用实例。研究硬件/软件协同设计问题的解决方案,是定义一种独特的、结构化的和自动化的方法,提供设计过程的加速和不同妥协的动态评估的主要任务。数据流Petri网是一种有效的解决方案,使其成为可能,并使用硬件描述语言,允许其在可编程芯片上实现。本文所描述的例子是一种指令可以适应残疾的轮椅。对于一些残疾人来说,由于身体能力弱或认知障碍,使用轮椅可能很困难。因此,人机界面必须是模块化的、可配置的、易于实现的。它必须带来可靠性,并尽可能多地使用非特定材料。因此,FRACAH项目(Fauteuil Roulant A command Adaptee au Handicap)已经开发出来,以便进行评估。例如,它的杠杆由一个人工神经网络控制,该网络记录手的功能限制,然后对它们进行补偿。一些针对手部残疾学生的实验已经完成。