題目：Bio-inspired Approaches for Real-Time Navigation of Mobile Robots in Unknown Environments
報(bào)告人： Simon X. Yang (楊先一) 教授，加拿大Guelph大學(xué)
時(shí)間：2014年6月16日 9:00
地點(diǎn)：信息工程學(xué)院（新校）信息樓323
Simon X. Yang(楊先一)，本科畢業(yè)于北京大學(xué)、碩士畢業(yè)于美國休斯頓大學(xué)（University of Huston）、博士畢業(yè)于加拿大阿爾伯塔大學(xué)(University of Alberta)。現(xiàn)為加拿大Guelph大學(xué)高級機(jī)器人與智能系統(tǒng)實(shí)驗(yàn)室主任，終身教授，博士生導(dǎo)師。
主要研究領(lǐng)域：移動機(jī)器人路徑規(guī)劃與控制、多傳感器信息融合、無線傳感器網(wǎng)絡(luò)、智能計(jì)算與優(yōu)化、多機(jī)器人系統(tǒng)等。國際雜志《IEEE Transactions On Neural Networks》、《IEEE Transactions On Systems, Man, And Cybernetics, Part B》、《International Journal of Robotics and Automation》、《Control and Intelligent Systems》副主編; 國際雜志《International Journal of Computational Intelligence and Applications》、《International Journal of Automation and Systems Engineering》、《Journal of Robotics》、《International Journal of Computing and Information Technology》、《International Journal of Information Acquisition》編委.
報(bào)告內(nèi)容簡介：
Studyies of biologically inspired intelligent systems have been made significant progress in both understanding the biological intelligence and applying to various artificial engineering systems. In this talk, two algorithms for real-time navigation of mobile robots in unknown environments is presented. The first approach integrates a novel learning algorithm de-rived from Skinner’s operant conditioning and a shunting neural dynamics model, producing the capability of path planning in unknown and cluttered environments, after training and assistance with an angular velocity map. Second, a fuzzy logic based bio-inspired system is developed for mobile robot navigation. Based on a modified Braitenberg’s automata model, a bio-inspired hybrid fuzzy neural network structure is designed to control the robot, where the neural network weights are obtained from the fuzzy system. The effectiveness of both proposed methods are validated by simulation studies. In comparison to the Chang-Gaudiano algorithm under the same conditions, the proposed bio-inspired algorithm not only allows the robot to navigate efficiently in cluttered environments, but also significantly improves the computational and training time. This bio-inspired algorithm was successfully implemented on a real mobile robot for indoor obstacle avoidance.
