题目：Bio-inspired Approaches for Real-Time Navigation of
Mobile Robots in Unknown Environments
报告人： Simon X. Yang (杨先一) 教授，加拿大Guelph大学
时间：2014.6.16, 上午9:00
地点：信息工程学院（新校）信息楼323
Simon X. Yang(杨先一)，本科毕业于北京大学、硕士毕业于美国休斯顿大学（University of Huston）、博士毕业于加拿大阿尔伯塔大学(University of Alberta)。现为加拿大Guelph大学高级机器人与智能系统实验室主任，终身教授，博士生导师。
主要研究领域：移动机器人路径规划与控制、多传感器信息融合、无线传感器网络、智能计算与优化、多机器人系统等。国际杂志《IEEE Transactions On Neural Networks》、《IEEE Transactions On Systems, Man, And Cybernetics, Part B》、《International Journal of Robotics and Automation》、《Control and Intelligent Systems》副主编; 国际杂志《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》编委.
报告内容简介：
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.
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