题目：New Insights into Autonomous Unmanned Vehicles and Embedded Systems: Convex Optimization and Soft Computing Methods
报告人：Chaomin Luo教授，University of Detroit Mercy, Michigan, USA
时间：2015.6.25, 9:30
地点：信息工程学院329室
Chaomin Luo（雒超民），本科毕业于东南大学无线电工程系，2002年在加拿大圭尔夫（Guelph）大学获得电子工程专业硕士学位。2008年在加拿大滑铁卢大学（Waterloo）电气与计算机工程系获得博士学位。2008年博士毕业后在国立台北大学电机工程研究所任助理教授及电机与资讯工程学院院长助理，目前为美国底特律大学电气与计算机工程系高级移动机器人实验室任终身教授。
主要从事人工智能及其应用，嵌入式系统，智能机器人，信息融合及超大规模集成电路的优化设计等方面的研究。在世界上首次成功将半正定规划，凸规划和基于二阶锥规划等优化算法用于超大规模集成电路设计，也是国际上首次采用基于生物激励的神经网络算法和模型用于智能机器人的研究者（如智能清扫移动机器人和智能探矿机器人）。
历任IEEE美国东南密歇根人工智能分会主席，副主席等职。雒博士现任IEEE美国西南密歇根州教育专家委员会主席，IEEE美国东南密歇根人工智能分会主席，和IEEE美国东南密歇根机器人和自动化分会副主席等学术职位。国际杂志《International Journal of Robotics and Automation》等4个期刊编委。报告内容简介：
Nowadays, optimization and soft computing methodologies play increasingly important role on electrical engineering. In this research, convex optimization and soft computing methods are applied for VLSI design and intelligent vehicle navigation, control and mapping.
A sequence of novel neural dynamics, genetic algorithms (GAs), and fuzzy logic (FL) approaches associated with developed, numerical method, spline-based and vector-driven intelligent vehicle navigation model are proposed. The biologically-motivated neural networks (BNN) algorithms are employed to guide an intelligent vehicle to reach goal with obstacle avoidance motivated by a biological neural system. A spline-based smooth guidance paradigm is developed for guidance of the vehicle locally so as to plan more reasonable and smoother trajectories with GAs and FL methodologies. BNN based scheme demonstrates that the algorithms avoid the issue of local minima in path planning of an intelligent vehicle. Simulation, comparison studies and experimental results of intelligent vehicle navigation demonstrate the effectiveness, efficiency and robustness of the proposed methodologies.
