浙江大学控制科学与工程学院陈剑教授应邀来校讲学
发布日期：2019-05-27
浏览次数：
学术讲座
报告题目：
智能车和机器人的感知与控制
报
告
人：陈剑，
浙江大学控制科学与工程学院教授
报告时间：
2019
年
5
月
29
日
下午
4
点
30
报告地点：信息楼
417
学术会议室
报告人介绍：
浙江大学控制科学与工程学院教授，博导，青年千人计划，浙江省特聘专家, IEEE Senior Member,中国自动化学会控制理论专委会委员、新能源控制学组主任，浙江省氢电混合动力系统创新团队负责人。主持和参与自然基金重点项目各一项。主要研究方向包括燃料电池系统控制、机器视觉、智能车、电池管理系统。出版英文学术专著一部，发表了110篇SCI/EI学术论文
。
报告摘要：
Abstract
: Computer vision provides general and abundant information for the environment and task description. Multiple view geometry can be used for the unified geometric modeling of visual perception and control tasks. In this talk, visual perception and control results of intelligent vehicles and robotics will be presented.
Visual perception provides the necessary feedback, such as the vehicle’s motion states and drivable road regions, for control systems. Since the 3D information might be lost and image noises exist in the imaging process, the effective pose estimation and motion identification of vehicles are challenging. Besides, intelligent vehicles are generally involved in complex scenarios. Therefore, it is difficult to robustly detect the drivable road space for safe vehicle maneuvers. Optimization and observer theories are applied to reconstruct the geometric information of the scene based on multiple view geometry. Then, real-time vehicle states and drivable road region can be identified effectively based on the reconstructed geometric information.
Visual control exploits the visual information for task descriptions and for controlling intelligent vehicles and robotics through appropriate visual feedback control laws. Since the depth information is lost in the imaging process of monocular cameras, there exist model uncertainties for the controller design. Moreover, the limited field of view of the camera and the physical non-holonomic constraints of intelligent vehicles also have great influences on the stability and robustness of the closed-loop system. Multiple view geometry is used for the geometric modeling and scaled pose estimation. Then, Lyapunov methods are applied to design stabilizing control laws in the presence of model uncertainties and multiple constraints.
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