CISE Research Instrumentation: High-Performance Vehicle with Visual and Deictic Control

CISE 研究仪器：具有视觉和指示控制的高性能车辆

• 批准号: 9421532
• 负责人: Randal Nelson
• 金额: $ 1.71万
• 依托单位: University of Rochester
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-05-01 至 1996-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9421532&HistoricalAwards=false
• 关键词: CISE; Research; Instrumentation; Performance; Vehicle

9421532 Nelson This award is to support the research effort for the University of Rochester's Computer Science and Mechanical Engineering Departments to build and control a pair of small, off-road vehicles; one human-piloted, and one computer-controlled. Intrinsically interesting aspects of the equipment are low cost, off-road capability, and relatively high speeds (50 kph). The two vehicles will support research on semi-autonomous control, real-time visual processing, real-time decision-making, control learning, and simulation. The research issues center around semi-autonomous or "deictic" control in which visual or symbolic commands are passed to the computer-controlled vehicle for local interpretation. This control style is increasingly popular for difficult, real-time control tasks (e.g. telemanipulation) in which neither full autonomy, nor direct teleoperation is feasible. Rochester will continue working on this topic. Deictic control does not solve all control problems: control learning can play an important role. Two aspects of control learning will be investigated: learning tunable "motor skills" necessary to cope with high-speed situations, and, at a higher level, learning sequences of appropriate actions. Both types of learning are already under investigation at Rochester. Today's learning algorithms often receive initial training in simulation, so sophisticated simulation work already underway will be used. ***

9421532 Nelson 该奖项旨在支持罗切斯特大学计算机科学和机械工程系的研究工作，以建造和控制一对小型越野车辆；一架由人类驾驶，一架由计算机控制。 该设备本质上有趣的方面是低成本、越野能力和相对较高的速度（50 公里/小时）。 这两辆车将支持半自主控制、实时视觉处理、实时决策、控制学习和仿真方面的研究。 研究问题围绕半自主或“指示”控制，其中视觉或符号命令被传递到计算机控制的车辆进行本地解释。 这种控制方式在困难的实时控制任务（例如远程操作）中越来越受欢迎，在这些任务中，完全自主或直接远程操作都不可行。 罗切斯特将继续研究这个主题。 指示控制并不能解决所有控制问题：控制学习可以发挥重要作用。 将研究控制学习的两个方面：学习应对高速情况所必需的可调节“运动技能”，以及在更高层次上学习适当动作的序列。 罗彻斯特已经对这两种类型的学习进行了研究。 当今的学习算法通常会接受模拟方面的初始训练，因此将使用已经在进行的复杂模拟工作。 ***