Sensors: Sensing Rich Drive Trains for Modern Mechatronic Systems

传感器：传感现代机电系统的丰富传动系统

• 批准号: 0529451
• 负责人: Masayoshi Tomizuka
• 金额: --
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-08-15 至 2009-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0529451&HistoricalAwards=false
• 关键词: Sensors; Sensing; Rich; Drive; Trains

AbstractCMS-0529451PI: Masayoshi Tomizuka (University California, Berkeley)This research develops mechatronic approaches to the design of drive trains for mechanical systems based on heavy sensing. A typical drive train includes an actuator (electrical motor), a set of gears, bearings and an inertia load (link), which may be connected to another drive train or an end effecter. The technical objectives of the research are to 1) study sensing of various signals in the drive train, in particular, those not utilized in conventional practice such as acceleration signals, force signal in the drive train and end effector's position, 2) develop signal processing and decision making algorithms that take advantages of the sensed signals for betterment of performance and other mechatonic advantages, and 3) demonstrate these benefits experimentally. New signal processing and decision making algorithms will handle nonlinearities and uncertainties in drive train mechanisms as well as the environment in which drive trains and entire mechanical systems operate. This will bring advantages to both producers and end users of mechanical systems, e.g. robots, assembly machines and other manufacturing equipment. The broader impacts will be from education, outreach and international collaboration. The project team includes the Principal Investigator, graduate student researchers and undergraduate researchers. Undergraduate students will be involved in this project through participation in experiments and mechatronics design. This is a sensor award under the solicitation NSF 05-526.

AbstractCMS-0529451PI：Masayoshi Tomizuka（加利福尼亚大学，伯克利大学）这项研究开发了机器方法，用于设计基于重感应的机械系统的驱动列车。 典型的驱动列车包括一个执行器（电动机），一组齿轮，轴承和惯性负载（链接），它们可能连接到另一个驱动列车或最终效应器。研究的技术目标是1）研究在驱动列车中的各种信号的研究感知，特别是在常规实践中未使用的信号，例如加速度信号，驱动列车中的力信号和最终效应器的位置，2）开发信号处理和决策算法，这些算法可以使绩效和其他机械性较高的效果和其他这些效果进行效果和其他这些效果。新的信号处理和决策算法将处理驱动火车机制中的非线性和不确定性，以及驱动火车和整个机械系统运行的环境。 这将为机械系统的生产者和最终用户带来优势，例如机器人，装配机和其他制造设备。 更广泛的影响将来自教育，外展和国际合作。 项目团队包括主要研究人员，研究生研究人员和本科研究人员。本科生将通过参与实验和机电一体化设计参与该项目。 这是NSF 05-526征求的传感器奖。