公司简介
电子与计算机工程系机器人实验室的研究重点是应用快速联想记忆和其他神经网络学习技术(如CMAC神经网络)解决控制、模式识别和信号处理等问题。其基本概念是设计通过实践提高自身性能的硬件/软件系统。具体研究的细节可以在发表的论文和研究生论文中找到。机器人实验室目前有六个实验环境用于学习控制的研究。第一种包括通用电气的P-5五轴铰接工业机械臂，它是我们很多实时实验的基础。这个手臂已经被用于学习高速动力学和学习低速手眼协调(使用视频反馈)的研究。第二个实验准备包括两个Scorbot-ER V桌面机器人操作器，用于包括路径规划、多臂合作和工作空间障碍物回避的实验。该实验包括一个真正的双目视觉系统，该系统可以使用具有六自由度的第三工作台机械臂主动定位和定向。第三个主要的实验准备包括一个10个自由度的双足行走结构，带有力感应脚和一个用于感知平衡的两轴加速度计。第四个实验准备包括一个二十自由度的四足行走结构，同样具有力感应脚和一个用于感知平衡的两轴加速度计。 The fifth experiment involves a wheeled mobile robot with an array of ultrasonic range finders for studies of adaptive navigation and map building. Finally, the six experiment involves using neural network learning in the myoelectric control path of a Liberty Technology Boston Elbow. Computing in the laboratory is performed primarily using several 80486, Pentium and Pentium-Pro (P6) based engineering workstations, two massively parallel SIMD processors, INMOS transputer based multi-processing systems, and special purpose neural network hardware (developed at UNH). These systems support real time control experiments, simulation studies, general purpose graphics and document preparation. The laboratory also maintains equipment and tools for electronic hardware development and testing (oscilloscopes, signal generators, power supplies, etc.). Vibration Control by Gordon Kraft Nearly everyone has experienced the annoyance of a long drive with an unbalanced tire, or the whir of a noisy hard drive, or seen the blur in a picture taken from a camera that moved as the shutter closed. If you saw the movie \"Hunt for Red October\", you know how important submarine underwater vibrations are to the Navy. The Hubbell telescope cannot function if the supporting platform in space is moving. Factory workers are less efficient if they feel machinery vibrations for long periods of time. All of these are examples of unwanted vibrations. Control of these unwanted vibrations is a very important problem. Most vibration control systems are passive. The rubber in your car engine mounts or in air conditioning ducts are examples. These are called vibration absorbers and by far most vibration reduction systems use these passive elements. In some systems it\'s important to reduce the vibrations beyond the capability of the passive systems. In these cases, an active feedback control loop is required. Simply stated, the vibration measurements taken from various types of acceleration sensors are processed and then applied to dynamic actuators to apply forces that oppose the vibrations. These systems are usually subject to \"ad hoc\" adjustments to tune the feedback controller for the particular application. Once the system is tuned the performance of the system remains fixed. That is, it never gets any better as time goes on. In 1997 we received an NSF grant for $365,000.00 to apply a neural network called CMAC to the area of vibration control. The NSF grant is from the Knowledge Modeling and Computational Intelligence Division of NSF (director is Dr. Paul Werbos). The UNH Robotics Lab version of CMAC has been very successful at other types of control systems such as robotics and signal processing applications. It has advantages over other neural networks such as reduced memory requirements, faster training times and faster real-time control cycles. The key point is that, with CMAC in the control loop, the control system performance will continue to improve with time. As the network accumulates more experience about the system, it is able to continuously improve the control signal to reduce the vibrations more effectively. The network is capable of working with linear or non-linear systems and adjusts itself to changing parameters in the system.
