【導(dǎo)讀】文中設(shè)計了一種六自由度機械手。該機械手主要由底座，腰部，主板，大手。臂，小手臂，手腕，夾爪組成，采用步進電機驅(qū)動，單片機控制。人手臂的大小相當。此手臂的空間活動半徑，定位精度為5mm.它能夠抓取重量較輕的物體，并放到預(yù)定位置。該機械手有過載保護以及斷電空。間位置的自鎖功能.可以用于教學(xué)演示，或者在有放射性的環(huán)境中完成特定工作。蝸輪蝸桿及帶傳動比的確定以及部分重要零件的設(shè)計。料、工件、零件或工具等)傳送或操作作業(yè)的機械裝置，

　　

【正文】 ing tasks such as grasping of parts on a moving conveyor system, assembly of articulated parts, or for grasping from a mobile robotic system. Coordination between an anism39。s sensing modalities and motor control system is a hallmark of intelligent behavior, and we are pursuing the goal of building an integrated sensing and actuation system that can operate in dynamic as opposed to static environments. The system we have built addresses three distinct problems in robotic handeye coordination for grasping moving objects: fast putation of 3D motion parameters from vision, predictive control of a moving robotic arm to track a moving object, and interception and grasping. The system is able to operate at approximately human arm movement rates, and experimental results in which a moving model train is tracked is presented, stably grasped, and picked up by the system. The algorithms we have developed that relate sensing to actuation are quite general and applicable to a variety of plex robotic tasks that require visual feedback for arm and hand control. I. INTRODUCTION The focus of our work is to achieve a high level of interaction between realtime vision systems capable of tracking moving objects in 3D and a robot arm equipped with a dexterous hand that can be used to intercept, grasp, and pick up a moving object. We are interested in exploring the interplay of handeye coordination for dynamic grasping tasks such as grasping of parts on a moving conveyor system, 安徽建筑工業(yè)學(xué)院 畢業(yè)設(shè)計（論文） 33 assembly of articulated parts, or for grasping from a mobile robotic system. Coordination between an anism39。s sensing modalities and motor control system is a hallmark of intelligent behavior, and we are pursuing the goal of building an integrated sensing and actuation system that can operate in dynamic as opposed to static environments. There has been much research in robotics over the last few years that address either visual tracking of moving objects or generalized grasping problems. However, there have been few efforts that try to link the two problems. It is quite clear that plex robotic tasks such as automated assembly will need to have integrated systems that use visual feedback to plan, execute, and monitor grasping. The system we have built addresses three distinct problems in robotic handeye coordination for grasping moving objects: fast putation of 3D motion parameters from vision, predictive control of a moving robotic arm to track a moving object, and interception and grasping. The system is able to operate at approximately human arm movement rates, using visual feedback to track, intercept, stably grasp, and pick up a moving object. The algorithms we have developed that relate sensing to actuation are quite general and applicable to a variety of plex robotic tasks that require visual feedback for arm and hand control. Our work also addresses a very fundamental and limiting problem that is inherent in building integrated sensing actuation systems。 integration of systems with different sampling and processing rates. Most plex robotic systems are actually amalgams of different processing devices, connected by a variety of methods. For example, our system consists of three separate putation systems: a parallel image processing puter。 a host puter that filters, triangulates, and predicts 3D position from the raw vision data。 and a separate arm control system puter that performs inverse kinematic transformations and jointlevel servicing. Each of these systems has its own sampling rate, noise characteristics, and processing delays, which need to be integrated to achieve smooth and stable realtime performance. In our case, this involves overing visual processing noise and delays with a predictive filter based upon a probabilistic analysis of the system noise characteristics. In addition, realtime arm control needs to be able to operate at fast servo rates regardless of whether new 安徽建筑工業(yè)學(xué)院 畢業(yè)設(shè)計（論文） 34 predictions of object position are available. The system consists of two fixed cameras that can image a scene containing a moving object (Fig. 1). A PUMA560 with a parallel jaw gripper attached is used to track and pick up the object as it moves (Fig. 2). The system operates as follows: 1) The imaging system performs a stereoscopic opticflow calculation at each pixel in the image. From these opticflow fields, a motion energy profile is obtained that forms the basis for a triangulation that can recover the 3D position of a moving object at video rates. 2) The 3D position of the moving object puted by step 1 is initially smoothed to remove sensor noise, and a nonlinear filter is used to recover the correct trajectory parameters which can be used for forward prediction, and the updated position is sent to the trajectoryplanner/armcontrol system. 3) The trajectory planner updates the jointlevel servos of the arm via kinematic transform equations. An additional fixedgain filter is used to provide servolevel control in case of missed or delayed munication from the vision and filtering system. 4) Once tracking is stable, the system mands the arm to intercept the moving object and the hand is used to grasp the object stably and pick it up. The following sections of the paper describe each of these subsystems in detail along with experimental results. П . PREVIOUS WORK Previous efforts in the areas of motion tracking and realtime control are too numerous to exhaustively list here. We instead list some notable efforts that have inspired us to use similar approaches. Burt et al. [9] have focused on highspeed feature detection and hierarchical scaling of images in order to meet the realtime demands of surveillance and other robotic applications. Related work has been reported by. Lee and Wohn [29] and Wiklund and Granlund [43] who u