توسعه و پیاده سازی یک سیستم کنترل معماری باز در زمان واقعی برای سیستم های ربات صنعتی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|18219||2004||15 صفحه PDF||سفارش دهید||9326 کلمه|
هزینه ترجمه مقاله بر اساس تعداد کلمات مقاله انگلیسی محاسبه می شود.
این مقاله شامل 9326 کلمه می باشد.
نسخه انگلیسی مقاله همین الان قابل دانلود است.
هزینه ترجمه مقاله توسط مترجمان با تجربه، طبق جدول زیر محاسبه می شود:
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Engineering Applications of Artificial Intelligence, Volume 17, Issue 5, August 2004, Pages 469–483
This paper presents a real-time open-architecture control system (ROACS) which has been developed for flexible manipulation of industrial robots. Flexible manipulation refers to robot manipulation that handles tasks with uncertainties; hence, decision-making based on feedback data is essential in realtime operation. A real-time open-architecture control system with the capacity of parallel processing of realtime events, extraction of information from realtime data, and intelligent decision-making, is developed. The entire system consists of a real-time subsystem which manages robot hardware and executes path planning and data processing, and an intelligent subsystem which performs intelligent decision-making and feedback task control. In the context of intelligent task control, information extraction, fuzzy-logic-based interpretation and decision-making, and a novel design of associated real-time robot task language (RTTL) are developed. The conflicts between high bandwidth requirements for real-time services and the undeterministic time length for intelligent decision-making are managed in a cooperative real-time intelligent system model. Client–server architecture is found quite suitable for implementation of the system. The entire system has been successfully developed, implemented, and demonstrated for a robotic salmon slicing task which requires online determination of the backbone position.
Robot manipulators are particularly suitable for flexible production operations where small batches of a range of different products are manufactured. A large majority of successful industrial robots are designed for carrying out tasks where accurate positioning would be important (De Silva, 2004; Unimation, 1985), and the positions have to be accurately specified in the robotic task commands. Force and mechanical impedance considerations are of minor significance here and are neither specified in the task description nor provided by the robot control system. However, there are many other process applications where tasks cannot be described in position specifications alone and force and impedance requirements become as important as the position requirements (De Silva, 2004; Gu, 1999). For example, in robotic tasks of interacting with a rigid environment (e.g., a parts assembly process), a very small change in displacement against a hard surface would result in a large force. In such tasks, position control can be viewed as an ill-posed problem, where exact positioning is almost impossible. Force control would be relatively easy and more appropriate in these circumstances. Fig. 1 illustrates a salmon slicing task, where a fish is sliced up to its backbone in the sequence a1, to c1, a2 to c2,..., and then the steaks are removed from the bone using a transverse cut sequence c0 to c1, to c2, ..., to cm. Steaks that are produced in this manner will be boneless.
نتیجه گیری انگلیسی
A sensor-based, five-layer, hierarchical control system has been developed to accommodate the new technologies of flexible manipulation in robotic systems. A distinctive feature of this architecture is the feasibility of information feedback at every level. Data can be processed and interpreted (abstracted) in appropriate formats for use in different levels. Paths of robot motion may be planned online according to the operation status of the particular task. Addressing industrial applications, approaches have been developed for knowledge-based (“intelligent”) interpretation of impedance information, which is useful in process monitoring and control. Task uncertainties at the top-level task controller can be reduced by online recognition of material characteristics using knowledge-based decision making, and feedback of this high-level information into the control system.