مشخصات مقاله | |
ترجمه عنوان مقاله | الگوریتم بهینه سازی ازدحام معلق های برنامه ریزی مسیر جدید برای برنامه ریزی مسیر بازوهای مکانیکی روبات |
عنوان انگلیسی مقاله | A New Trajectory-Planning Beetle Swarm Optimization Algorithm for Trajectory Planning of Robot Manipulators |
انتشار | مقاله سال 2019 |
تعداد صفحات مقاله انگلیسی | 15 صفحه |
هزینه | دانلود مقاله انگلیسی رایگان میباشد. |
پایگاه داده | نشریه IEEE |
نوع نگارش مقاله |
مقاله پژوهشی (Research Article) |
مقاله بیس | این مقاله بیس نمیباشد |
نمایه (index) | Scopus – Master Journals List – JCR |
نوع مقاله | ISI |
فرمت مقاله انگلیسی | |
ایمپکت فاکتور(IF) |
4.641 در سال 2018 |
شاخص H_index | 56 در سال 2019 |
شاخص SJR | 0.609 در سال 2018 |
شناسه ISSN | 2169-3536 |
شاخص Quartile (چارک) | Q2 در سال 2018 |
مدل مفهومی | ندارد |
پرسشنامه | ندارد |
متغیر | ندارد |
رفرنس | دارد |
رشته های مرتبط | مهندسی کامپیوتر، مهندسی برق |
گرایش های مرتبط | مهندسی الگوریتم و محاسبات، رباتیک |
نوع ارائه مقاله |
ژورنال |
مجله / کنفرانس | دسترسی – IEEE Access |
دانشگاه | Department of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China |
کلمات کلیدی | بهینه سازی ازدحام معلق ها، بازوهای مکانیکی روبات، برنامه ریزی مسیر، الگوریتم های بهینه سازی، سیستم های کنترل |
کلمات کلیدی انگلیسی | Beetle swarm optimization, robot manipulators, trajectory planning, optimization algorithms, control systems |
شناسه دیجیتال – doi |
https://doi.org/10.1109/ACCESS.2019.2949271 |
کد محصول | E13906 |
وضعیت ترجمه مقاله | ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید. |
دانلود رایگان مقاله | دانلود رایگان مقاله انگلیسی |
سفارش ترجمه این مقاله | سفارش ترجمه این مقاله |
فهرست مطالب مقاله: |
Abstract I. Introduction II. Preliminaries III. Methodology IV. Simulation Studies and Comparisons V. Conclusion and Future Work Authors Figures References |
بخشی از متن مقاله: |
Abstract
Based on a heuristic optimization algorithm, this paper proposes a new algorithm named trajectory-planning beetle swarm optimization (TPBSO) algorithm for solving trajectory planning of robots, especially robot manipulators. Firstly, two specific manipulator trajectory planning problems are presented as the practical application of the algorithm, which are point-to-point planning and fixed-geometric-path planning. Then, in order to verify the effectiveness of the algorithm, this paper develops a control model and conducts numerical experiments on two planning tasks. Moreover, it compares with existing algorithms to show the superiority of our proposed algorithm. Finally, the results of numerical comparisons show that algorithm has a relatively faster computational speed and better control performance without increasing computational complexity. Introduction The robotic arm, or the manipulator, has similar functions to a human arm and can be either a separate mechanism or part of a more complex robot [1]. It can be considered that the link of the robot arm forms a kinematic chain, and the end of the kinematic chain is called an end-effector for performing actual operations such as grasping, spraying, cutting, etc. The robotic arm is widely used in modern society. In the field of industrial manufacturing, it is used for assembly, spraying, welding etc [2]. In the medical field, it is used as a surgical aid [3], [4]. And in agriculture, for picking vegetables and fruits. Even in space exploration the robotic arm can also find its application. In the research area of robot manipulator, trajectory planning has always been a hot spot, which is usually performed with constraints that may come from dynamic equations or from the inputs [5]. There are several important issues in the study of manipulator trajectory planning, one of which is the solution of inverse kinematics transformation [6]–[10]. Due to the high nonlinearity of inverse kinematics transformation, the solution process is difficult. Therefore, improving the efficiency and effectiveness of inverse kinematics is very important in manipulator trajectory planning, especially in redundant manipulator trajectory planning [11]–[18]. The second is obstacle avoidance. In many manufacturing applications today, robot manipulators must use their endeffector to pass through the desired curve while their fuselage avoids collisions with obstacles in the environment. |