مشخصات مقاله | |
ترجمه عنوان مقاله | فیلتر ذرات بهبود یافته برای محلی سازی روبات های تلفن همراه بر اساس بهینه سازی ازدحام ذرات |
عنوان انگلیسی مقاله | An improved particle filter for mobile robot localization based on particle swarm optimization |
انتشار | مقاله سال 2019 |
تعداد صفحات مقاله انگلیسی | 13 صفحه |
هزینه | دانلود مقاله انگلیسی رایگان میباشد. |
پایگاه داده | نشریه الزویر |
نوع نگارش مقاله |
مقاله پژوهشی (Research Article) |
مقاله بیس | این مقاله بیس میباشد |
نمایه (index) | Scopus – Master Journals List – JCR |
نوع مقاله | ISI |
فرمت مقاله انگلیسی | |
ایمپکت فاکتور(IF) |
5.891 در سال 2018 |
شاخص H_index | 162 در سال 2019 |
شاخص SJR | 1.190 در سال 2018 |
شناسه ISSN | 0957-4174 |
شاخص Quartile (چارک) | Q1 در سال 2018 |
مدل مفهومی | ندارد |
پرسشنامه | ندارد |
متغیر | ندارد |
رفرنس | دارد |
رشته های مرتبط | مهندسی کامپیوتر |
گرایش های مرتبط | الگوریتم و محاسبات |
نوع ارائه مقاله |
ژورنال |
مجله / کنفرانس | سیستم های خبره با کابردهای مربوطه – Expert Systems with Applications |
دانشگاه | Department of Automation, University of Science and Technology of China, Hefei 230027, PR China |
کلمات کلیدی | روبات تلفن همراه، محلی سازی جهانی، ردیابی حالت محلی، فیلتر ذرات، بهینه سازی ازدحام ذرات |
کلمات کلیدی انگلیسی | Mobile robot، Global localization، Local pose tracking، Particle filter، Particle swarm optimization |
شناسه دیجیتال – doi |
https://doi.org/10.1016/j.eswa.2019.06.006 |
کد محصول | E13562 |
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 1. Introduction 2. Preliminaries 3. Proposed algorithm 4. Experimental results 5. Conclusion CRediT authorship contribution statement Declaration of Competing Interest Acknowledgments References |
بخشی از متن مقاله: |
Abstract
As one of the most important issues in the field of mobile robotics, self-localization allows a mobile robot to identify and keep track of its own position and orientation as the robot moves through the environment. In this work, a hybrid localization approach based on the particle filter and particle swarm optimization algorithm is presented, focusing on the localization tasks when an a priori environment map is available. This results an accurate and robust particle filter based localization algorithm that is able to work in symmetrical environments. The performance of the proposed approach has been evaluated for indoor robot localization and compared with two benchmark algorithms. The experimental results show that the proposed method achieves robust and accurate positioning results in indoor environments, requiring fewer particles than the benchmark methods. This advance could be integrated in a wide range of mobile robot systems, helping to reduce the computational cost and improve the navigation efficiency. Introduction Along with the technological advancements in the field of mobile robotics, research interest in autonomous mobile robots has been increasing in the past decades. A diverse range of applications in rescue (Michael et al., 2014), mining (Ma & Mao, 2018), agriculture (Bengochea-Guevara, Conesa-Muñoz, Andújar, & Ribeiro, 2016), military (Miksik, Petyovsky, Zalud, & Jura, 2011) and civilian tasks (Choi, Lee, Viet, & Chung, 2017; Le, Phung, & Bouzerdoum, 2014; Song, Gao, Ding, Deng, & Chao, 2017) encourage researchers to carry out research works in mobile robotics. Self-localization is a prerequisite for successful deployment of an autonomous mobile robot since it identifies the robot’s pose (position and orientation) as it moves in the environment. By providing an “absolute” position estimate to the map frame, robot localization is one of the critical issues for mobile robot systems and it is typically the foundation of a variety of tasks, including map building, autonomous navigation, mobile manipulation, target tracking, etc. The mobile robot localization problem falls into two main categories: global localization (GL) and local pose tracking (relocalization) (Thrun, Burgard, & Fox, 2005). The local pose tracking problem assumes that the initial pose of the robot is already known, and it tries to keep track of the robot state over time. The GL problem is fundamentally different because no prior knowledge about the robot’s position is available, hence the robot has to locate itself from scratch and reduce the ambiguities of pose estimates. |