موقعیت یابی ربات سیار با استفاده از فیلتر کالمن دو بخشی هموار
محورهای موضوعی : مهندسی برق و کامپیوتررمضان هاونگی 1 * , سیمین حسین زاده 2
1 - دانشکده مهندسی برق و کامپیوتر، دانشگاه بیرجند
2 - دانشکده مهندسی برق و کامپیوتر، دانشگاه بیرجند
کلید واژه: ربات سیار, فیلتر کالمن, فیلتر کالمن دوبخشی, موقعیت‌یابی ربات,
چکیده مقاله :
مهم‌ترین مسئله برای یک ربات متحرک، جهت‌یابی است. موفقیت در موقعیت‌یابی یکی از چهار نیاز اصلی در جهت‌یابی است که شامل ادراک، موقعیت‌یابی، شناخت و کنترل حرکت می‌باشد. چگونگی ارائه یک راه حل دقیق موقعیت‌یابی برای ربات‌های سیار در بسیاری از کاربردهای اینترنت اشیا ضروری است. برای رسیدن به این هدف در این مقاله روشی مبتنی بر فیلتر کالمن دوبخشی برای موقعیت‌یابی ربات‌های سیار پیشنهاد شده است. الگوریتم پیشنهادی شامل دو بخش است که بخش اول رگرسیون خطی آماری و بخش دوم یک فیلتر کالمن با بردار خطای حالت می‌باشد. روش پیشنهادی در مقایسه با روش جدید ترکیبی TLNF/UK در مسیرهای حرکت دایره‌ای، مستطیلی و Zشکل که همراه با نویز است، آزمایش شده است. نتایج تجربی نشان می‌دهند که روش پیشنهادی قادر به دستیابی به دقت موقعیت‌یابی بهتری بوده و همچنین مشاهده می‌شود که خطاهای تخمین در روش پیشنهادی کمتر است و توانسته دقت تخمین را نسبت به روش ترکیبی TLNF/UK افزایش دهد.
The most important issue for a mobile robot is orientation. Success in localization is one of the four main needs in orientation, which include: perception, localization, recognition and movement control. How to provide an accurate localization solution for mobile robots is essential in many IoT applications. To achieve this goal, in this article, a method based on two-part Kalman filter is proposed for localization of mobile robot. The proposed algorithm consists of two parts, the first part is statistical linear regression and the second part is a Kalman filter with state error vector. The proposed method is tested in comparison with the new hybrid TLNF/UK method on circular, rectangular and z-shaped motion paths that are accompanied by noise. The experimental results show that the proposed method has been able to achieve better localization accuracy and it is also observed that the estimation errors in the proposed method are less and it has been able to increase the estimation accuracy compared to the combined TLNF/UK method.
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