Abstract




 
   

IJE TRANSACTIONS A: Basics Vol. 28, No. 4 (April 2015) 537-545   

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  PATH FOLLOWING AND VELOCITY OPTIMIZING FOR AN OMNIDIRECTIONAL MOBILE ROBOT
 
K. Kanjanawanishkul
 
( Received: October 06, 2014 – Accepted: January 29, 2015 )
 
 

Abstract    In this paper, the path following controller of an omnidirectional mobile robot (OMR) has been extended in such a way that the forward velocity has been optimized and the actuator velocity constraints have been taken into account. Both have been attained through the proposed model predictive control (MPC) framework. The forward velocity has been included into the objective function, while the actuator saturation has been considered as hard constraints. As shown in the simulation results, the OMR can converge to and follow a reference path successfully and safely. The forward velocity of the robot was close to the desired one and the desired orientation angle was achieved at a given point on the path, while the actuator constraints were not violated. Furthermore, to show the effectiveness of our proposed framework, a comparison with conventional approaches used to bound actuator constraints has been conducted. Mean squared error (MSE), integral squared error (ISE), and traveling distance were used as performance indices. As seen in the results, the proposed control strategy outperforms the conventional approaches. The proportion between translational and rotational velocities was optimized, although the limitation of the rotational and translational velocities was coupled via the OMR’s orientation angle.

 

Keywords    Path Following Control, Robot Motion, Model Predictive Control, Omnidirectional Mobile Robots, Actuator Constraints

 

چکیده    در این مقاله، مسیر کنترل کننده یک ربات همراه چندوجهی (OMR) به گونه ای توسعه داده شده است که سرعت رو به جلو بهینه و محدودیت های سرعت محرک در نظر گرفته شده است. هر دو از طریق چارچوب کنترل پیش بینی مدل پیشنهادی (MPC) به دست آمده است. سرعت رو به جلو درتابع هدف گنجانده شده است، در حالی که اشباع محرک به عنوان محدودیت های سخت در نظر گرفته می شود. همانطور که در نتایج شبیه سازی نشان داده شده است،OMR می تواند به یک مسیر مرجع به طور موفقیت آمیز و امن همگرایی و پیروی کند. سرعت رو به جلو ربات به یک شخص مورد نظر نزدیک و زاویه گرایش مورد نظر در یک نقطه داده شده در مسیر به دست آمد، در حالی که محدودیت های محرک نقض نمی شود. علاوه بر این، برای نشان دادن اثربخشی چارچوب پیشنهادی ما، یک مقایسه با روشهای مرسوم که برای اتصال به محدودیت های محرک استفاده می شود، هدایت شده است. میانگین مربعات خطا (MSE)، خطای مربع جدایی ناپذیر (ISE)، و مسافت سفر به عنوان شاخص عملکرد استفاده شد. همانطور که در نتایج دیده می شود، استراتژی کنترل پیشنهاد بهتر از روشهای مرسوم عمل می کند. نسبت بین ترجمه و سرعت چرخشی بهینه سازی شد اگر چه محدودیت سرعت چرخشی و ترجمه ای از طریق زاویه گرایش OMR کوپل شده است.

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