آموزش تقلیدی حرکات پیچیده به ربات‌های انسان‌نما به کمک بهینه سازی تکاملی شبکه‌ عصبی مولد الگوی واحد

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشکده مهندسی مکانیک، دانشگاه اصفهان، اصفهان، ایران

2 دانشکده مهندسی کامپیوتر، دانشگاه اصفهان، اصفهان، ایران

چکیده

در این مقاله یک سامانه مبتنی بر ساختارهای عصبی موسوم به مولد مرکزی الگوی واحد ارائه شده که قادر است الگوی موردنیاز برای حرکت در یک ربات را براساس یک نوع آموزش نمایشی به دست آورد. مولد الگوی واحد می‌تواند به دو زیرسامانه گسسته و ریتمیک تقسیم‌بندی شود. زیرسامانه اول مسئولیت تولید حرکات کوتاه و معطوف به هدف و زیرسامانه دوم مسئولیت تولید حرکات ریتمیک را به عهده دارد. الگوریتم آموزشی خاصی برای استفاده از این مولدهای الگوی واحد در این مقاله طراحی شده است. مفاصل و بازوهای ربات با تشخیص اسکلت بدن انسان توسط حسگر کینکت به‌صورت بلادرنگ کنترل می‌شوند. اساس کار انجام شده بدین شکل است که ابتدا به کمک حسگر کینکت دنباله های حرکتی بدن مربی ضبط شده و به کامپیوتر منتقل می‌شوند. این دنباله‌های حرکتی تعدادی نوسان سازهای غیرخطی را آموزش داده و سپس نوسان سازهای غیرخطی حرکات را برای ربات انسان نما بازتولید می‌کنند. برای ارزیابی نتایج سیگنال‌های آموزش داده شده در نرم افزار وی رپ شبیه سازی شده و سپس بر روی ربات انسان نمای بایولوید پیاده سازی شدند. مدل ارائه شده یک بستر مناسب جهت ارائه آموزش‌های نمایشی و مبتنی بر تقلید برای ربات‌های انسان‌نما محسوب می‌شود که نیاز به کنترل صریح و برنامه نویسی مستقیم را برطرف می‌سازد و امکان آموزش غیرمستقیم رفتارهای پیچیده روی انواع ربات ها را مهیا می‌سازد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Imitation Learning of Complex Behaviors to Humanoid Robots using Evolutionary Optimization of Neural Network of Unit Pattern Generator

نویسندگان [English]

  • B. Khodabandeh 1
  • H. Shahbazi 1
  • A. Monadjemi 2
1 Department of Mechanical Engineering, University of Isfahan, Isfahan, Iran
2 Department of Computer Engineering, University of Isfahan, Isfahan, Iran
چکیده [English]

In this paper, a system based on neural structures known as central pattern generator is presented which enables to acquire the required patterns to move a robot based on a demonstration training. Unit pattern generator can be divided to two subsystems, one is a rhythmic system and the other is a discrete system. The first subsystem is responsible to produce short movements and the second subsystem is responsible to produce rhythmic movements. The special learning algorithm is designed to use these unit pattern generators. Joints and limbs of robot were controlled by Kinect sensor in real time by recognition of the human body skeleton. The work steps were done in this way that the motion sequences of teacher’ body were recorded by Kinect sensor, then transmitted to the computer. These motion sequences teach some nonlinear oscillators then they reproduce motions for humanoid robot. As a result, humanoid body joints imitate the teacher movement in a real time. The main contribution of this paper is design of this learning algorithm which is able to simultaneously search for the weights and topology of the network the algorithm synchronize the neural network by coupling the neurons at the last stage.

کلیدواژه‌ها [English]

  • Humanoid Robot
  • Imitation Learning
  • Nonlinear Oscillator
  • Central pattern Generator
  • Kinect Microsoft
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