مسیریابی ربات با استفاده از الگوریتم انتخاب کلونال
محورهای موضوعی : مهندسی برق و کامپیوتر
سید علی دانش نیا
1
(دانشگاه پیام نور مرکز قشم)
شهرام گلزاري
2
(دانشگاه هرمزگان)
عباس حریفی
3
(دانشگاه هرمزگان)
عباسعلی رضائی
4
(دانشگاه پیام نور تهران)
کلید واژه: ربات سیارالگوریتم انتخاب کلونالطرح‌ریزی مسیرمسیریابی,
چکیده مقاله :
مسیریابی ربات یکی از موضوعات مهم در مبحث رباتیک سیار است. هدف، پیداکردن یک مسیر پیوسته از یک موقعیت اولیه به یک مقصد نهایی است به طوری که عاری از برخورد بوده و بهینه یا نزدیک به بهینه نیز باشد. از آنجایی که مسئله مسیریابی ربات از نوع مسایل بهینه‌سازی است، می‌توان از الگوریتم‌های تکاملی برای حل این مسئله استفاده نمود. امروزه الگوریتم انتخاب کلونال به علت داشتن ویژگی‌های محاسباتی ارزنده به دفعات برای حل مسایل مورد استفاده قرار گرفته است، اما در زمینه استفاده از این روش برای حل مسئله مسیریابی ربات تلاش‌های بسیار کمی انجام شده است. اندک تلاش‌های انجام‌گرفته نیز در واقع نوعی الگوریتم ژنتیک بهبودیافته می‌باشند. در این پژوهش با بهره‌گیری از تمام ویژگی‌های الگوریتم کلونال روشی کارا برای مسیریابی ربات در حضور موانع طراحی شده است. روش ارائه‌شده در محیط‌های متنوع و با اجراهای مختلف از نظر معیارهای طول مسیر پیشنهادی و تعداد نسل‌های لازم برای تولید مسیر مورد ارزیابی قرار می‌گیرد. بر اساس نتایج حاصل از آزمایش‌های متعدد، روش ارائه‌شده عملکرد بهتری نسبت به الگوریتم ژنتیک در تمامی محیط‌ها و همه پارامترهای ارزیابی از خود نشان می‌دهد. به‌خصوص با افزایش تعداد رئوس موانع و نیز موانع مقعر، روش پیشنهادی عملکرد بسیار بهینه‌تری در مقایسه با الگوریتم ژنتیک از خود نشان می‌دهد. همچنین مقایسه عملکرد روش پیشنهادی با الگوریتم ترکیبی جغرافیای زیستی-ازدحام ذرات بیانگر برتری الگوریتم مسیریابی مبتنی بر انتخاب کلونال هست.
Path planning of mobile robot is one of the most important topics in mobile robotic discussion. The aim of this study is to find a continuous path from an initial position to the final target; So that, it should be free of collision and optimal or near to optimal. Since path planning problem of robot is one type of optimization problems, the evolutionary algorithms can be used to solve this problem. Nowadays, clonal selection algorithm is frequently used to solve the problems because of having valuable computational characteristics. But very little attempts have been done in the field of using this method to solve robot path planning problem. Few accomplished attempts are actually a kind of improved genetic algorithm. In this research, an efficient method for robot path planning in the presence of obstacles is designed using all the features of the clonal selection algorithm. The proposed method is evaluated in various environments with different runs in terms of the proposed path length criteria and the number of generations needed to generate the path. Based on the results of experiments, the proposed method shows better performance than the genetic algorithm in all environments and all the evaluation parameters. Especially, by increasing the number of obstacles vertices and also concave obstacles, the proposed method shows much more efficient performance than the genetic algorithm. Also, comparing the performance of the proposed method with the BPSO algorithm (presented in another study) indicates the superiority of path planning algorithm based on the clonal selection.
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