بهینهسازی مسیر ربات متحرک با رویکرد چندهدفه: ترکیب الگوریتم بهینهسازی ذرات و خفاش وفقی
محورهای موضوعی : هوش مصنوعی و رباتیکمحمد اسعدی 1 , زهرا محمدی 2 , حسین نظامآبادیپور 3 , محدثه سليمانپورمقدم 4 *
1 - مهندسی مکاترونیک، مجتمع آموزش عالی بم، بم، ایران
2 - دانشکده مهندسی برق، دانشگاه شهید باهنر کرمان، کرمان، ایران
3 - دانشگاه شهید باهنر کرمان
4 - مهندسی مکاترونیک، مجتمع آموزش عالی بم، بم، ایران
کلید واژه: برنامهریزی مسیر, بهینهسازی چند هدفه, الگوریتمهای فراابتکاری, الگوریتم خفاش و الگوریتم بهینهسازی ذرات.,
چکیده مقاله :
این پژوهش یک چارچوب نوین برای برنامهریزی مسیر مبتنی بر بهینهسازی چندهدفه ارائه میدهد که به طور همزمان سه معیار کلیدی شامل کمینهسازی طول مسیر، کاهش نرخ تغییرات زاویهای و حداکثرسازی فاصله ایمن از موانع را مد نظر قرار میدهد. در مرحله نخست از ترکیب روش محدودیت اپسیلون و رویکرد جمع وزنی برای تبدیل مسئله بهینهسازی چندهدفه به یک مسئله تکهدفه استفاده شده است. به دلیل NP-hard بودن مسئله، یک الگوریتم فرا ابتکاری ترکیبی تطبیقی با عنوان PSO-ABA توسعه یافته است که از ادغام دو مکانیزم مکمل جستجوی سراسری مبتنی بر الگوریتم بهینهسازی ذرات (PSO) و الگوریتم خفاش انطباقی (ABA) بهره میبرد. در این الگوریتم، وظیفه بهروزرسانی موقعیت ربات بر عهده الگوریتم ABA است، در حالی که الگوریتم PSO به دنبال تنظیم پارامترهای مناسب میباشد. این مقاله به چند نکته کلیدی میپردازد: نخست، الگوریتم مذکور با اصلاح روابط موجود در الگوریتم خفاش، طراحی مسیری در فضای واقعی را هدف قرار میدهد بهطوریکه حرکت ربات تا حد امکان به سمت مسیر هدف هدایت شود و از چرخشهای ناگهانی ربات جلوگیری گردد. دوم، با اصلاح تابع شایستگی، این الگوریتم مانع از بهروزرسانی موقعیت ربات در داخل موانع میشود و مسیرهای نامعتبر را حذف میکند. ارزیابی عملکرد در محیطهای شبیهسازی شده با چیدمان موانع پیچیده نشان میدهد که چارچوب پیشنهادی در مقایسه با روشهای مرسوم مسیریابی، عملکرد بهتری در دستیابی به مسیر بهینه برای ربات دارد.
This study proposes a novel path planning framework based on multi-objective optimization, which simultaneously addresses three key criteria: minimizing path length, optimizing motion smoothness, and maximizing safe distance from obstacles. The multi-objective problem is first transformed into a single-objective formulation using the ε-constraint method and weighted sum approach. Given the NP-hard nature of the problem, an adaptive hybrid metaheuristic algorithm, termed PSO-ABA (Particle Swarm Optimization-Adaptive Bat Algorithm), is developed by integrating the global search mechanism of PSO with the frequency-tuning capabilities of the Bat Algorithm (BA). Subsequently, an adaptive hybrid metaheuristic algorithm, termed PSO-ABA, is developed, which leverages the integration of two complementary global search mechanisms based on Particle Swarm Optimization (PSO) and the Adaptive Bat Algorithm (ABA). In this algorithm, the task of updating the robot’s position is assigned to the ABA, while the PSO is focused on tuning the appropriate parameters. The proposed algorithm addresses several key aspects. First, it aims to develop a trajectory in real space by modifying the existing relationships within the Bat Algorithm. This modification ensures that the robot’s movement is directed as closely as possible toward the target path while minimizing abrupt turns. Second, by enhancing the fitness function, the algorithm prevents the robot’s position from being updated when it is within obstacles, effectively eliminating invalid paths. Performance evaluation in simulated environments with complex obstacle arrangements demonstrates that the proposed framework outperforms conventional routing methods in achieving optimal paths for robots.
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