کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6903244 | 1446989 | 2018 | 30 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Autonomous task allocation for multi-UAV systems based on the locust elastic behavior
ترجمه فارسی عنوان
تخصیص وظیفه مستقل برای سیستم های چند منظوره ی مبتنی بر رفتار الاستیک یخ زده
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کلمات کلیدی
چند پهپاد تخصیص وظیفه، سیستم های هواپیمایی بدون سرنشین، الگوریتم های الهام گرفته از بیوگرافی، جستجو و نجات، آگاهی وضعیت، محاسبات نرم،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزارهای علوم کامپیوتر
چکیده انگلیسی
Task allocation is a grand challenge facing researches and practitioners in multiple unmanned aerial vehicles (multi-UAVs) missions. This paper proposes a new autonomous bio-inspired approach for efficiently allocating tasks among multiple UAVs during a mission. Task assignments are dynamically adjusted by each UAV on the basis of criteria related to the individual UAV operational status and mission parameters, without direct communication between the UAVs actively taking part in the mission. The proposed approach was inspired by the nature of locust species and their autonomous and elastic behavior in response to inside and outside impetus. Four long-standing multi-UAVs task allocation paradigms, including the auction-based, max-sum, ant colony optimization and opportunistic coordination schemes were used to benchmark the performance of the proposed approach. Experimental results demonstrated that the new approach substantially improves the net throughput and the mean task completion time while maintaining a linear running time when compared to all benchmarks under different scales of fleet size and number of tasks, demonstrating better scalability and sustainability.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Soft Computing - Volume 71, October 2018, Pages 110-126
Journal: Applied Soft Computing - Volume 71, October 2018, Pages 110-126
نویسندگان
Heba A. Kurdi, Ebtesam Aloboud, Maram Alalwan, Sarah Alhassan, Ebtehal Alotaibi, Guillermo Bautista, Jonathan P. How,