کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
382122 660737 2015 15 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Path planning for mobile robots using Bacterial Potential Field for avoiding static and dynamic obstacles
ترجمه فارسی عنوان
برنامه ریزی مسیر برای روبات های تلفن همراه با استفاده از زمینه بالقوه باکتریایی برای اجتناب از موانع استاتیک و پویا
کلمات کلیدی
برنامه ریزی مسیر زمینه بالقوه باکتری، الگوریتم تکاملی باکتریایی، روبات های موبایل اجتناب از برخورد
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی


• The BPF proposal ensures a feasible, optimal and safe path for robot navigation.
• The results of BPF overcomes APF and other EAPF methods like those based in GAs.
• The BPF is quite faster in optimization leading to reduction in computation burden.
• The BPF running in parallel mode is the most suitable to fulfill local and global controllability.
• The BPF is capable to work in offline and online mode with static and dynamic obstacles.

In this paper, optimal paths in environments with static and dynamic obstacles for a mobile robot (MR) are computed using a new method for path planning. The proposed method called Bacterial Potential Field (BPF) ensures a feasible, optimal and safe path. This novel proposal makes use of the Artificial Potential Field (APF) method with a Bacterial Evolutionary Algorithm (BEA) to obtain an enhanced flexible path planner method taking all the advantages of using the APF method, strongly reducing its disadvantages. Comparative experiments for sequential and parallel implementations of the BPF method against the classic APF method, as well as with the Pseudo-Bacterial Potential Field (PBPF) method, and with the Genetic Potential Field (GPF) method, all of them based on evolutionary computation to optimize the APF parameters, were achieved. A simulation platform that uses an MR realistic model was designed to test the path planning algorithms. In general terms, it was demonstrated that the BPF outperforms the APF, GPF, and the PBPF methods by reducing the computational time to find the optimal path at least by a factor of 1.59. These results have a positive impact in the ability of the BPF path planning method to satisfy local and global controllability in dynamic complex environments, avoiding collisions with objects that will interfere the navigation of the MR.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 42, Issue 12, 15 July 2015, Pages 5177–5191
نویسندگان
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