NDLI: A UAV path planning with parallel ACO algorithm on CUDA platform

Content Provider	IEEE Xplore Digital Library
Author	Cekmez, U. Ozsiginan, M. Sahingoz, O.K.
Copyright Year	2014
Description	Author affiliation: Comput. Eng. Dept., Turkish Air Force Acad., Istanbul, Turkey (Sahingoz, O.K.) \|\| Comput. Eng. Dept., Yildiz Tech. Univ., Istanbul, Turkey (Cekmez, U.) \|\| Aeronaut. & Space Technol. Inst., Turkish Air Force Acad., Istanbul, Turkey (Ozsiginan, M.)
Abstract	Solving the path planning problem of a UAV is a challenging issue especially if there are too many checkpoints to visit. Mainly, the brute force approach is used to find the shortest path in the mission area, which requires too many times to find a solution. Therefore, evolutionary algorithms and swarm intelligence techniques are used to find a feasible solution in an acceptable time. In this study, path planning problem of a UAV is solved by using a highly parallelized Ant Colony Optimization (ACO) algorithm on CUDA platform. The UAV path is constructed for disseminating keys and collecting data from a Wireless Sensor Network, which is previously defined. Due to its simplicity and effectiveness, ACO is selected as a path planning algorithm. However, ACO is not satisfactory if the mission area becomes large and there are an excessive number of checkpoints and/or additional constraints. In order to increase the performance, some parallelization techniques must be used in high performance computing platforms. GPU architecture has emerged as a powerful and low cost architecture for enabling impressive speedups for scientific calculations. Therefore, the parallel structure is constructed on CUDA architecture. The experimental results are compared with the CPU performance of the serial algorithm, and they clearly show that the proposed approach have a great potential for acceleration of ACO and allow to solve many complex tasks such as UAV path planning problem. We also present the execution results with different parameter values to expose the results for the researchers.
Sponsorship	IEEE Control Syst. Soc.
Starting Page	347
Ending Page	354
File Size	2655800
Page Count	8
File Format	PDF
ISBN	9781479923762
DOI	10.1109/ICUAS.2014.6842273
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-27
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Instruction sets Graphics processing units Computer architecture Cities and towns Path planning Planning Central Processing Unit
Content Type	Text
Resource Type	Article

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