FPGA implementation of an evolutionary algorithm for autonomous unmanned aerial vehicle on-board path planning

Kok, Jonathan, Gonzalez, Luis F., & Kelson, Neil A. (2013) FPGA implementation of an evolutionary algorithm for autonomous unmanned aerial vehicle on-board path planning. IEEE Transactions on Evolutionary Computation, 17(2), pp. 272-281.

View at publisher


In this paper, a hardware-based path planning architecture for unmanned aerial vehicle (UAV) adaptation is proposed. The architecture aims to provide UAVs with higher autonomy using an application specific evolutionary algorithm (EA) implemented entirely on a field programmable gate array (FPGA) chip. The physical attributes of an FPGA chip, being compact in size and low in power consumption, compliments it to be an ideal platform for UAV applications. The design, which is implemented entirely in hardware, consists of EA modules, population storage resources, and three-dimensional terrain information necessary to the path planning process, subject to constraints accounted for separately via UAV, environment and mission profiles. The architecture has been successfully synthesised for a target Xilinx Virtex-4 FPGA platform with 32% logic slices utilisation. Results obtained from case studies for a small UAV helicopter with environment derived from LIDAR (Light Detection and Ranging) data verify the effectiveness of the proposed FPGA-based path planner, and demonstrate convergence at rates above the typical 10 Hz update frequency of an autopilot system.

Impact and interest:

19 citations in Scopus
9 citations in Web of Science®
Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

468 since deposited on 25 Mar 2012
42 in the past twelve months

Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 49288
Item Type: Journal Article
Refereed: Yes
Additional URLs:
Keywords: evolutionary algorithm, field programmable gate array, path planning, unmanned aerial vehicle
DOI: 10.1109/TEVC.2012.2192124
ISSN: 1089-778X
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000)
Divisions: Current > Research Centres > Australian Research Centre for Aerospace Automation
Current > Research Centres > High Performance Computing and Research Support
Copyright Owner: Copyright 2013 IEEE
Deposited On: 25 Mar 2012 22:37
Last Modified: 15 Apr 2013 07:30

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page