Hardware Implementation of Compact Genetic Algorithm for Robot Path Planning in Globally Static Environment in 8-bit Microcontroller. / Ali, Mian Mujtaba ; Farooq, Omer ; Khan, Muhammd H. D ; Haxha, Shyqyri.

2019. 242-247 Paper presented at Proceedings The 5th International Conference on Information Management, Cambridge, United Kingdom.

Research output: Contribution to conferencePaper

E-pub ahead of print

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Abstract

In this paper, hardware implementation of genetic algorithm for Robot path planning in Globally static Environment is presented. Genetic algorithm is modified and implemented in 8-bit Microcontroller (MCU) PIC18F452. The
genetic algorithm is designed to decrease number of iteration and processing power by using predefined priorities for parent/ initial path generation rather than creating parent paths randomly. The unmanned ground vehicle (UGV) is designed which receives starting node, final destination and obstacles wirelessly, it then create multiple parent paths by using different priorities, cross over to create new child paths, uses distance as fitness function for determining Optimal or shortest path while avoiding the obstacles and uses stepper motors with three- dimensional movements to reach its destination. The environment is 5x5 static Grid Map in which obstacles are known before path planning. The MCU determines optimal path with no obstacles and require minimum distance to reach its destination.
Original languageEnglish
Pages242-247
Number of pages6
DOIs
Publication statusE-pub ahead of print - 16 May 2019
EventProceedings The 5th International Conference on Information Management: ICIM 2019 conference Proceedings (IEEE) - Cambridge, Cambridge, United Kingdom
Duration: 24 Mar 201927 Mar 2019
Conference number: 5
http://www.icim.org/

Conference

ConferenceProceedings The 5th International Conference on Information Management
Abbreviated titleICIM2019 March 24-27, 2019
CountryUnited Kingdom
CityCambridge
Period24/03/1927/03/19
Internet address
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

ID: 33747048