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http://dx.doi.org/10.6109/JKIICE.2009.13.8.1483

Multiple Path-planning of Unmanned Autonomous Forklift using Modified Genetic Algorithm and Fuzzy Inference system  

Kim, Jung-Min (부산대학교 전자전기공학과)
Heo, Jung-Min (부산대학교 전자전기공학과)
Kim, Sung-Shin (부산대학교 전자전기공학과)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.13, no.8, 2009 , pp. 1483-1490 More about this Journal
Abstract
This parer is presented multiple path-planning of unmanned autonomous forklift using modified genetic algorithm and fuzzy inference system. There are a task-level feedback method and a method that path is dynamically replaned in realtime while the autonomous vehicles are moving by means of an optimal algorithm for existing multiple path-planning. However, such methods cause malfunctions and inefficiency in the sense of time and energy, and path-planning should be dynamically replanned in realtime. To solve these problems, we propose multiple path-planning using modified genetic algorithm and fuzzy inference system and show the performance with autonomous vehicles. For experiment, we designed and built two autonomous mobile vehicles that equipped with the same driving control part used in actual autonomous forklift, and test the proposed multiple path-planning algorithm. Experimental result that actual autonomous mobile vehicle, we verified that fast optimized path-planning and efficient collision avoidance are possible.
Keywords
autonomous robot; autonomous forklift; multiple path-planning; fuzzy inference system;
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