NORTH ATLANTIC UNIVERSITY UNION
In this paper, the authors propose Genetic Algorithms (GAs) for path Autonomous Mobile Vehicle (AMV). This approach has an advantage of adaptively such that the GA works perfectly even if an environment is unknown. First, they present a software implementation GA path planning in a terrain. The results gotten of the GA on randomly generated terrains are very satisfactory and promising. Second, they discuss extensions of the GA for solving both paths planning and trajectory planning using a single Static Random Access Memory (SRAM) for Field Programmable Gate Array (FPGA).