Wei-Cheng Wang, Chow-Yong Ng, Rongshun Chen*
Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 991-1006, 2022, DOI:10.32604/iasc.2022.022067
Abstract Path planning is intrinsically regarded as a multi-objective optimization problem (MOOP) that simultaneously optimizes the shortest path and the least collision-free distance to obstacles. This work develops a novel optimized approach using the genetic algorithm (GA) to drive the multi-objective evolutionary algorithm (MOEA) for the path planning of a mobile robot in a given finite environment. To represent the positions of a mobile robot as integer-type genes in a chromosome of the GA, a grid-based method is also introduced to relax the complex environment to a simple grid-based map. The system architecture is composed of a mobile robot, embedded with… More >
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