fa مسیریابی روبات متحرک با استفاده از روشی جدید مبتنی بر الگوریتم ژنتیک با طول آرایه متغیر سایر موضوعاتی که به مرزهای دانش در مهندسی صنایع و تولید کمک می کند Other related Industrial and production reserach subjects in which has direct relation to the state-of-the art of the IE پژوهشي Research <p align="right" > <p> <i> Being one of the major research fields in the robotics discipline, the robot motion planning problem deals with finding an obstacle-free start-to-goal path for a robot navigating among workspace obstacles. Such a problem is also encountered in path planning of intelligent vehicles and Automatic Guided Vehicles (AGVs). Traditional (exact) algorithms have failed to solve the problem effectively since it is proven that the complexity of the problem is NP-hard. Consequently, several heuristic algorithms have been developed for solving the problem, among which the Genetic Algorithms (GA) evolutionary approach has been increasingly utilized. In this paper, a new GA-based method has been developed to improve the drawbacks of current GA methods regarding fixed chromosome string size and limitations in the crossover and mutation operators. In this method, first the workspace is tessellated via the Delaunay triangulation, and a set of paths are created using the powerful Rapidly-exploring Random Trees (RRT) and smoothed. As a result, a population of variable-length chromosomes is obtained, which is a novelty in the field. Next, these solutions are improved further by using new selection, crossover and mutation operators, in which the triangulation plays a pivotal role. Extensive Simulations of the algorithm showed that the obtained path lengths are averagely 25% shorter than those found by the pure RRT method. </i></p> Robot Motion Planning,Genetic Algorithms,Delaunay Triangulation,Rapidly-exploring Random Trees 99 111 http://ijiepm.iust.ac.ir/browse.php?a_code=A-10-135-4&slc_lang=fa&sid=1 Ellips Masehian masehian@modares.ac.ir 180031947532846003115 180031947532846003115 Yes Assistant Professor, Industrial Engineering Department, Tarbiat Modares University Farnaz Barzinpour 180031947532846003116 180031947532846003116 No Assistant Professor, School of Industrial Engineering, Iran University of Science and Technology Samira Saedi 180031947532846003117 180031947532846003117 No M.Sc. graduate, School of Industrial Engineering, Iran University of Science and Technology