Path Planning of Quadrotors in a Dynamic Environment Using a Multicriteria Multi-Verse Optimizer
作者机构:Research Laboratory in Automatic Control(LARA)National Engineering School of Tunis(ENIT)University of Tunis El ManarTunis1002Tunisia Department of Systems EngineeringKing Fahd University of Petroleum&MineralsDhahran31261Saudi Arabia College of Engineering at Wadi AddawaserPrince Sattam Bin Abdulaziz UniversityAl-Kharj11911Saudi Arabia Department of Electrical EngineeringFaculty of EngineeringMinia UniversityMinia61517Egypt High Institute of Industrial Systems of Gabès(ISSIG)University of GabèsGabès6011Tunisia
出 版 物:《Computers, Materials & Continua》 (计算机、材料和连续体(英文))
年 卷 期:2021年第69卷第11期
页 面:2159-2180页
核心收录:
学科分类:0831[工学-生物医学工程(可授工学、理学、医学学位)] 0808[工学-电气工程] 0809[工学-电子科学与技术(可授工学、理学学位)] 08[工学] 0805[工学-材料科学与工程(可授工学、理学学位)] 0701[理学-数学] 0801[工学-力学(可授工学、理学学位)] 0812[工学-计算机科学与技术(可授工学、理学学位)]
主 题:Quadrotors path planning dynamic obstacles multi-objective optimization global metaheuristics TOPSIS decision-making Friedman statistical tests
摘 要:Paths planning of Unmanned Aerial Vehicles(UAVs)in a dynamic environment is considered a challenging task in autonomous flight control *** this work,an efficient method based on a Multi-Objective MultiVerse Optimization(MOMVO)algorithm is proposed and successfully applied to solve the path planning problem of quadrotors with moving *** a path planning task is formulated as a multicriteria optimization problem under operational *** proposed MOMVO-based planning approach aims to lead the drone to traverse the shortest path from the starting point and the target without collision with moving *** vehicle moves to the next position from its current one such that the line joining minimizes the total path length and allows aligning its direction towards the *** choose the best compromise solution among all the non-dominated Pareto ones obtained for compromise objectives,the modified Technique for Order Preference by Similarity to Ideal Solution(TOPSIS)is investigated.A set of homologous metaheuristics such as Multiobjective Salp Swarm Algorithm(MSSA),Multi-Objective Grey Wolf Optimizer(MOGWO),Multi-Objective Particle Swarm Optimization(MOPSO),and Non-Dominated Genetic Algorithm II(NSGAII)is used as a basis for the performance *** results and statistical analyses show the superiority and effectiveness of the proposed MOMVO-based planning *** obtained results are satisfactory and encouraging for future practical implementation of the path planning strategy.