Genetic Algorithm Based Approach for Autonomous Mobile Robot Path Planning

被引:277
作者
Lamini, Chaymaa [1 ]
Benhlima, Said [1 ]
Elbekri, Ali [1 ]
机构
[1] FSM, Meknes 5000, Morocco
来源
PROCEEDINGS OF THE FIRST INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING IN DATA SCIENCES (ICDS2017) | 2018年 / 127卷
关键词
Genetic algorithm; path planning; crossover operator; navigation; mobile robot;
D O I
10.1016/j.procs.2018.01.113
中图分类号
TP18 [人工智能理论];
学科分类号
140502 [人工智能];
摘要
In this study, an improved crossover operator is suggested, for solving path planning problems using genetic algorithms (GA) in static environment. GA has been widely applied in path optimization problem which consists in finding a valid and feasible path between two positions while avoiding obstacles and optimizing some criteria such as distance (length of the path), safety (the path must be as far as possible from the obstacles)...etc. Several researches have provided new approaches used GA to produce an optimal path. Crossover operators existing in the literature can generate infeasible paths, most of these methods dont take into account the variable length chromosomes. The proposed crossover operator avoids premature convergence and offers feasible paths with better fitness value than its parents, thus the algorithm converges more rapidly. A new fitness function which takes into account the distance, the safety and the energy, is also suggested. In order to prove the validity of the proposed method, it is applied to many different environments and compared with three studies in the literature. The simulation results show that using GA with the improved crossover operators and the fitness function helps to find optimal solutions compared to other methods. (C) 2018 The Authors. Published by Elsevier B.V.
引用
收藏
页码:180 / 189
页数:10
相关论文
共 31 条
[1]
Alajlan M, 2013, 2013 INTERNATIONAL CONFERENCE ON INDIVIDUAL AND COLLECTIVE BEHAVIORS IN ROBOTICS (ICBR), P1, DOI 10.1109/ICBR.2013.6729271
[2]
[Anonymous], 2001, An Introduction to Genetic Algorithms. Complex Adaptive Systems
[3]
[Anonymous], 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation, DOI DOI 10.1109/ROBOT.1985.1087373
[4]
Back T., 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence (Cat. No.94TH0650-2), P57, DOI 10.1109/ICEC.1994.350042
[5]
Back Thomas., 2000, Evolutionary computation 1: Basic algorithms and operators, V1
[6]
Optimal path planning and execution for mobile robots using genetic algorithm and adaptive fuzzy-logic control [J].
Bakdi, Azzeddine ;
Hentout, Abdelfetah ;
Boutami, Hakim ;
Maoudj, Abderraouf ;
Hachour, Ouarda ;
Bouzouia, Brahim .
ROBOTICS AND AUTONOMOUS SYSTEMS, 2017, 89 :95-109
[7]
Chen Andrew, 2017, 2017 IEEE International Ultrasonics Symposium (IUS), DOI 10.1109/ULTSYM.2017.8091914
[8]
Multi-objective path planning in discrete space [J].
Davoodi, Mansoor ;
Panahi, Fatemeh ;
Mohades, Ali ;
Hashemi, Seyed Naser .
APPLIED SOFT COMPUTING, 2013, 13 (01) :709-720
[9]
Giralt G., 1990, Autonomous Robot Vehicles, P420
[10]
Hachour O., 2008, INT J SYSTEMS APPL E, V2, P178