|Table of Contents|

Application of improved adaptive genetic algorithmin mobile robot path planning(PDF)


Research Field:
Publishing date:


Application of improved adaptive genetic algorithmin mobile robot path planning
Wang LeiLi Ming
School of Mechanical and Automotive Engineering,Anhui Polytechnic University,Wuhu 241000,China
adaptive genetic algorithm path planning mobile robots artificial potential field
In order to deal with the problem of the slow convergence speed and local optima of the basic genetic algorithm(GA)in solving the robot path planning,an improved adaptive genetic algorithm(IGA)is proposed here.An artificial potential field method is employed to create the initial population,and the adaptive crossover probability and the mutation probability are designed.Meanwhile,a hybrid selection method is adopted to improve the convergence speed and the evolutionary efficiency and overcome the premature phenomenon of the basic genetic algorithm obviously.Some experiments under the grid environment verify the feasibility and effectiveness of the improved adaptive genetic algorithm in mobile robot path planning.


[1] Contreras-Cruz M A,Ayala-Ramirez V.Mobile robot path planning using artificial bee colony and evolutionary programming[J].Applied Soft Computing,2015,30:319-328.
[2]Mohanta J C,Parhi D R,Patel S K.Path planning strategy for autonomous mobile robot navigation using Petri-GA optimization[J].Computers & Electrical Engineering,2011,37(6):1058-1070.
[3]Xie Shaorong,Wu Peng.An improved path planning method based on artificial Potential field for a mobile robot[J].Cybernetics and Information Technologie,2015,15(2):181-191.
[4]Deng Lixia,Ma Xin.Multi-robot dynamic formation path planning with improved polyclonal artificial immune algorithm[J].Control and Intelligent System,2014,42(4):284-291.
Zhao Juanping,Gao Xianwen,Fu Xiuhui.Improved ant colony optimization algorithm for solving path planning problem of mobile robot[J].Journal of Nanjing University of Science and Technology,2011,35(5):637-641.
[6]Park J H.Local path planning for mobile robot using artificial neural network-potential field algorithm[J].Robotics and Computer-Integrated Manufacturing,2015,64(10):1479-1485.
[7]Zhang Hong,Chen Yanping,Chen Binqiang.Path planning for intelligent robot based on switching local evolutionary PSO algorithm[J].Assembly Automation,2016,36(2):120-126.
Shen Xiaoning,Guo Yu,Chen Qingwei,et al.Application of multi-objective optimization genetic algorithm to robot path planning[J].Journal of Nanjing University of Science and Technology,2006,30(6):659-663.
[9]Amir Hossein Karami,Maryam Hasanzadeh.An adaptive genetic algorithm for robot motion planning in 2D complex environments[J].Computers and Electrical Engineering,2015,43:317-329.
[10]Tuncer A,Yildirim M.Dynamic path planning of mobile robots with improved genetic algorithm[J].Computers & Electrical Engineering,2012,38(6):1564-1572.
[11]Imen Chaari,Anis Koubaa,Sahar Trigui.An efficient hybrid ACO-GA algorithm for solving the global path planning problem of mobile robots[J].International Journal of Advanced Robotic Systems,2014,11(1):399-412.
[12]Qu Hong,Ke Xing,Takacs A.An improved genetic algorithm with co-evolutionary strategy for global path planning of multiple mobile robots[J].Neurocomputing,2013,120(10):509-517.
Hu Chibing,Feng Wuyang.Optimization problem of mobile robot route with improved adaptive genetic algorithm[J].Journal of Lanzhou University of Technology,2011,37(5):41-45.
Hao Bo,Qin Lijuan,Jiang Mingyang.Research on the path planning methods for mobile robots based on an improved genetic algorithm[J].Computer Engineering & Science,2010,32(7):104-107.
Liu Xuxun,Cao Yang,Chen Xiaowei.Mouse colony optimization algorithm for mobile robot path planning[J].Control and Decision,2008,23(9):1060-1064.
Zhao Yifan,Li Xiaoan.Robot path planning based on artificial potential field method and attention mechanism[J].Science Technology and Engineering,2010,10(9):2094-2097.
[18]Nadia Adnan Shiltagh,Lana Dalawr Jalal.Path planning of intelligent mobile robot using modified genetic algorithm[J].International Journal of Soft Computing & Engineering,2013,3(2):31-36.
Zhang Yi,Dai Enchan,Luo Yuan.Mobile robot path planning based on an improved genetic algorithm[J].Computer Measurement & Control,2016,24(1):313-316.


Last Update: 2017-09-30