Path planning and smoothing based on quantum-behaved fireworks algorithm for mobile robot

被引：5
|
作者：
Xue Y.-Y. [1 ,2 ,3 ]
Zhang X.-Y. [1 ,2 ,3 ]
Zhang G.-L. [1 ,2 ,3 ]
Jia S.-M. [1 ,2 ,3 ]
机构：
[1] Faculty of Information Technology, Beijing University of Technology, Beijing
[2] Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing
[3] Engineering Research Center of Digital Community, Ministry of Education, Beijing
来源：
Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2019年 / 36卷 / 09期
基金：
中国国家自然科学基金;
关键词：
Artificial potential field; Average filter; Fireworks algorithm; Path planning; Path smoothing; Quantum-behaved;
D O I：
10.7641/CTA.2018.80473
中图分类号：
学科分类号：
摘要：
In view of the global path planning problem of mobile robot, a path planning method based on quantumbehaved fireworks algorithm (QFWA) is proposed. A quantum-behavior fireworks explosion method is added to the fireworks algorithm (FWA). The method has strong local search ability when the fitness value is close to the global optimal fitness value, and has strong global search capability when the fitness value is relatively bigger. The algorithm has improved the diversity of fireworks explosion and the algorithm convergence speed. Test the improved algorithm with Benchmark test function, and contrast the result with other algorithm, the results shows that the improved algorithm has the best optimization effect. The QFWA algorithm is applied to the path planning of mobile robot, and using a method of the artificial potential field algorithm based on the average filter to smooth the planned path. The effectiveness and feasibility of the improved algorithm is verified by simulation results. © 2019, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.
引用
收藏
页码：1398 / 1408
页数：10
相关论文
共 26 条
  • [1] Zhao J., Gao X., Fu X., Et al., Improved ant colony algorithm of path planning for mobile robot, Control Theory & Applications, 28, 4, pp. 457-461, (2011)
  • [2] Zhu D., Yan M., Survey on technology of mobile robot path planning, Control and Decision, 30, 2, pp. 229-232, (2013)
  • [3] Zhang P.C., Xiong C., Li W.K., Et al., Path planning for mobile robot based on modified rapidly exploring random tree method and neural network, International Journal of Advance Robotic Systems, 15, 3, pp. 1-8, (2018)
  • [4] Doostie S., Hoshiar A.K., Nazarahari M., Et al., Optimal path planning of multiple nanoparticles in continuous environment using a novel adaptive genetic algorithm, Precision Engineering-Journal of The International Societies for Precision Engineering and Nanotechnology, 53, pp. 65-78, (2018)
  • [5] Oleiwi B.K., Al-Jarrah R., Roth H., Et al., Multi objective optimization of trajectory planning of non-holonomic mobile robot in dynamic environment using enhanced GA by fuzzy motion control and A*, Icnnai: International Conference, 440, pp. 66-73, (2014)
  • [6] Tan Y., Introduction to Fireworks Algorithm, (2015)
  • [7] Tan Y., Zhu Y., Fireworks algorithm for optimization, International Conference on Advances in Swarm Intelligence, pp. 355-364, (2010)
  • [8] Zheng S., Janecek A., Tan Y., Enhanced fireworks algorithm, Congress on Evolutionary Computation, pp. 2069-2077, (2013)
  • [9] Zheng S., Janecek A., Li J., Et al., Dynamic search in fireworks algorithm, Congress on Evolutionary Computation, pp. 3222-3229, (2014)
  • [10] Li J., Zheng S., Tan Y., Adaptive fireworks algorithm, Congress on Evolutionary Computation, pp. 3214-3221, (2014)
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