Robot path planning based on improved artificial fish swarm algorithm and MAKLINK graph

被引：0
|
作者：
Guo W. [1 ]
Qin G.-X. [1 ]
Wang L. [1 ]
Sun R.-J. [1 ]
机构：
[1] College of Mechanical Engineering, Tianjin University, Tianjin
来源：
Guo, Wei (wguo@tju.edu.cn) | 1600年 / Northeast University卷 / 35期
关键词：
Artificial fish swarm algorithm; MAKLINK graph; Mobile robot; MS algorithm; Path planning;
D O I：
10.13195/j.kzyjc.2019.0030
中图分类号：
学科分类号：
摘要：
A global path planning method, based on the improved artificial fish swarm algorithm (IAFSA) and MAKLINK graph, is proposed to solve the global path planning problem in the two-dimensional static environment. Lorentzian function and normal distribution function are chosen as adaptive operators of step and visual, the exponential decreasing inertia weighting factor is also introduced, which can improve the convergence speed and accuracy of the AFSA algorithm. The MS algorithm is combined with the IAFSA algorithm to calculate for two steps. The optimal path optimized by the IAFSA algorithm is selected as the global optimal path, which solves the problem of that the previous algorithm can only get the approximate global optimal path in the MAKLINK graph. The simulation results show the feasibility and effectiveness of the proposed improved algorithm. © 2020, Editorial Office of Control and Decision. All right reserved.
引用
收藏
页码：2145 / 2152
页数：7
相关论文
共 21 条
  • [1] Zhu D Q, Yan M Z, Survey on technology of mobile robot path planning, Control and Decision, 25, 7, pp. 961-967, (2010)
  • [2] Zhao X, Wang Z, Huang C K, Mobile robot path planning based on an improved A* algorithm, Robot, 40, 6, pp. 903-910, (2018)
  • [3] Zhang C F, Wang C L., Service robot path planning based on improved Dijkstra algorithm, i-CREATe 2018 Proceedings of the 12th International Convention on Rehabilitation Engineering and Assistive Technology, pp. 77-80, (2018)
  • [4] Kala R., Code for robot path planning using rapidly-exploring random trees
  • [5] Kala R., Code for Robot path planning using probabilistic roadmap
  • [6] Chen T B, Zhang Q S., Robot motion planning based on improved artificial potential field, Proceedings of the 3rd International Conference on Computer Science and Network Technology, pp. 1208-1211, (2013)
  • [7] Ni J, Wang K, Huang H, Et al., Robot path planning based on an improved genetic algorithm with variable length chromosome, The 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), pp. 145-149, (2016)
  • [8] Liu J H, Yang J G, Liu H P, An improved ant colony algorithm for robot path planning, Soft Computing, 21, 19, pp. 5829-5839, (2017)
  • [9] Tharwat A, Elhoseny M, Hassanien A, Intelligent bézier curve-based path planning model using chaotic particle swarm optimization algorithm, Cluster Computing, pp. 1-22, (2018)
  • [10] Hwu T, Wang A Y, Oros N, Adaptive robot path planning using a spiking neuron algorithm with axonal delays, IEEE Transactions on Cognitive and Developmental Systems, 10, 2, pp. 126-137, (2018)
123