Research on path planning of mobile robot based on improved ant colony algorithm

被引:0
|
作者
Jiang M. [1 ]
Wang F. [1 ]
Ge Y. [1 ]
Sun L. [1 ]
机构
[1] College of Electrical Engineering, Anhui Polytechnic University, Wuhu
关键词
Ant colony algorithm; Deadlock; Mobile robot; Path planning;
D O I
10.19650/j.cnki.cjsi.J1804429
中图分类号
学科分类号
摘要
The ant colony algorithm is slow in convergence and easy to fall into local optimal value in complex environment. To solve these problems, an improved ant colony optimization algorithm is proposed. The position information of the starting point and the target point are utilized to select the global favorable region. In this way, the initial pheromone concentration is increased and the efficiency of early ant search is improved. The obstacle avoidance strategy is added to avoid ant blind search. A large number of cross paths are generated and the number of ant deadlocks is effectively reduced. Based on the pseudorandom transfer strategy of dynamic parameter control, the global performance of the algorithm is improved. The updating principle of high quality ant pheromone and adjusting the volatility coefficient adaptively are proposed. The second path planning is carried out to optimize the path and reduce the loss of energy consumption of mobile robots. Experimental results show that the algorithm has the feature of higher global searching ability, faster convergence speed and higher working efficiency of mobile robot. The proposed algorithm is verified to be effective and superior. © 2019, Science Press. All right reserved.
引用
收藏
页码:113 / 121
页数:8
相关论文
共 19 条
  • [1] Chen Y.J., Wang Y.N., Tan J.H., Et al., Incremental sampling path planning for service robot based on local environments, Chinese Journal of Scientific Instrument, 38, 5, pp. 1093-1100, (2017)
  • [2] Liu E.H., Yao X.F., Lan H.Y., Et al., AGV dynamic path planning based on improved genetic algorithm and its implementation, Computer Integrated Manufacturing Systems, 24, 6, pp. 1455-1467, (2018)
  • [3] Juang C.F., Yeh Y.T., Multiobjective evolution of biped robot gaits using advanced continuous ant-colony optimized recurrent neural networks, IEEE Transactions on Cybernetics, 48, 6, pp. 1910-1922, (2018)
  • [4] Xu C.P., Lv Y., Huang X.J., Et al., On-line test route optimization of digital microfluidic chip based on particle swarm optimization, Journal of Electronic Measurement and Instrumentation, 31, 8, pp. 1192-1199, (2017)
  • [5] Liu H.R., Sun M.T., Li L., Et al., Study on Bayesian network structure learning algorithm based on ant colony node order optimization, Chinese Journal of Scientific Instrument, 38, 1, pp. 143-150, (2017)
  • [6] Dorigo M., Gambardella L.M., Ant colony system: A cooperative learning approach to the traveling salesman problem, IEEE Transactions on Evolutionary Computation, 1, 1, pp. 53-66, (1997)
  • [7] You X.M., Liu S.H., Lv J.Q., Ant colony algorithm based on dynamic search strategy and its application on path planning of robot, Control and Decision, 32, 3, pp. 552-556, (2017)
  • [8] Wang J.J., Liu J.K., Huang X.J., Et, Al, Test path scheduleing of digital microfluidic biochips based on combined genetic and ant colony algorithm, Journal of Electronic Measurement and Instrumentation, 31, 8, pp. 1183-1191, (2017)
  • [9] Qu H., Huang L.W., Ke X., Research of improved ant colony based robot path planning under dynamic environment, Journal of University of Electronic Science and Technology of China, 44, 2, pp. 260-265, (2015)
  • [10] Zhang C., Ling Y.Z., Chen M.Y., Path planning of mobile robot based on an improved ant colony algorithm, Journal of Electronic Measurement and Instrumentation, 30, 11, pp. 30-37, (2016)