Electronic Science and Technology ›› 2020, Vol. 33 ›› Issue (1): 13-18.doi: 10.16180/j.cnki.issn1007-7820.2020.01.003

Previous Articles     Next Articles

Research on Robot Path Planning Based on Improved Ant Colony Algorithm

LIU Yongjian1,ZENG Guohui1,HUANG Bo1,LI Xiaobin2   

  1. 1. School of Electronic and Electrical Engineering,Shanghai University of Engineering Science,Shanghai 201620,China
    2. School of Electrical and Electronic Engineering,Shanghai Institute of Technology,Shanghai 200235,China
  • Received:2018-12-17 Online:2020-01-15 Published:2020-03-12
  • Supported by:
    National Natural Science Foundation of China(61603242);Open Task of Jiangxi Province Economic Crime Detection and Prevention Technology Collaborative Innovation Center(JXJZXTCX-030);Mechanical and Electronic Engineering Discipline Construction Project(2018xk-A-03)


Aiming at the problem that the traditional ant colony algorithm had slow convergence speed and easy to fall into local optimum, an improved ant colony algorithm was proposed. Based on the traditional A * algorithm, the valuation function of the traditional A * algorithm was improved. which was further introduced into the ant colony algorithm. The modified heuristic function η was proposed to increase the attraction of the target point to the path search and improve the convergence speed. The pheromone volatilization factor ρ was improved, and the pheromone volatilization factor was dynamically changed, which promoted the global search ability of the algorithm and prevent it from falling into local optimum. The simulation results showed that the improved ant colony algorithm was nearly 50% faster than the traditional ant colony algorithm in convergence rate, and was superior to the traditional ant colony algorithm in the shortest path, which proved the effectiveness of the improved algorithm.

Key words: ant colony algorithm, A * algorithm, robot, heuristic factor, pheromonevolatil, path planning

CLC Number: 

  • TP242.6