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Article Navigation >  Journal of University of Electronic Science and Technology of China  > 2015  >  44(2): 260-265

QU Hong, HUANG Li-wei, KE Xing. Research of Improved Ant Colony Based Robot Path Planning Under Dynamic Environment[J]. Journal of University of Electronic Science and Technology of China, 2015, 44(2): 260-265. doi: 10.3969/j.issn.1001-0548.2015.02.017
Citation: QU Hong, HUANG Li-wei, KE Xing. Research of Improved Ant Colony Based Robot Path Planning Under Dynamic Environment[J]. Journal of University of Electronic Science and Technology of China, 2015, 44(2): 260-265. doi: 10.3969/j.issn.1001-0548.2015.02.017
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Research of Improved Ant Colony Based Robot Path Planning Under Dynamic Environment

doi: 10.3969/j.issn.1001-0548.2015.02.017
  • 1.

    Computing Intelligence Lab,University of Electronic Science and Technology of China Chengdu 611731

  • Received Date: 2014-03-20
  • Rev Recd Date: 2014-05-30
  • Publish Date: 2015-04-15
  • This paper presents an improved ant colony algorithm for mobile robot path planning under dynamic complex conditions based on prior knowledge of global static environment. On the basis of conventional ant colony algorithm, by adjusting the transition probability, limiting the bounds of pheromone, and introducing relevant strategy to solve the deadlock problem, the improved ant colony algorithm not only can avoid the blindness of early planning and increase the diversity of solutions, but also can improve global search capability of the algorithm, and further reduce the possibility of algorithm prematurity as well. During the planning process, according to the direction changes of the dynamic obstacles, corresponding collision avoidance strategies are put forward. The Follw_wall behavior is introduced for unexpected situations in the environment. Simulation results show that the proposed algorithm is superior to conventional ant colony algorithm. It can effectively guide the mobile robot to avoid dynamic obstacles. Thus obtains a collision free optimal or suboptimal path, which adapts to the changes of the environment more effectively.
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  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

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Research of Improved Ant Colony Based Robot Path Planning Under Dynamic Environment

doi: 10.3969/j.issn.1001-0548.2015.02.017
  • 1. Computing Intelligence Lab,University of Electronic Science and Technology of China Chengdu 611731
  • Received Date: 2014-03-20
  • Rev Recd Date: 2014-05-30
  • Publish Date: 2015-04-15

Abstract: This paper presents an improved ant colony algorithm for mobile robot path planning under dynamic complex conditions based on prior knowledge of global static environment. On the basis of conventional ant colony algorithm, by adjusting the transition probability, limiting the bounds of pheromone, and introducing relevant strategy to solve the deadlock problem, the improved ant colony algorithm not only can avoid the blindness of early planning and increase the diversity of solutions, but also can improve global search capability of the algorithm, and further reduce the possibility of algorithm prematurity as well. During the planning process, according to the direction changes of the dynamic obstacles, corresponding collision avoidance strategies are put forward. The Follw_wall behavior is introduced for unexpected situations in the environment. Simulation results show that the proposed algorithm is superior to conventional ant colony algorithm. It can effectively guide the mobile robot to avoid dynamic obstacles. Thus obtains a collision free optimal or suboptimal path, which adapts to the changes of the environment more effectively.

QU Hong, HUANG Li-wei, KE Xing. Research of Improved Ant Colony Based Robot Path Planning Under Dynamic Environment[J]. Journal of University of Electronic Science and Technology of China, 2015, 44(2): 260-265. doi: 10.3969/j.issn.1001-0548.2015.02.017
Citation: QU Hong, HUANG Li-wei, KE Xing. Research of Improved Ant Colony Based Robot Path Planning Under Dynamic Environment[J]. Journal of University of Electronic Science and Technology of China, 2015, 44(2): 260-265. doi: 10.3969/j.issn.1001-0548.2015.02.017
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