Path planning of mobile robot based on learning mechanism ant colony algorithm
A learning mechanism ant colony algorithm was proposed for the path planning problem of mobile robot in U-shaped obstacle environment.Firstly,to solve the problem of long algorithm running time,neighborhood removal was introduced to discard poor and symmetric paths.Secondly,to solve the problem of slow convergence speed,taboo strategies were applied to enable ants to quickly escape U-shaped obstacles.Then,to solve the problem of path deadlock,a learning mechanism was proposed to continu-ously discard deadlocked paths.Finally,a simulation comparison was conducted between the proposed algorithm and other im-proved algorithms.The results showed that the learning mechanism ant colony algorithm not only shortened the running time com-pared to the control group algorithm,but also improved the convergence speed,which verifies superiority of the algorithm.
国家科技期刊平台
NSTL
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