Abstract
This paper presents a modified natural selection based quantum behaved particle swarm optimization (SelQPSO) algorithm for the path planning of mobile robot vehicles. To ensure the global searching and the high efficiency of the QPSO’s searching process, the particle swarms are sorted by fitness and the group of the particles with worst fitness are replaced by the group with best fitness in each iteration of the whole procedure. The effectiveness and feasibility of this algorithm are demonstrated by the results from numerical experiments on well-known benchmark functions. Then, this algorithm is employed to estimate the basic parameters of the mobile robot path planning in the barrier free environment. The convergency of the estimation method versus particle numbers and iteration times is studied with variation of particle dimension. A unary linear regression equation taking the particle number, maximum generation and particle dimension as variables is formulated. The results from experiments for optimal path planning of a mobile robot in complex environment justifies the estimation method.
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Tokgo, M., Li, R. (2014). Estimation Method for Path Planning Parameter Based on a Modified QPSO Algorithm. In: Agre, G., Hitzler, P., Krisnadhi, A.A., Kuznetsov, S.O. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2014. Lecture Notes in Computer Science(), vol 8722. Springer, Cham. https://doi.org/10.1007/978-3-319-10554-3_27
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DOI: https://doi.org/10.1007/978-3-319-10554-3_27
Publisher Name: Springer, Cham
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