Abstract:The A* algorithm has been widely investigated and applied in path planning problems, but it does not fully consider the safety and smoothness of the path. Therefore, an improved A* algorithm is presented in this paper. Firstly, a new environment modeling method is proposed in which the evaluation function of A* algorithm is improved by taking the safety cost into account. This results in a safer path which can stay farther away from obstacles. Then a new path smoothing method is proposed, which introduces a path evaluation mechanism into the smoothing process. This method is then applied to smoothing the path without safety reduction. Secondly, with respect to path planning problems in complex terrains, a complex terrain environment model is established in which the distance and safety cost of the evaluation function of the A* algorithm are converted into time cost. This results in a unification of units as well as a clarity in their physical meanings. The simulation results show that the improved A* algorithm can greatly improve the safety and smoothness of the planned path and the movement time of the robot in complex terrain is greatly reduced.
Population spatial migration tendency forecasting is very important for research of spatial demography. Statistical and artificial intelligence (soft computing) based approaches are too complex to be used for time series prediction. This paper presents Fourier series grey model (FGM) integrating prediction method including grey model (GM) and Fourier series to predict the trend of Jiangsu Provinces migration in China. There are two parts of forecast. The first one is to build a grey model from a series of data, and the other uses the Fourier series to refine the residuals produced by the mentioned model. It is evident that the proposed approach gets the better result performance in studying the population migration. Satisfactory results have been obtained, which improve GM reached when only GM was used for the population spatial migration tendency forecasting.
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