Applying support vector machines optimized by genetic algorithm for estimating the spatial distribution of mean annual precipitation

Achite, Mohammed; Tsangaratos, Paraskevas; Ilia, Ioanna; Toubal, Abderrezak Kamel

doi:10.1007/s12517-021-07006-5

Cited by 2 publications

(1 citation statement)

References 72 publications

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“…The traditional path planning algorithms include Dijkstra algorithm, A * algorithm, and artificial potential field method [3][4][5][6][7]. A series of intelligent bionic algorithms gradually emerge with the increase of the complexity of the working space of mobile robots, such as genetic algorithm, particle swarm algorithm, artificial bee colony algorithm, and so on [8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Path Planning of Mobile Robot Based on Improved PRM Based on Cubic Spline

Gong

Tan

et al. 2022

Wireless Communications and Mobile Computing

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In view of the shortcomings of low search efficiency and many path turning points of Probabilistic Roadmaps (PRM), a bidirectional search PRM global path planning algorithm is proposed. The algorithm improves the search connection rules by using the positive and negative directions to search the path alternately, so that the connection of unnecessary nodes reduces, thereby speeding up the efficiency of path planning. Besides, the algorithm incorporates cubic spline interpolation. That will increase the smoothness of path planning and ensure that the mobile robot can realize the path planning task more smoothly and safely. The simulation results show that the improved algorithm can effectively improve the convergence speed and path smoothness of the algorithm. Finally, the improved algorithm is applied to the actual mobile robot navigation experiment. The experimental results have proven that the path planning strategy was able to a superior advantage over traditional PRM in path quality and computational time.

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