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Path Planning Method Based on Multi-Layer ELM Optimized A*

Chuangrong Yin, Yunfeng Xia, Rui Yang, Zhibing Yuan, Lunjiong Li

Year
2021
Citations
2

Abstract

Global path planning is considered as a basic problem of mobile robot. In this paper, an optimized A* algorithm based on multi-layer Extreme Learning Machine (named as ELM-A *) is proposed to solve the problem of path planning. Taking the environment and the path point information as input, the direction of the next search node is predicted quickly by multi-layer ELM. Then the output direction of multi-layer ELM is used to calculate the cost function of A* algorithm. This method can determine accurate search direction by estimating the influence of obstacles. Simulation results show that compared with RD, RA*and Greedy algorithm, the proposed algorithm reduces the number of traversed nodes and shows better time efficiency stability.

Keywords

Motion planningExtreme learning machineComputer sciencePath (computing)Layer (electronics)Node (physics)Greedy algorithmPoint (geometry)Mathematical optimizationAlgorithm

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