An Occupancy Grids Building Method with Sonar Sensors Based on Improved Neural Network Model

  • Authors:

  • Hongshan Yu;Yaonan Wang;Jinzhu Peng

  • Affiliations:

  • College of Electrical and Information Engineering, Hunan University, Changsha Hunan, 410082, P.R. China;College of Electrical and Information Engineering, Hunan University, Changsha Hunan, 410082, P.R. China;College of Electrical and Information Engineering, Hunan University, Changsha Hunan, 410082, P.R. China

  • Venue:
  • ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
  • Year:
  • 2007

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Abstract

This paper presents an improved neural network model interpretating sonar readings to build occupancy grids of mobile robot. The proposed model interprets sensor readings in the context of their space neighbors and relevant successive history readings simultaneously. Consequently the presented method can greatly weaken the effects by multiple reflections or specular reflection. The output of the neural network is the probability vector of three possible status(empty, occupancy, uncertainty) for the cell. As for sensor readings integration, three probabilities of cell's status are updated by the Bayesian update formula respectively, and the final status of cell is defined by Max-Min principle.Experiments performed in lab environment has shown occupancy map built by proposed approach is more consistent, accurate and robust than traditional method while it still could be conducted in real time.