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一种通过融合接近传感器数据构建障碍物检测模型的神经网络方法。

A Neural Network Approach for Building An Obstacle Detection Model by Fusion of Proximity Sensors Data.

作者信息

Farias Gonzalo, Fabregas Ernesto, Peralta Emmanuel, Vargas Héctor, Hermosilla Gabriel, Garcia Gonzalo, Dormido Sebastián

机构信息

Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2147, Valparaíso 2362804, Chile.

Departamento de Informática y Automática, Universidad Nacional de Educación a Distancia, Juan del Rosal 16, 28040 Madrid, Spain.

出版信息

Sensors (Basel). 2018 Feb 25;18(3):683. doi: 10.3390/s18030683.

DOI:10.3390/s18030683
PMID:29495338
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5877112/
Abstract

Proximity sensors are broadly used in mobile robots for obstacle detection. The traditional calibration process of this kind of sensor could be a time-consuming task because it is usually done by identification in a manual and repetitive way. The resulting obstacles detection models are usually nonlinear functions that can be different for each proximity sensor attached to the robot. In addition, the model is highly dependent on the type of sensor (e.g., ultrasonic or infrared), on changes in light intensity, and on the properties of the obstacle such as shape, colour, and surface texture, among others. That is why in some situations it could be useful to gather all the measurements provided by different kinds of sensor in order to build a unique model that estimates the distances to the obstacles around the robot. This paper presents a novel approach to get an obstacles detection model based on the fusion of sensors data and automatic calibration by using artificial neural networks.

摘要

接近传感器广泛应用于移动机器人的障碍物检测。这种传感器的传统校准过程可能是一项耗时的任务，因为它通常是通过手动且重复的识别方式来完成的。由此产生的障碍物检测模型通常是非线性函数，对于连接到机器人的每个接近传感器可能会有所不同。此外，该模型高度依赖于传感器类型（如超声波或红外线）、光强度的变化以及障碍物的属性，如形状、颜色和表面纹理等。这就是为什么在某些情况下，收集不同类型传感器提供的所有测量数据以构建一个估计机器人周围障碍物距离的独特模型可能会很有用。本文提出了一种基于传感器数据融合和使用人工神经网络进行自动校准来获得障碍物检测模型的新方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f42/5877112/de3ce57985af/sensors-18-00683-g020.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f42/5877112/3e18e1cbda4e/sensors-18-00683-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f42/5877112/ea708723d525/sensors-18-00683-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f42/5877112/bec61dadd775/sensors-18-00683-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f42/5877112/556342e64e52/sensors-18-00683-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f42/5877112/cf3b608252fe/sensors-18-00683-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f42/5877112/27f5550a762d/sensors-18-00683-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f42/5877112/4bfb61a2d69b/sensors-18-00683-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f42/5877112/90a7a4429b98/sensors-18-00683-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f42/5877112/e65183887d58/sensors-18-00683-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f42/5877112/414d8d6e2210/sensors-18-00683-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f42/5877112/0b5cd7a3cd56/sensors-18-00683-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f42/5877112/b17b2e01e494/sensors-18-00683-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f42/5877112/a665b569a90a/sensors-18-00683-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f42/5877112/e4aedf940b2d/sensors-18-00683-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f42/5877112/e9339360c3ed/sensors-18-00683-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f42/5877112/db16ec1d9fbc/sensors-18-00683-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f42/5877112/05f5e8ef94a5/sensors-18-00683-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f42/5877112/eeedf84abf7d/sensors-18-00683-g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f42/5877112/2ad59f8a6835/sensors-18-00683-g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f42/5877112/de3ce57985af/sensors-18-00683-g020.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f42/5877112/3e18e1cbda4e/sensors-18-00683-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f42/5877112/ea708723d525/sensors-18-00683-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f42/5877112/bec61dadd775/sensors-18-00683-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f42/5877112/556342e64e52/sensors-18-00683-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f42/5877112/cf3b608252fe/sensors-18-00683-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f42/5877112/27f5550a762d/sensors-18-00683-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f42/5877112/4bfb61a2d69b/sensors-18-00683-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f42/5877112/90a7a4429b98/sensors-18-00683-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f42/5877112/e65183887d58/sensors-18-00683-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f42/5877112/414d8d6e2210/sensors-18-00683-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f42/5877112/0b5cd7a3cd56/sensors-18-00683-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f42/5877112/b17b2e01e494/sensors-18-00683-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f42/5877112/a665b569a90a/sensors-18-00683-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f42/5877112/e4aedf940b2d/sensors-18-00683-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f42/5877112/e9339360c3ed/sensors-18-00683-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f42/5877112/db16ec1d9fbc/sensors-18-00683-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f42/5877112/05f5e8ef94a5/sensors-18-00683-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f42/5877112/eeedf84abf7d/sensors-18-00683-g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f42/5877112/2ad59f8a6835/sensors-18-00683-g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f42/5877112/de3ce57985af/sensors-18-00683-g020.jpg

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