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Journal|[J]Journal of Autonomous IntelligenceVolume 3, Issue 1. 2020.

MT一种基于朴素贝叶斯分类器的避障方法

An Obstacle Avoidance Approach Based on Naive Bayes Classifier

作者: Peiqiao Shang; Wenqian Li
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DOI: 10.32629/jai.v3i1.139

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Frontier Scientific Publishing

Frontier Scientific Publishing(Open Access)

摘要 / Abstract

MT

避障在移动机器人中起着重要的作用。然而,传统的避障方法很难通过边缘检测区分多个障碍物。本文对传统避障方法进行改进,实现多障碍物避障功能。在实现过程中,采用LiDAR代替相机,降低了处理图像噪声的难度,实现了可靠的障碍物检测。即使在复杂的环境中,它也能准确地检测最近障碍物的边界,并执行避障。在避障预测方面,基于机器人速度、障碍物左边界和障碍物右边界三个属性,通过朴素贝叶斯分类器进行模型训练。在训练过程中,对数据集进行扩展,以增强分类器模型的准确性。当机器人前进时,改进方法使机器人能够以较高的速度运动。仿真结果表明了先进避障方法的可行性。

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原文

Obstacle avoidance plays an important role in mobile robot. However, the traditional methods of obstacle avoidance have difficulty in distinguishing multiple obstacles by edge detection. In this paper, the traditional obstacle avoidance methods are improved to realize the function of multi-obstacle avoidance. Regarding the implementation process, the LiDAR is used instead of the camera, which reduces the difficulty of handling image noise and achieves reliable obstacle detection. It can accurately detect the borders of the nearest obstacle even in complex environments and perform obstacle avoidance. Regarding the obstacle avoidance prediction, the model training is performed through the Naive Bayes classifier based on the three attributes of the velocity of the robot, the left boundary of the obstacle and the right boundary of the obstacle. In the training process, dataset was expanded to enhance the accuracy of classifier model. When the robot goes forward, the improved method enables the robot to move at a higher velocity. The results show the feasibility of advanced obstacle avoidance method by simulation.

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关键词 / Keywords

MT
机器人避障; 朴素贝叶斯; LiDAR; Gazebo Simulation

原文
Robot Obstacle Avoidance; Naive Bayes; LiDAR; Gazebo Simulation

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引证文献 / Cited by（被引用的文献）：引用本文作为参考依据的文献

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