变电站巡检机器人避障方法研究与应用

为提升变电站巡检机器人的导航避障能力,将深度学习技术应用于变电站场景识别中,提出了一种基于深度卷积神经网络的避障方法.该方法联合图像分类和语义分割两个分支来共同辅助机器人导航避障,分类分支通过获取图像全局信息,保证机器人正确行驶方向;而语义分割支路则根据图像局部信息以及机器人前方目标类别,指导机器人准确避障.实验结果表明,避障方法可以高效地对图像进行分类和分割,同时,在实际变电站环境中,该方法也能为巡检机器人提供有效的避障信息,实现实时自主避障.

Research and Application of Obstacle Avoidance Method for Substation Inspection Robot

In order to improve the navigation obstacle avoidance ability to the substation inspection robot, a deep convolution neural network based on deep learning technology is used to robot obstacle avoidance. This method combines image classification and semantic segmentation to assist the robot in navigation and obstacle avoidance. The classification branch obtains global image information to ensure the correct direction of the robot. The semantic segmentation branch guides the robot to avoid obstacles accurately based on the local information of the image and the target category in front of the robot. Experimental results show that the method proposed in this paper can classify and segment images effectively. At the same time, in the actual substation environment, it can also provide effective obstacle avoidance information for inspection robots and realize real-time autonomous obstacle avoidance.