赵小勇, 陈钦柱, 郑鸿彦, 陈川刚. 基于道路场景理解的巡检机器人避障方法研究与应用[J]. 微电子学与计算机, 2022, 39(4): 118-127. DOI: 10.19304/J.ISSN1000-7180.2021.1077
引用本文: 赵小勇, 陈钦柱, 郑鸿彦, 陈川刚. 基于道路场景理解的巡检机器人避障方法研究与应用[J]. 微电子学与计算机, 2022, 39(4): 118-127. DOI: 10.19304/J.ISSN1000-7180.2021.1077
ZHAO Xiaoyong, CHEN Qinzhu, ZHENG Hongyan, CHEN Chuangang. Research and application of obstacle avoidance method for inspection robot based on road scene understanding[J]. Microelectronics & Computer, 2022, 39(4): 118-127. DOI: 10.19304/J.ISSN1000-7180.2021.1077
Citation: ZHAO Xiaoyong, CHEN Qinzhu, ZHENG Hongyan, CHEN Chuangang. Research and application of obstacle avoidance method for inspection robot based on road scene understanding[J]. Microelectronics & Computer, 2022, 39(4): 118-127. DOI: 10.19304/J.ISSN1000-7180.2021.1077

基于道路场景理解的巡检机器人避障方法研究与应用

Research and application of obstacle avoidance method for inspection robot based on road scene understanding

  • 摘要: 为提升巡检机器人的导航避障能力,本文将深度学习技术应用于场景识别中,提出了一种基于道路场景理解的巡检机器人自主避障方法(Road Scene Understanding Net, RSUNet).该方法首先将多层卷积与LeakyReLU激活函数相结合,并以残差结构和金字塔上采样结构的方式,构建高精度道路场景理解网络;其次,设计自适应控制模块来对比前后两帧图像的深层特征信息,并根据特征差异大小自动控制后续网络层的特征计算,避免相似特征重复提取,保障网络效率;最后,将场景理解结果转化为巡检机器人前方不同区域的目标信息,通过分析机器人前方可行道路区域以及障碍物所处位置来指导巡检机器人实现导航避障.实验结果表明,所提方法有效的平衡了场景理解网络的识别精度与计算效率,同时,在实际变电站巡检机器人平台上,该方法也表现出较强的适应性,并能准确高效的为机器人提供场景信息,辅助机器人完成实时自主避障.

     

    Abstract: In order to improve the navigation obstacle avoidance ability to the inspection robot, deep learning technology is applied to scene recognition in this paper, and an autonomous obstacle avoidance method of inspection robot based on road scene understanding is proposed. Firstly, a high-precision road scene understanding network is constructed by combining multi-layer convolution with LeakyReLU activation function, and in the way of residual structure and sampling structure on pyramid; Secondly, an adaptive control module is designed to compare the deep feature information of the two images before and after, and automatically control the feature calculation of the subsequent network layer according to the feature difference, so as to avoid repeated extraction of similar features and ensure the network efficiency; Finally, the scene understanding results are transformed into target information in different areas in front of the inspection robot, and the feasible road area in front of the robot and the location of obstacles are analyzed to guide the inspection robot to realize navigation and obstacle avoidance. Experiments show that the proposed method effectively balances the recognition accuracy and calculation efficiency of the scene understanding network. At the same time, on the actual substation inspection robot platform, the method also shows strong adaptability, and can accurately and efficiently provide scene information for the robot and assist the robot to realize real-time autonomous obstacle avoidance.

     

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