Ran T, Yuan L, Zhang J B
School of Mechanical Engineering, Xinjiang University, Urumqi, China.
School of Mechanical Engineering, Xinjiang University, Urumqi, China; Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing, China.
ISA Trans. 2021 Mar;109:389-400. doi: 10.1016/j.isatra.2020.10.023. Epub 2020 Oct 12.
Only vision-based navigation is the key of cost reduction and widespread application of indoor mobile robot. Consider the unpredictable nature of artificial environments, deep learning techniques can be used to perform navigation with its strong ability to abstract image features. In this paper, we proposed a low-cost way of only vision-based perception to realize indoor mobile robot navigation, converting the problem of visual navigation to scene classification. Existing related research based on deep scene classification network has lower accuracy and brings more computational burden. Additionally, the navigation system has not yet been fully assessed in the previous work. Therefore, we designed a shallow convolutional neural network (CNN) with higher scene classification accuracy and efficiency to process images captured by a monocular camera. Besides, we proposed an adaptive weighted control (AWC) algorithm and combined with regular control (RC) to improve the robot's motion performance. We demonstrated the capability and robustness of the proposed navigation method by performing extensive experiments in both static and dynamic unknown environments. The qualitative and quantitative results showed that the system performs better compared to previous related work in unknown environments.
仅基于视觉的导航是降低室内移动机器人成本并实现广泛应用的关键。考虑到人工环境的不可预测性，深度学习技术凭借其强大的图像特征抽象能力可用于执行导航。在本文中，我们提出了一种仅基于视觉感知的低成本方法来实现室内移动机器人导航，将视觉导航问题转化为场景分类问题。现有的基于深度场景分类网络的相关研究准确性较低且带来更多计算负担。此外，导航系统在先前的工作中尚未得到充分评估。因此，我们设计了一个具有更高场景分类准确性和效率的浅层卷积神经网络（CNN）来处理单目相机捕获的图像。此外，我们提出了一种自适应加权控制（AWC）算法，并与常规控制（RC）相结合以提高机器人的运动性能。我们通过在静态和动态未知环境中进行广泛实验，展示了所提出导航方法的能力和鲁棒性。定性和定量结果表明，该系统在未知环境中比先前的相关工作表现更好。
