School of Automation, Northwestern Polytechnical University, Xi'an 710072, China.
School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore.
Sensors (Basel). 2019 Feb 14;19(4):783. doi: 10.3390/s19040783.
With rapid advancements in artificial intelligence and mobile robots, some of the tedious yet simple jobs in modern libraries, like book accessing and returning (BAR) operations that had been fulfilled manually before, could be undertaken by robots. Due to the limited accuracies of the existing positioning and navigation (P&N) technologies and the operational errors accumulated within the robot P&N process, however, most of the current robots are not able to fulfill such high-precision operations. To address these practical issues, we propose, for the first time (to the best of our knowledge), to combine the binocular vision and Quick Response (QR) code identification techniques together to improve the robot P&N accuracies, and then construct an autonomous library robot for high-precision BAR operations. Specifically, the binocular vision system is used for dynamic digital map construction and autonomous P&N, as well as obstacle identification and avoiding functions, while the QR code identification technique is responsible for both robot operational error elimination and robotic arm BAR operation determination. Both simulations and experiments are conducted to verify the effectiveness of the proposed technique combination, as well as the constructed robot. Results show that such a technique combination is effective and robust, and could help to significantly improve the P&N and BAR operation accuracies, while reducing the BAR operation time. The implemented autonomous robot is fully-autonomous and cost-effective, and may find applications far beyond libraries with only sophisticated technologies employed.
随着人工智能和移动机器人技术的快速发展,现代图书馆中一些繁琐但简单的工作,如书籍存取(BAR)操作,以前是手动完成的,现在可以由机器人来完成。然而,由于现有定位和导航(P&N)技术的精度有限,以及机器人 P&N 过程中的操作误差累积,大多数当前的机器人无法完成这种高精度的操作。为了解决这些实际问题,我们首次(据我们所知)提出将双目视觉和快速响应(QR)码识别技术结合起来,以提高机器人的 P&N 精度,然后构建一个用于高精度 BAR 操作的自主图书馆机器人。具体来说,双目视觉系统用于动态数字地图构建和自主 P&N,以及障碍物识别和避免功能,而 QR 码识别技术则负责消除机器人操作误差和确定机械臂 BAR 操作。进行了模拟和实验来验证所提出的技术组合以及构建的机器人的有效性。结果表明,这种技术组合是有效和鲁棒的,可以显著提高 P&N 和 BAR 操作的精度,同时减少 BAR 操作时间。实现的自主机器人是完全自主和具有成本效益的,并且可能会在仅采用复杂技术的图书馆之外找到广泛的应用。