摘 要
在物联网（IoT）和人工智能（AI）技术的推动下，智能设备在商业和制造业中的应用日益广泛。本研究旨在探索一种集成无线射频识别（RFID）技术能够同步定位与地图构建技术的智能购物车系统，以提升超市顾客的购物体验。本系统采用超宽带（UWB）技术，实现了购物车在大型超市环境中的自主跟随功能，通过结合多种传感器结合多种AI技术，使智能购物小车具备了快速结账、智能导航和自动避障的能力。
在研究中，使用了多种方式进行程序开发以及案例研究。在环境感知中使用激光SLAM进行地图构建，通过多种滤波算法进行自我位置确认。测试结果表明识别度以及建图后的结果良好，验证了应用能力。
本研究展示了通过整合RFID技术、UWB技术、激光SLAM技术配合AI算法，可以显著提升智能购物车的性能，实现集快速结账、自主跟随、智能导航和自动避障等关键功能。这些功能的实现为大型超市的运营添加一种智能可行方案。未来的工作将集中在进一步优化系统性能和探索更多商业应用场景。
关键词：RFID；SLAM；智能购物小车；UWB；
ABSTRACT
The proliferation of Internet of Things (IoT) and Artificial Intelligence (AI) technologies has led to an increasingly widespread application of smart devices in the commercial and manufacturing sectors. This study aims to explore an integrated system that combines Radio Frequency Identification (RFID) technology with Simultaneous Localization and Mapping (SLAM) technology to enhance the shopping experience of supermarket customers. The system employs Ultra-Wideband (UWB) technology to achieve autonomous following capabilities of shopping carts in large supermarkets. By integrating various sensors with multiple AI technologies, the shopping cart is endowed with the abilities of quick checkout, intelligent navigation, and automatic obstacle avoidance.
In the research, a variety of methods were utilized for program development and case studies. Laser SLAM was used for environmental perception and map construction, and multiple filtering algorithms were employed for self-position confirmation. The test results demonstrated good recognition and mapping outcomes, validating the application capabilities.
This study illustrates that by integrating RFID technology, UWB technology, and laser SLAM with AI algorithms, the performance of smart shopping carts can be significantly enhanced, enabling key functionalities such as quick checkout, autonomous following, intelligent navigation, and automatic obstacle avoidance. The realization of these functions adds an intelligent and feasible solution to the operation of large supermarkets. Future work will focus on further optimizing system performance and exploring more commercial application scenarios.
KEYWORDS:Radio Frequency Identification;Simultaneous Localization and Mapping;intelligent shopping trolley;Ultra-Wideband;
