随着科技的飞速发展,现代社会逐渐趋向智能化,智能小车和智能机器人的发展前景越来越广阔,本设计开发了基于STM32f103C8T6微控制器的智能循迹避障小车。以STM32芯片作为核心控制单元,充分利用其高性能、低功耗以及丰富的外设接口等优势...随着科技的飞速发展,现代社会逐渐趋向智能化,智能小车和智能机器人的发展前景越来越广阔,本设计开发了基于STM32f103C8T6微控制器的智能循迹避障小车。以STM32芯片作为核心控制单元,充分利用其高性能、低功耗以及丰富的外设接口等优势来实现小车的各项功能。对于避障功能,通过搭载超声波传感器、红外传感器等多种类型的距离检测传感器,实时感知小车周围环境中的障碍物信息。运用PID算法对传感器采集的数据进行处理分析,使小车能够在行进过程中及时检测到前方、侧方等潜在障碍物,并自主做出合理的避障决策,例如改变行驶方向或者暂停运动,确保小车安全且平稳地运行。本设计结合了嵌入式系统开发、传感器技术以及无线通信技术,经过实际测试验证,小车可有效实现无线智能控制与避障功能,具备一定的实用价值和应用前景,可广泛应用于智能家居、仓储物流巡检等诸多领域。With the rapid development of science and technology, modern society is gradually becoming more intelligent. The development prospects of intelligent cars and intelligent robots are getting broader. This design develops an intelligent line-following and obstacle-avoiding car based on the STM32f103C8T6 microcontroller. Taking the STM32 chip as the core control unit, it fully utilizes its advantages such as high performance, low power consumption, and rich peripheral interfaces to achieve various functions of the car. For the obstacle-avoiding function, by installing multiple types of distance detection sensors, such as ultrasonic sensors and infrared sensors, it can perceive the obstacle information in the surrounding environment of the car in real-time. The PID algorithm is used to process and analyze the data collected by the sensors, enabling the car to detect potential obstacles in the front, side, and other directions promptly during the driving process and make reasonable obstacle-avoiding decisions independently, such as changing the driving direction or pausing movement, to ensure the safe and stable operation of the car. This design combines embedded system development, sensor technology, and wireless communication technology. Through practical tests, the car can effectively achieve wireless intelligent control and obstacle-avoiding functions, has certain practical value and application prospects, and can be widely applied in many fields, such as smart home, warehousing logistics inspection.展开更多
近年来,随着计算机技术的发展,机器视觉已经在各个领域得到了广泛的应用。其中,利用机器视觉技术来对水果种类进行精确识别是一直以来的研究热点。水果销售作为餐饮零售业的重要分支,正逐渐引入无接触服务的概念。通过非接触式水果销售...近年来,随着计算机技术的发展,机器视觉已经在各个领域得到了广泛的应用。其中,利用机器视觉技术来对水果种类进行精确识别是一直以来的研究热点。水果销售作为餐饮零售业的重要分支,正逐渐引入无接触服务的概念。通过非接触式水果销售机器人,消费者可以享受到安全、卫生、快速的购买体验,同时商家也因此获得更高效的销售方式和更为便捷的管理手段,其目的是实现自动化水果种类识别,节省农业劳动力资源,提高生产效率,促进智慧农业的发展。因此本课题利用K210视觉模块对水果进行图像采集,将采集的图像进行预处理、立体校正,接着在Mixhub开源数据平台进行模型训练,K210视觉模块不仅可以降低成本的使用,还可以精确识别水果的种类。In recent years, with the development of computer technology, machine vision has been widely applied in various fields. Among them, the precise identification of fruit types using machine vision technology has been a research hotspot for a long time. Fruit sales, as an important branch of the catering and retail industry, are gradually introducing the concept of contactless service. Through contactless fruit vending robots, consumers can enjoy a safe, hygienic, and fast purchasing experience, while businesses obtain more efficient sales methods and more convenient management tools. The aim is to achieve automated fruit type recognition, save agricultural labor resources, increase production efficiency, and promote the development of smart agriculture. Therefore, this project utilizes the K210 vision module to capture images of fruits, preprocess and rectify the captured images in three dimensions, and then trains models on the Mixhub open-source data platform. The K210 vision module not only reduces costs but also accurately identifies fruit types.展开更多
文摘随着科技的飞速发展,现代社会逐渐趋向智能化,智能小车和智能机器人的发展前景越来越广阔,本设计开发了基于STM32f103C8T6微控制器的智能循迹避障小车。以STM32芯片作为核心控制单元,充分利用其高性能、低功耗以及丰富的外设接口等优势来实现小车的各项功能。对于避障功能,通过搭载超声波传感器、红外传感器等多种类型的距离检测传感器,实时感知小车周围环境中的障碍物信息。运用PID算法对传感器采集的数据进行处理分析,使小车能够在行进过程中及时检测到前方、侧方等潜在障碍物,并自主做出合理的避障决策,例如改变行驶方向或者暂停运动,确保小车安全且平稳地运行。本设计结合了嵌入式系统开发、传感器技术以及无线通信技术,经过实际测试验证,小车可有效实现无线智能控制与避障功能,具备一定的实用价值和应用前景,可广泛应用于智能家居、仓储物流巡检等诸多领域。With the rapid development of science and technology, modern society is gradually becoming more intelligent. The development prospects of intelligent cars and intelligent robots are getting broader. This design develops an intelligent line-following and obstacle-avoiding car based on the STM32f103C8T6 microcontroller. Taking the STM32 chip as the core control unit, it fully utilizes its advantages such as high performance, low power consumption, and rich peripheral interfaces to achieve various functions of the car. For the obstacle-avoiding function, by installing multiple types of distance detection sensors, such as ultrasonic sensors and infrared sensors, it can perceive the obstacle information in the surrounding environment of the car in real-time. The PID algorithm is used to process and analyze the data collected by the sensors, enabling the car to detect potential obstacles in the front, side, and other directions promptly during the driving process and make reasonable obstacle-avoiding decisions independently, such as changing the driving direction or pausing movement, to ensure the safe and stable operation of the car. This design combines embedded system development, sensor technology, and wireless communication technology. Through practical tests, the car can effectively achieve wireless intelligent control and obstacle-avoiding functions, has certain practical value and application prospects, and can be widely applied in many fields, such as smart home, warehousing logistics inspection.
文摘近年来,随着计算机技术的发展,机器视觉已经在各个领域得到了广泛的应用。其中,利用机器视觉技术来对水果种类进行精确识别是一直以来的研究热点。水果销售作为餐饮零售业的重要分支,正逐渐引入无接触服务的概念。通过非接触式水果销售机器人,消费者可以享受到安全、卫生、快速的购买体验,同时商家也因此获得更高效的销售方式和更为便捷的管理手段,其目的是实现自动化水果种类识别,节省农业劳动力资源,提高生产效率,促进智慧农业的发展。因此本课题利用K210视觉模块对水果进行图像采集,将采集的图像进行预处理、立体校正,接着在Mixhub开源数据平台进行模型训练,K210视觉模块不仅可以降低成本的使用,还可以精确识别水果的种类。In recent years, with the development of computer technology, machine vision has been widely applied in various fields. Among them, the precise identification of fruit types using machine vision technology has been a research hotspot for a long time. Fruit sales, as an important branch of the catering and retail industry, are gradually introducing the concept of contactless service. Through contactless fruit vending robots, consumers can enjoy a safe, hygienic, and fast purchasing experience, while businesses obtain more efficient sales methods and more convenient management tools. The aim is to achieve automated fruit type recognition, save agricultural labor resources, increase production efficiency, and promote the development of smart agriculture. Therefore, this project utilizes the K210 vision module to capture images of fruits, preprocess and rectify the captured images in three dimensions, and then trains models on the Mixhub open-source data platform. The K210 vision module not only reduces costs but also accurately identifies fruit types.
