一款基于zcode,opencv的树莓派机械臂控制方案 (robot_control-with-Opencv)
最后更新时间：2023年8月(2023.8 the last time update)
为我和其他支持者(for myself and other supporters)
-成功开发出一套完整的系统，该系统能够识别形状和Z码，并控制机器人手臂系统。(Successfully developed a complete system that can recognize shapes and Z-codes, and control the robot arm system.)
-使用Python编程语言，配合树莓派4B实现。(Using Python programming language, combined with Raspberry Pi 4B implementation.)
-使用中凌科技公司的舵机和Uno微控制器。(Use Zhongling Technology's servo and Uno microcontroller.)
-机器人手臂具有六自由度，包括2个PWM舵机和4个步进舵机。(The robot arm has six degrees of freedom, including two PWM servos and four stepper servos.)
## 包含两个解决方案：(Includes two solutions:)
-1：通过最终坐标点的逆运算导出动作，即逆向运动学。这种方法可以精确控制机器人手臂的每一个动作。(Option 1: Derive the action through the inverse operation of the final coordinate point, that is, inverse kinematics. This method can precisely control every action of the robot arm.)
-2.正向运动学，直接调整每个舵机的角度进行控制。这种方法相对简单，但舵机只能旋转固定角度，灵活性较低。(Option 2: Forward kinematics, directly adjust the angle of each servo for control. This method is relatively simple, but the servo can only rotate at a fixed angle, resulting in lower flexibility.)
文件结构（The file structure）
arm_control.py ：展示如何通过逆运动学控制机器人手臂。这个文件包含了控制机器人手臂运动的核心逻辑。(Show how to control a robot arm through inverse kinematics. This file contains the core logic for controlling the movement of robot arms.)
cluster.py 介绍使用OpenCV的基础方法。在这个程序中，我使用了Python-OpenCV库进行调用。它还展示了如何通过RGB值检测不同颜色。(Introduce the basic methods of using OpenCV. In this program, I used the Python OpenCV library for calling. It also demonstrates how to detect different colors through RGB values.)
shapes.py ：展示如何使用霍夫圆算法和边缘点算法检测三角形和矩形。这个文件对于识别物体形状至关重要。(Show how to use the Hough circle algorithm and edge point algorithm to detect triangles and rectangles. This file is crucial for recognizing the shape of objects.)
uart.py ：讲解UART协议使用方法。我提供了两种使用方式：单线程和多线程。这个文件确保了机器人手臂与电脑之间的稳定通信。(Explain the usage of UART protocol. I offer two usage options: single threaded and multi-threaded. This file ensures stable communication between the robot arm and the computer)
zcode.py ：使用pyzbar库来检测Z码。这个文件使得机器人能够识别和处理Z码信息。(Use the pyzbar library to detect Z-codes. This file enables robots to recognize and process Z-code information.)
word_detect.py ：这是最终解决方案。由于我在比赛中的使用场景已经不再对其他程序开放，所以我没有展示全部内容。（This is the ultimate solution. Since my usage scenarios in the competition are no longer open to other programs, I did not showcase all the content.）
进度与特性：(The progress and features:)
- [√] 程序开发已完成。(finish the program)
- [√] 支持通过UART进行通信，确保了与其他设备的兼容性。(can use with uart)
- [√] 可以在所有树莓派系列上使用，包括树莓派3、3B、3B+、4、4B和40。(can use with raspberry pi all series)
- [√]支持Python >=3.8，这是目前最常用的Python版本之一。( support python >=3.8)
- [√] 支持OpenCV >=4.0，这是开源计算机视觉库的最新版本。(support cv >=4.0)
- [√] 由于使用了通用的库，因此也可以在其他品牌的ARM开发板上使用。这增加了系统的可移植性。(can use with other brands of ARM development board as it uses general libs.)
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GITHUB项目链接：https://github.com/kouge0510/robot_control-with-Opencv
作者：抠哥