V-REP和Python的联合仿真

2023-11-07 13:44
文章标签 python 联合 仿真 rep

本文主要是介绍V-REP和Python的联合仿真,希望对大家解决编程问题提供一定的参考价值,需要的开发者们随着小编来一起学习吧!

机器人仿真软件 各类免费的的机器人仿真软件优缺点汇总_robot 仿真 软件收费么_dyannacon的博客-CSDN博客

课程地址 https://class.guyuehome.com/p/t_pc/course_pc_detail/column/p_605af87be4b007b4183a42e7

课程资料 guyueclass: 古月学院课程代码

旋转变换 旋转的左乘与右乘 - 知乎

四足机器人站立控制原理 【基础知识】四足机器人的站立姿态控制原理 - 知乎

单腿逆解参考 https://github.com/richardbloemenkamp/Robotdog

Vrep文档

Vrep放大object

Vrep 导入模型步骤:

1. plugins-->urdf import导入机器人URDF文件

2. 删除机器人对象中的world_joint和world_link_visual

3. 双击设置机器人参数

碰撞参数设置:body参数设置,自身碰撞勾选前四个勾,leg参数设置,自身碰撞勾选后四个勾,即不计算与自身的碰撞关系

设置关节参数

调节颜色

python联合仿真

remote API路径:C:\Program Files\CoppeliaRobotics\CoppeliaSimEdu\programming\remoteApiBindings

1. 选择仿真器

2. 创建Vrep脚本用于远程连接

3. 绑定脚本到机器人

4. 编辑脚本,添加远程连接代码

4. 编写python脚本并测试(将腿部足端位置转换为关节的角度)

连接V-REP需要从remote API路径拷贝相关文件

"""
连接VREP Server并测试控制四足机器人
"""
try:import sim
except ImportError:print('--------------------------------------------------------------')print('"sim.py" could not be imported. This means very probably that')print('either "sim.py" or the remoteApi library could not be found.')print('Make sure both are in the same folder as this file,')print('or appropriately adjust the file "sim.py"')print('--------------------------------------------------------------')print('')sim = Noneimport time
import numpy as npdef start_simulation():sim.simxFinish(-1)# 开启套接字与server进行通信clientID = sim.simxStart('127.0.0.1', 19999, True, True, 5000, 5)if clientID != -1:print('Connected to remote API server with ClientID ', clientID)# 开始模拟sim.simxStartSimulation(clientID, sim.simx_opmode_oneshot)return clientIDelse:return -1def get_joints(client_id):# 机器人电机力矩参数rotation_forces = [# RB[500, 500, 500],# RF[500, 500, 500],# LB[500, 500, 500],# LF[500, 500, 500]]# 获取机器人关节对象句柄rec, rb_rot_1 = sim.simxGetObjectHandle(client_id, 'rb_rot_1', sim.simx_opmode_blocking)rec, rb_rot_2 = sim.simxGetObjectHandle(client_id, 'rb_rot_2', sim.simx_opmode_blocking)rec, rb_rot_3 = sim.simxGetObjectHandle(client_id, 'rb_rot_3', sim.simx_opmode_blocking)rec, rf_rot_1 = sim.simxGetObjectHandle(client_id, 'rf_rot_1', sim.simx_opmode_blocking)rec, rf_rot_2 = sim.simxGetObjectHandle(client_id, 'rf_rot_2', sim.simx_opmode_blocking)rec, rf_rot_3 = sim.simxGetObjectHandle(client_id, 'rf_rot_3', sim.simx_opmode_blocking)rec, lb_rot_1 = sim.simxGetObjectHandle(client_id, 'lb_rot_1', sim.simx_opmode_blocking)rec, lb_rot_2 = sim.simxGetObjectHandle(client_id, 'lb_rot_2', sim.simx_opmode_blocking)rec, lb_rot_3 = sim.simxGetObjectHandle(client_id, 'lb_rot_3', sim.simx_opmode_blocking)rec, lf_rot_1 = sim.simxGetObjectHandle(client_id, 'lf_rot_1', sim.simx_opmode_blocking)rec, lf_rot_2 = sim.simxGetObjectHandle(client_id, 'lf_rot_2', sim.simx_opmode_blocking)rec, lf_rot_3 = sim.simxGetObjectHandle(client_id, 'lf_rot_3', sim.simx_opmode_blocking)# 设置电机力矩rec = sim.simxSetJointForce(client_id, rb_rot_1, rotation_forces[0][0], sim.simx_opmode_blocking)rec = sim.simxSetJointForce(client_id, rb_rot_2, rotation_forces[0][1], sim.simx_opmode_blocking)rec = sim.simxSetJointForce(client_id, rb_rot_3, rotation_forces[0][2], sim.simx_opmode_blocking)rec = sim.simxSetJointForce(client_id, rf_rot_1, rotation_forces[1][0], sim.simx_opmode_blocking)rec = sim.simxSetJointForce(client_id, rf_rot_2, rotation_forces[1][1], sim.simx_opmode_blocking)rec = sim.simxSetJointForce(client_id, rf_rot_3, rotation_forces[1][2], sim.simx_opmode_blocking)rec = sim.simxSetJointForce(client_id, lb_rot_1, rotation_forces[2][0], sim.simx_opmode_blocking)rec = sim.simxSetJointForce(client_id, lb_rot_2, rotation_forces[2][1], sim.simx_opmode_blocking)rec = sim.simxSetJointForce(client_id, lb_rot_3, rotation_forces[2][2], sim.simx_opmode_blocking)rec = sim.simxSetJointForce(client_id, lf_rot_1, rotation_forces[3][0], sim.simx_opmode_blocking)rec = sim.simxSetJointForce(client_id, lf_rot_2, rotation_forces[3][1], sim.simx_opmode_blocking)rec = sim.simxSetJointForce(client_id, lf_rot_3, rotation_forces[3][2], sim.simx_opmode_blocking)return [rb_rot_1, rb_rot_2, rb_rot_3], \[rf_rot_1, rf_rot_2, rf_rot_3], \[lb_rot_1, lb_rot_2, lb_rot_3], \[lf_rot_1, lf_rot_2, lf_rot_3]def leg_inverse_kine(x, y, z):# h,hu和hl分别是单条腿杆件的长度h = 0.15hu = 0.35hl = 0.382dyz = np.sqrt(y**2 + z**2)lyz = np.sqrt(dyz**2 - h**2)gamma_yz = -np.arctan(y/z)gamma_h_offset = -np.arctan(h/lyz)gamma = gamma_yz - gamma_h_offsetlxzp = np.sqrt(lyz**2 + x**2)n = (lxzp**2 - hl**2 - hu**2) / (2 * hu)beta = -np.arccos(n / hl)alfa_xzp = -np.arctan(x/lyz)alfa_off = np.arccos((hu + n) / lxzp)alfa = alfa_xzp + alfa_offreturn gamma, alfa, betaif __name__ == '__main__':# 机器人电机角度参数rb_poses = [40*np.pi/180, 0, 0]rf_poses = [0, 0, 0]lb_poses = [0, 0, 0]lf_poses = [0, 0, 0]client_id = start_simulation()if client_id != -1:joints = get_joints(client_id)rb_joints = joints[0]rf_joints = joints[1]lb_joints = joints[2]lf_joints = joints[3]time.sleep(1)timeout = 60start_time = time.time()curr_time = time.time()# 初始关节角度rb_poses = leg_inverse_kine(0, -0.3, -0.632)rf_poses = leg_inverse_kine(0, -0.3, -0.632)lb_poses = leg_inverse_kine(0, -0.3, -0.632)lf_poses = leg_inverse_kine(0, -0.3, -0.632)while curr_time - start_time < timeout:# 设置关节角度rec = sim.simxSetJointTargetPosition(client_id, rb_joints[0], -rb_poses[0], sim.simx_opmode_oneshot)rec = sim.simxSetJointTargetPosition(client_id, rb_joints[1], rb_poses[1], sim.simx_opmode_oneshot)rec = sim.simxSetJointTargetPosition(client_id, rb_joints[2], rb_poses[2], sim.simx_opmode_oneshot)rec = sim.simxSetJointTargetPosition(client_id, rf_joints[0], rf_poses[0], sim.simx_opmode_oneshot)rec = sim.simxSetJointTargetPosition(client_id, rf_joints[1], rf_poses[1], sim.simx_opmode_oneshot)rec = sim.simxSetJointTargetPosition(client_id, rf_joints[2], rf_poses[2], sim.simx_opmode_oneshot)rec = sim.simxSetJointTargetPosition(client_id, lb_joints[0], -lb_poses[0], sim.simx_opmode_oneshot)rec = sim.simxSetJointTargetPosition(client_id, lb_joints[1], lb_poses[1], sim.simx_opmode_oneshot)rec = sim.simxSetJointTargetPosition(client_id, lb_joints[2], lb_poses[2], sim.simx_opmode_oneshot)rec = sim.simxSetJointTargetPosition(client_id, lf_joints[0], lf_poses[0], sim.simx_opmode_oneshot)rec = sim.simxSetJointTargetPosition(client_id, lf_joints[1], lf_poses[1], sim.simx_opmode_oneshot)rec = sim.simxSetJointTargetPosition(client_id, lf_joints[2], lf_poses[2], sim.simx_opmode_oneshot)curr_time = time.time()# print("curr time :", curr_time - start_time)# 完成模拟sim.simxStopSimulation(client_id, sim.simx_opmode_blocking)sim.simxFinish(client_id)else:print('Failed connecting to remote API server')

显示足端轨迹

1. 打开shape编辑模式,并在vertex编辑模式下选择节点,在添加dummy

将dummy移动到腿部object下

2. 添加图用于创建curve

3. 设置3D Curve

4. 修改位置控制速度上限(将速度上限修改为500)

步态控制

utils.py

import sim
import numpy as npdef start_simulation():sim.simxFinish(-1)# 开启套接字与server进行通信clientID = sim.simxStart('127.0.0.1', 19999, True, True, 5000, 5)if clientID != -1:print('Connected to remote API server with ClientID ', clientID)# 开始模拟sim.simxStartSimulation(clientID, sim.simx_opmode_oneshot)return clientIDelse:return -1def get_joints(client_id):# 机器人电机力矩参数rotation_forces = [# RB[500, 500, 500],# RF[500, 500, 500],# LB[500, 500, 500],# LF[500, 500, 500]]# 获取机器人关节对象句柄rec, rb_rot_1 = sim.simxGetObjectHandle(client_id, 'rb_rot_1', sim.simx_opmode_blocking)rec, rb_rot_2 = sim.simxGetObjectHandle(client_id, 'rb_rot_2', sim.simx_opmode_blocking)rec, rb_rot_3 = sim.simxGetObjectHandle(client_id, 'rb_rot_3', sim.simx_opmode_blocking)rec, rf_rot_1 = sim.simxGetObjectHandle(client_id, 'rf_rot_1', sim.simx_opmode_blocking)rec, rf_rot_2 = sim.simxGetObjectHandle(client_id, 'rf_rot_2', sim.simx_opmode_blocking)rec, rf_rot_3 = sim.simxGetObjectHandle(client_id, 'rf_rot_3', sim.simx_opmode_blocking)rec, lb_rot_1 = sim.simxGetObjectHandle(client_id, 'lb_rot_1', sim.simx_opmode_blocking)rec, lb_rot_2 = sim.simxGetObjectHandle(client_id, 'lb_rot_2', sim.simx_opmode_blocking)rec, lb_rot_3 = sim.simxGetObjectHandle(client_id, 'lb_rot_3', sim.simx_opmode_blocking)rec, lf_rot_1 = sim.simxGetObjectHandle(client_id, 'lf_rot_1', sim.simx_opmode_blocking)rec, lf_rot_2 = sim.simxGetObjectHandle(client_id, 'lf_rot_2', sim.simx_opmode_blocking)rec, lf_rot_3 = sim.simxGetObjectHandle(client_id, 'lf_rot_3', sim.simx_opmode_blocking)# 设置电机力矩rec = sim.simxSetJointForce(client_id, rb_rot_1, rotation_forces[0][0], sim.simx_opmode_blocking)rec = sim.simxSetJointForce(client_id, rb_rot_2, rotation_forces[0][1], sim.simx_opmode_blocking)rec = sim.simxSetJointForce(client_id, rb_rot_3, rotation_forces[0][2], sim.simx_opmode_blocking)rec = sim.simxSetJointForce(client_id, rf_rot_1, rotation_forces[1][0], sim.simx_opmode_blocking)rec = sim.simxSetJointForce(client_id, rf_rot_2, rotation_forces[1][1], sim.simx_opmode_blocking)rec = sim.simxSetJointForce(client_id, rf_rot_3, rotation_forces[1][2], sim.simx_opmode_blocking)rec = sim.simxSetJointForce(client_id, lb_rot_1, rotation_forces[2][0], sim.simx_opmode_blocking)rec = sim.simxSetJointForce(client_id, lb_rot_2, rotation_forces[2][1], sim.simx_opmode_blocking)rec = sim.simxSetJointForce(client_id, lb_rot_3, rotation_forces[2][2], sim.simx_opmode_blocking)rec = sim.simxSetJointForce(client_id, lf_rot_1, rotation_forces[3][0], sim.simx_opmode_blocking)rec = sim.simxSetJointForce(client_id, lf_rot_2, rotation_forces[3][1], sim.simx_opmode_blocking)rec = sim.simxSetJointForce(client_id, lf_rot_3, rotation_forces[3][2], sim.simx_opmode_blocking)return [rb_rot_1, rb_rot_2, rb_rot_3], \[rf_rot_1, rf_rot_2, rf_rot_3], \[lb_rot_1, lb_rot_2, lb_rot_3], \[lf_rot_1, lf_rot_2, lf_rot_3]def leg_inverse_kine(x, y, z):"""求四足机器人单条腿的逆运动学,输入足端位置,返回单腿关节的旋转的角度"""# h,hu和hl分别是单条腿杆件的长度h = 0.15hu = 0.35hl = 0.382dyz = np.sqrt(y ** 2 + z ** 2)lyz = np.sqrt(dyz ** 2 - h ** 2)gamma_yz = -np.arctan(y / z)gamma_h_offset = -np.arctan(h / lyz)gamma = gamma_yz - gamma_h_offsetlxzp = np.sqrt(lyz ** 2 + x ** 2)n = (lxzp ** 2 - hl ** 2 - hu ** 2) / (2 * hu)beta = -np.arccos(n / hl)alfa_xzp = -np.arctan(x / lyz)alfa_off = np.arccos((hu + n) / lxzp)alfa = alfa_xzp + alfa_offreturn gamma, alfa, betadef pose_control(roll, pitch, yaw, pos_x, pos_y, pos_z):"""输入"""b = 0.4l = 0.8w = 0.7# 基座的高度h = 0.732# 转换角度R = roll * np.pi / 180P = pitch * np.pi / 180Y = yaw * np.pi / 180pos = np.mat([pos_x, pos_y, pos_z]).T# 定义旋转矩阵rotx = np.mat([[1, 0, 0],[0, np.cos(R), -np.sin(R)],[0, np.sin(R), np.cos(R)]])roty = np.mat([[np.cos(P), 0, -np.sin(P)],[0, 1, 0],[np.sin(P), 0, np.cos(P)]])rotz = np.mat([[np.cos(Y), -np.sin(Y), 0],[np.sin(Y), np.cos(Y), 0],[0, 0, 1]])rot_mat = rotx * roty * rotz# 基座位置body_struct = np.mat([[l / 2, b / 2, h],[l / 2, -b / 2, h],[-l / 2, b / 2, h],[-l / 2, -b / 2, h]]).T# 足端位置footpoint_struct = np.mat([[l / 2, w / 2, 0],[l / 2, -w / 2, 0],[-l / 2, w / 2, 0],[-l / 2, -w / 2, 0]]).Tleg_pose = np.mat(np.zeros((3, 4)))for i in range(4):leg_pose[:, i] = -pos - rot_mat * body_struct[:, i] + footpoint_struct[:, i]return np.squeeze(np.array(leg_pose[:, 3])), np.squeeze(np.array(leg_pose[:, 0])), \np.squeeze(np.array(leg_pose[:, 1])), np.squeeze(np.array(leg_pose[:, 2]))def cycloid(dt: float, period: float = 1.0, xs: float = -0.1, xf: float = 0.1, zs: float = -0.582, h: float = 0.1):"""计算摆线上在给定时间t处的坐标。参数:t (float): 当前时间点Ts (float): 摆线运动总时间,默认为1.0xs (float): 起始x坐标,默认为-0.1xf (float): 终点x坐标,默认为0.1zs (float): 起始z坐标,默认为-0.582h (float): 摆线垂直位移,默认为0.1返回:tuple[float, float]: xep和zep的坐标值"""sigma = 2 * np.pi * dt / periodx_p = (xf - xs) * ((sigma - np.sin(sigma)) / (2 * np.pi)) + xsy_p = h * (1 - np.cos(sigma)) / 2 + zsreturn x_p, y_pif __name__ == '__main__':for pos in pose_control(30, 0, 0, 0, 0, 0.732):print(pos)

main.py

import time
from utils import *walk_period = 1.0
trot_period = 0.4gait = 1def cal_phase(dt, T, factor, zs = -0.482, h = 0.15):if dt < T * factor:return cycloid(dt, period=T * factor, zs=zs, h=h)else:return 0.1 - 0.2 / (T * (1 - factor)) * (dt - T * factor), zsdef walk_gait(dt):zs = -0.482h = 0.15lb_dt = dt % walk_periodrf_dt = (dt + 0.25) % walk_periodrb_dt = (dt + 0.5) % walk_periodlf_dt = (dt + 0.75) % walk_periodlb_pos = cal_phase(lb_dt, T=walk_period, factor=0.25, zs=zs, h=h)rf_pos = cal_phase(rf_dt, T=walk_period, factor=0.25, zs=zs, h=h)rb_pos = cal_phase(rb_dt, T=walk_period, factor=0.25, zs=zs, h=h)lf_pos = cal_phase(lf_dt, T=walk_period, factor=0.25, zs=zs, h=h)return lb_pos, rf_pos, rb_pos, lf_posdef trot_gait(dt):zs = -0.482h = 0.1dt_1 = dt % trot_perioddt_2 = (dt + 0.2) % trot_periodpos_1 = cal_phase(dt_1, T=trot_period, factor=0.5, zs=zs, h=h)pos_2 = cal_phase(dt_2, T=trot_period, factor=0.5, zs=zs, h=h)return pos_1, pos_2if __name__ == '__main__':# 连接到V-REP服务器clientID = start_simulation()# 检查连接是否成功if clientID != -1:joints = get_joints(clientID)rb_joints = joints[0]rf_joints = joints[1]lb_joints = joints[2]lf_joints = joints[3]timeout = 60start_time = time.time()curr_time = start_timesim_start_time, sim_curr_time = None, Nonelb_pos, rf_pos, rb_pos, lf_pos = None, None, None, None# 获取仿真时间while curr_time - start_time < timeout:res, sim_curr_time = sim.simxGetFloatSignal(clientID, 'time', sim.simx_opmode_oneshot)if res == sim.simx_return_ok:if sim_start_time is None:sim_start_time = sim_curr_timeprint("time ", sim_curr_time - sim_start_time)if sim_start_time:dt = sim_curr_time - sim_start_timeif gait == 0:# dt = (sim_curr_time - sim_start_time) % walk_periodlb_pos, rf_pos, rb_pos, lf_pos = walk_gait(dt)elif gait == 1:# dt = (sim_curr_time - sim_start_time) % trot_periodpos_1, pos_2 = trot_gait(dt)lb_pos = pos_1rf_pos = pos_1rb_pos = pos_2lf_pos = pos_2# 从足端位置求解关节角度rb_pose = leg_inverse_kine(rb_pos[0], -0.15, rb_pos[1])rf_pose = leg_inverse_kine(rf_pos[0], -0.15, rf_pos[1])lb_pose = leg_inverse_kine(lb_pos[0], -0.15, lb_pos[1])lf_pose = leg_inverse_kine(lf_pos[0], -0.15, lf_pos[1])rec = sim.simxSetJointTargetPosition(clientID, rb_joints[0], -rb_pose[0], sim.simx_opmode_oneshot)rec = sim.simxSetJointTargetPosition(clientID, rb_joints[1], rb_pose[1], sim.simx_opmode_oneshot)rec = sim.simxSetJointTargetPosition(clientID, rb_joints[2], rb_pose[2], sim.simx_opmode_oneshot)rec = sim.simxSetJointTargetPosition(clientID, rf_joints[0], rf_pose[0], sim.simx_opmode_oneshot)rec = sim.simxSetJointTargetPosition(clientID, rf_joints[1], rf_pose[1], sim.simx_opmode_oneshot)rec = sim.simxSetJointTargetPosition(clientID, rf_joints[2], rf_pose[2], sim.simx_opmode_oneshot)rec = sim.simxSetJointTargetPosition(clientID, lb_joints[0], -lb_pose[0], sim.simx_opmode_oneshot)rec = sim.simxSetJointTargetPosition(clientID, lb_joints[1], lb_pose[1], sim.simx_opmode_oneshot)rec = sim.simxSetJointTargetPosition(clientID, lb_joints[2], lb_pose[2], sim.simx_opmode_oneshot)rec = sim.simxSetJointTargetPosition(clientID, lf_joints[0], lf_pose[0], sim.simx_opmode_oneshot)rec = sim.simxSetJointTargetPosition(clientID, lf_joints[1], lf_pose[1], sim.simx_opmode_oneshot)rec = sim.simxSetJointTargetPosition(clientID, lf_joints[2], lf_pose[2], sim.simx_opmode_oneshot)# 停止仿真并断开与V-REP的连接sim.simxStopSimulation(clientID, sim.simx_opmode_oneshot)sim.simxFinish(clientID)else:print("无法连接到V-REP")

walk步态

trot步态

这篇关于V-REP和Python的联合仿真的文章就介绍到这儿,希望我们推荐的文章对编程师们有所帮助!



http://www.chinasem.cn/article/364005

相关文章

python获取指定名字的程序的文件路径的两种方法

《python获取指定名字的程序的文件路径的两种方法》本文主要介绍了python获取指定名字的程序的文件路径的两种方法,文中通过示例代码介绍的非常详细,对大家的学习或者工作具有一定的参考学习价值,需要... 最近在做项目,需要用到给定一个程序名字就可以自动获取到这个程序在Windows系统下的绝对路径,以下

使用Python批量将.ncm格式的音频文件转换为.mp3格式的实战详解

《使用Python批量将.ncm格式的音频文件转换为.mp3格式的实战详解》本文详细介绍了如何使用Python通过ncmdump工具批量将.ncm音频转换为.mp3的步骤,包括安装、配置ffmpeg环... 目录1. 前言2. 安装 ncmdump3. 实现 .ncm 转 .mp34. 执行过程5. 执行结

Python实现批量CSV转Excel的高性能处理方案

《Python实现批量CSV转Excel的高性能处理方案》在日常办公中,我们经常需要将CSV格式的数据转换为Excel文件,本文将介绍一个基于Python的高性能解决方案,感兴趣的小伙伴可以跟随小编一... 目录一、场景需求二、技术方案三、核心代码四、批量处理方案五、性能优化六、使用示例完整代码七、小结一、

Python中 try / except / else / finally 异常处理方法详解

《Python中try/except/else/finally异常处理方法详解》:本文主要介绍Python中try/except/else/finally异常处理方法的相关资料,涵... 目录1. 基本结构2. 各部分的作用tryexceptelsefinally3. 执行流程总结4. 常见用法(1)多个e

Python中logging模块用法示例总结

《Python中logging模块用法示例总结》在Python中logging模块是一个强大的日志记录工具,它允许用户将程序运行期间产生的日志信息输出到控制台或者写入到文件中,:本文主要介绍Pyt... 目录前言一. 基本使用1. 五种日志等级2.  设置报告等级3. 自定义格式4. C语言风格的格式化方法

Python实现精确小数计算的完全指南

《Python实现精确小数计算的完全指南》在金融计算、科学实验和工程领域,浮点数精度问题一直是开发者面临的重大挑战,本文将深入解析Python精确小数计算技术体系,感兴趣的小伙伴可以了解一下... 目录引言:小数精度问题的核心挑战一、浮点数精度问题分析1.1 浮点数精度陷阱1.2 浮点数误差来源二、基础解决

使用Python实现Word文档的自动化对比方案

《使用Python实现Word文档的自动化对比方案》我们经常需要比较两个Word文档的版本差异,无论是合同修订、论文修改还是代码文档更新,人工比对不仅效率低下,还容易遗漏关键改动,下面通过一个实际案例... 目录引言一、使用python-docx库解析文档结构二、使用difflib进行差异比对三、高级对比方

深度解析Python中递归下降解析器的原理与实现

《深度解析Python中递归下降解析器的原理与实现》在编译器设计、配置文件处理和数据转换领域,递归下降解析器是最常用且最直观的解析技术,本文将详细介绍递归下降解析器的原理与实现,感兴趣的小伙伴可以跟随... 目录引言:解析器的核心价值一、递归下降解析器基础1.1 核心概念解析1.2 基本架构二、简单算术表达

从入门到精通详解Python虚拟环境完全指南

《从入门到精通详解Python虚拟环境完全指南》Python虚拟环境是一个独立的Python运行环境,它允许你为不同的项目创建隔离的Python环境,下面小编就来和大家详细介绍一下吧... 目录什么是python虚拟环境一、使用venv创建和管理虚拟环境1.1 创建虚拟环境1.2 激活虚拟环境1.3 验证虚

详解python pycharm与cmd中制表符不一样

《详解pythonpycharm与cmd中制表符不一样》本文主要介绍了pythonpycharm与cmd中制表符不一样,这个问题通常是因为PyCharm和命令行(CMD)使用的制表符(tab)的宽... 这个问题通常是因为PyCharm和命令行(CMD)使用的制表符(tab)的宽度不同导致的。在PyChar