551 lines
22 KiB
Python
551 lines
22 KiB
Python
import cv2
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import os
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from datetime import datetime
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import math
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import numpy as np
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import sys
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import random
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from pathlib import Path
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import time
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from scipy.spatial.transform import Rotation as R
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from scipy.interpolate import CubicSpline
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import matplotlib.pyplot as plt
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from mpl_toolkits.mplot3d import Axes3D
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sys.path.append(str(Path(__file__).resolve().parent.parent))
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sys.path.append('/home/jsfb/jsfb_ws/MassageRobot_aubo/Massage/MassageControl')
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from hardware.remote_cam import ToolCamera
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from hardware.aubo_C5 import AuboC5
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np.set_printoptions(precision=8, suppress=True)
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# 设置寻找亚像素角点的参数，采用的停止准则是最大循环次数30和最大误差容限0.001
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criteria = (cv2.TERM_CRITERIA_MAX_ITER | cv2.TERM_CRITERIA_EPS, 30, 0.001)
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# 标定计算类
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# -- 连接AuboC5
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# -- 连接 ToolCamera
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# -- 运动到初始位置 计算下一步运动位姿
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# -- 开始手眼标定
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# -- 输出结果
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class Calibration:
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def __init__(self, board_size, square_size, file_folder):
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"""
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初始化标定计算类。
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board_size: 标定板的格子数，格式为 (width, height)
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square_size: 格子的大小，单位为米
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"""
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self.board_size = board_size
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self.square_size = square_size
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self.objp = self._create_object_points()
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self.image_points = []
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self.robot_poses = []
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self.poselist = []
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self.cam = ToolCamera(host='127.0.0.1')
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self.cam.start()
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self.file_address = file_folder
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rgb_image, depth_image, camera_intrinsics = self.cam.get_latest_frame()
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cv2.imshow("rgb1",rgb_image)
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cv2.waitKey(0)
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if rgb_image is None :
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print(f'===============相机连接失败请检查并重试==============')
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else:
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print(f'===============相机连接成功==============')
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self.size = rgb_image.shape[:2]
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self.intrinsics = camera_intrinsics
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self.arm = AuboC5()
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self.arm.init()
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self.obj_points = []
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print(f'===============机械臂连接成功==============')
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def _create_object_points(self):
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"""创建标定板的3D点"""
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objp = np.zeros((self.board_size[0] * self.board_size[1], 3), np.float32)
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objp[:, :2] = np.mgrid[0:self.board_size[0], 0:self.board_size[1]].T.reshape(-1, 2) * self.square_size
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return objp
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def collect_data(self):
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#移动到初始位置
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# move = [(90) * (np.pi / 180), 4.23 * (np.pi / 180), 124.33 * (np.pi / 180), 61.56 * (np.pi / 180), -89.61 * (np.pi / 180), 0 * (np.pi / 180)]
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move = [(89.74) * (np.pi / 180), 11.21 * (np.pi / 180), 97.12 * (np.pi / 180), 29.06 * (np.pi / 180), -89.61 * (np.pi / 180), 0 * (np.pi / 180)]
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code = self.arm.move_joint(move,4,wait=True)
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if code == -1:
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print("运动到拍照位置失败")
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# time.sleep(3)
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#拍照
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rgb, depth, intrinsics = self.cam.get_latest_frame()
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cv2.imshow("rgb2",rgb)
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cv2.waitKey(0)
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if self.check_corners_add_sample() is False:
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print("初始位置下没有找到标定板")
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rgb, depth, intrinsics = self.cam.get_latest_frame()
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pose = self.arm.robot_rpc_client.getRobotInterface(self.arm.robot_name).getRobotState().getTcpPose()
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cv2.imshow("not right",rgb)
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cv2.waitKey(0)
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rgb, depth, intrinsics = self.cam.get_latest_frame()
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gray = cv2.cvtColor(rgb, cv2.COLOR_BGR2GRAY)
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ret, corners = cv2.findChessboardCorners(gray, self.board_size, None)
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robot_pose = self.get_pose_homomat()
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print(f"EE pose:{robot_pose}")
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center = self.init_chessboard_center(rgb)
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print(f"current center EE: {center}")
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''' Z轴抖动 '''
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# EE坐标系下的点
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trajectory, rpy_values = self.generate_cyclic_trajectory_above_center(center)
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self.visualization(center,trajectory,rpy_values)
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homo_trajectory = np.hstack((trajectory, np.ones((len(trajectory), 1))))
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tcp_pose = self.get_pose_homomat()
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# 把生成的trajectory 移动到 世界坐标下
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trajectory = (tcp_pose @ homo_trajectory.T).T[:, :3]
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rpy_angles = []
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for tool_point in trajectory:
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vector_center_to_tool = tool_point - center
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roll, pitch, yaw = self.get_roll_pitch_yaw(vector_center_to_tool)
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rpy_angles.append((roll, pitch, yaw))
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rpy_angles = np.array(rpy_angles)
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pose = self.concatenate_path_and_rpy(trajectory,rpy_angles )
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#世界坐标系下的中心
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center_homo = np.append(center, 1).reshape(4, 1)
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tcp_pose = self.get_pose_homomat()
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center_world_homo = tcp_pose @ center_homo # 4x4 *4x1 =4x1
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center_world = center_world_homo[:3].flatten()
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print(f"world center: {center_world} \n top pose:\n {pose[:5]}")
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lenOfResult = self.traversal_position_direction(pose)
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''' Z轴抖动 '''
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assert lenOfResult > 20, "没有收集到足够多的数据来完成精确的标定"
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def generate_cyclic_trajectory_above_center(self,center, num_steps=12, x_amplitude=0.12, z_amplitude=0.20, frequency=4, min_z_offset=0.60,step_z=0.01):
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"""
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生成所有轨迹点都在中心点上方的周期性波动轨迹。
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"""
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trajectory = []
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rpy_values = []
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y_constant = center[1]
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x_values = np.linspace(-x_amplitude, x_amplitude, num_steps)
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y_values = [center[1]+0.05,center[1]+0.15]
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# y_values = [center[1]+0.05,center[1]+0.15,center[1]+0.30]
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# y_values = [center[1]+0.15,center[1]+0.03,center[1]+0.06,center[1]+0.10,center[1]+0.15]
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# y_values = [center[1],center[1],center[1]]
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for i in range(len(y_values)):
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y = y_values[i]
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for x in x_values:
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# 使用绝对值正弦函数确保 Z 始终在 center[2] 之上
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z = z_amplitude * np.abs(np.sin(frequency * x * np.pi)) + center[2] + min_z_offset + step_z*i
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tool_point = np.array([x + center[0], y, z])
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trajectory.append(tool_point)
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# 计算从中心点射向轨迹点的向量
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vector_center_to_tool = tool_point - center
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roll, pitch, yaw = self.get_roll_pitch_yaw(vector_center_to_tool)
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rpy_values.append((roll, pitch, yaw))
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print(f"above part center:{center}\n {trajectory[:5]}")
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return np.array(trajectory), np.array(rpy_values)
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def get_roll_pitch_yaw(self,vector):
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"""
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计算从原点指向给定向量的 roll, pitch, yaw (弧度).
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假设 Z 轴向前，Y 轴向上，X 轴向右。
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"""
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x, y, z = vector
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roll = math.atan2(y, z)
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pitch = math.atan2(-x, math.sqrt(y**2 + z**2))
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yaw = 0 # 假设指向 tool 的方向没有偏航，或者根据实际需求调整
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return roll, pitch, yaw
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def concatenate_path_and_rpy(self,path, rpy_angles):
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"""
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Concatenates a list of positions [x, y, z] with a corresponding list of RPY angles [roll, pitch, yaw].
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Args:
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path (list of lists): A list where each inner list contains [x, y, z].
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rpy_angles (list of lists or tuples): A list where each inner list or tuple contains [roll, pitch, yaw].
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Returns:
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list of lists: A new list where each inner list is a concatenation of the
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corresponding position and RPY angles: [x, y, z, roll, pitch, yaw].
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"""
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if len(path) != len(rpy_angles):
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raise ValueError("The lengths of the path and rpy_angles lists must be the same.")
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combined_list = []
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for i in range(len(path)):
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position = path[i]
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orientation = rpy_angles[i]
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# combined = np.concatenate((position,orientation))
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combined = np.hstack((position.flatten(), orientation))
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combined_list.append(combined)
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print(f"combined xyz|rpy :{combined}")
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return combined_list
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def check_corners_add_sample(self):
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rgb, depth, intrinsics = self.cam.get_latest_frame()
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gray = cv2.cvtColor(rgb, cv2.COLOR_BGR2GRAY)
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ret, corners = cv2.findChessboardCorners(gray, self.board_size)
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pic_name = self.file_address + "/"+str(len(self.image_points)) + ".jpg"
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if ret:
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robot_pose = self.get_pose_homomat()
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self.add_sample(gray,corners,robot_pose)
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print(f"save path as: {pic_name}")
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# 绘制检测到的角点 (用于可视化，可选)
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# cv2.drawChessboardCorners(rgb, self.board_size, corners, ret)
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cv2.imwrite(pic_name, rgb)
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return True
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cv2.imwrite(pic_name, rgb)
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return False
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def add_sample(self, gray, corners,robot_pose):
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"""
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添加标定样本。
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image: 捕获的图像
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robot_pose: 机械臂的位姿，格式为 [x, y, z, roll, pitch, yaw]
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"""
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# 检测棋盘角点
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corners2 = cv2.cornerSubPix(gray, corners, (11, 11), (-1, -1), criteria) # 在原角点的基础上寻找亚像素角点
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# if self._validate_corners(corners2):
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if [corners2]:
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self.obj_points.append(self.objp)
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self.image_points.append(corners2)
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self.robot_poses.append(robot_pose)
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print(f"insert {robot_pose}")
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def get_pose_homomat(self):
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pose = self.arm.robot_rpc_client.getRobotInterface(self.arm.robot_name).getRobotState().getTcpPose()
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x, y, z, rx, ry, rz = pose
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r = R.from_euler('xyz', [rx, ry, rz])
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rotation_matrix = r.as_matrix()
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translation_vector = np.array([x, y, z]).reshape(3, 1)
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homo_mat = np.eye(4) # 创建 4x4 单位矩阵
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homo_mat[:3, :3] = rotation_matrix
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homo_mat[:3, 3] = translation_vector.flatten()
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return homo_mat
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def camera_calib(self):
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ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(self.obj_points, self.image_points, (self.size[1],self.size[0]) , None, None)
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if ret:
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print("内参矩阵:\n", mtx) # 内参数矩阵
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print("畸变系数:\n", dist) # 畸变系数 distortion cofficients = (k_1,k_2,p_1,p_2,k_3)
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print("++++++++++相机标定完成++++++++++++++")
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else:
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print("-----------相机标定失败--------------")
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return rvecs, tvecs
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def split_robot_pose(self):
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"""
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将包含 4x4 齐次矩阵的 robot_pose 列表拆分成 R_arm 和 t_arm。
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Args:
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robot_pose: 包含 4x4 齐次矩阵的 Python list。
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Returns:
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R_arm: 旋转矩阵的 Python list, 每个元素是 3x3 numpy 数组。
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t_arm: 平移向量的 Python list, 每个元素是 3x1 numpy 数组。
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"""
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R_arm = []
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t_arm = []
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for pose_matrix in self.robot_poses:
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# 1. 提取旋转矩阵 (3x3)
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rotation_matrix = pose_matrix[:3, :3]
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R_arm.append(rotation_matrix)
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# 2. 提取平移向量 (3x1)
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translation_vector = pose_matrix[:3, 3] # 获取齐次矩阵的平移部分 (1x3 numpy 数组)
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translation_vector = translation_vector.reshape(3, 1) # 转换为 3x1 的列向量
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t_arm.append(translation_vector)
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return R_arm, t_arm
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def calibrate(self):
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rvecs, tvecs = self.camera_calib()
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r_arm, t_arm= self.split_robot_pose()
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rvecs_array = np.array(rvecs)
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tvecs_array = np.array(tvecs)
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t_arma = np.array(t_arm)
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r_arma = np.array(r_arm)
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print(rvecs_array.shape)
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print(tvecs_array.shape)
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print(t_arma.shape)
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print(r_arma.shape)
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R, t = cv2.calibrateHandEye(r_arma, t_arma, rvecs_array, tvecs_array, cv2.CALIB_HAND_EYE_HORAUD)
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print("+++++++++++手眼标定完成+++++++++++++++")
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return R, t
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def init_chessboard_center(self,image):
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'''
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返回的是EE坐标下的中点
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'''
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intrinsics = self.intrinsics
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# 检测标定板获取初始位置
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ret, corners = cv2.findChessboardCorners(image, self.board_size)
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if not ret: raise ValueError("标定板未检测到")
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# 计算标定板中心在相机坐标系中的位置
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obj_pts = np.array([[i*self.square_size, j*self.square_size, 0] for i in range(self.board_size[0]) for j in range(self.board_size[1])], dtype=np.float32)
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print(intrinsics)
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camera_matrix = np.array([
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[intrinsics['fx'], 0, intrinsics['cx']],
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[0, intrinsics['fy'], intrinsics['cy']],
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[0, 0, 1]
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], dtype=np.float32)
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dist_coeffs = np.array([
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intrinsics['distortion_coeffs']['k1'],
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intrinsics['distortion_coeffs']['k2'],
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intrinsics['distortion_coeffs']['p1'],
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intrinsics['distortion_coeffs']['p2'],
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intrinsics['distortion_coeffs']['k3']
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], dtype=np.float32) # 同样确保数据类型
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print("obj_pts shape:", obj_pts.shape)
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print("corners shape:", corners.shape)
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ret, rvec, tvec = cv2.solvePnP(obj_pts, corners, camera_matrix, dist_coeffs)
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# 这里我们假设标定板的初始位置是原点，没有旋转和平移
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rvec_initial = np.zeros((3, 1), dtype=np.float32) # 初始旋转向量
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tvec_initial = np.zeros((3, 1), dtype=np.float32) # 初始平移向量
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# 初始变换（标定板到初始坐标系）
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initial_transform = np.eye(4)
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initial_transform[:3, :3], _ = cv2.Rodrigues(rvec_initial)
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initial_transform[:3, 3] = tvec_initial.flatten()
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print(f"board itselt {initial_transform}")
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# PnP 结果（初始坐标系到相机）
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cam2initial = np.eye(4)
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cam2initial[:3, :3], _ = cv2.Rodrigues(rvec)
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cam2initial[:3, 3] = tvec.flatten()
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# 合成最终变换：标定板 -> 初始坐标系 -> 相机
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board2cam = initial_transform @ cam2initial
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print(f"board2cam\n{board2cam}")
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# 手眼标定矩阵的逆（相机到TCP的固定变换）
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cam2tool = np.eye(4)
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cam2tool[:3, :3] = np.array([[0.0, 1.0 , 0.0],
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[1.0 , 0.0 , 0.0],
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[0.0, 0.0 , -1.0]])
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cam2tool[:3, 3] = np.array([0.00209053, 0.01021561, -0.16877656]).flatten()
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###找到棋盘格中心
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camera_center = None
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chessboard_center_object = self.get_chessboard_center_object_points()
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if chessboard_center_object is not None:
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# Convert to homogeneous coordinates
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chessboard_center_homogeneous = np.append(chessboard_center_object, 1)
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# Transform the center to camera coordinates
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camera_center_homogeneous = cam2tool@ board2cam @ chessboard_center_homogeneous
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camera_center = camera_center_homogeneous[:3]
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return camera_center
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def get_chessboard_center_object_points(self):
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rows, cols = self.board_size
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obj_pts = self.objp
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if obj_pts is None or len(obj_pts) != rows * cols:
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return None
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# Get the range of x and y indices
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x_indices = obj_pts[:, 0].flatten()
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y_indices = obj_pts[:, 1].flatten()
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center_x_index = (np.max(x_indices) + np.min(x_indices)) / 2.0
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center_y_index = (np.max(y_indices) + np.min(y_indices)) / 2.0
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center_z = 0 # Assuming the chessboard is on the Z=0 plane in its frame
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return np.array([center_x_index, center_y_index, center_z])
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def generate_perturbed_joint_angles_rad(self,original_angle):
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"""
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Args:
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original_angle (list or tuple): 包含6个角度值的列表或元组 (单位：度).
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Returns:
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numpy.ndarray: 一个形状为 (6, 6) 的 numpy 数组，包含6组6个角度值（单位：弧度）。
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每组数据的含义如下：
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- 第1组: 原始角度（转换为弧度）。
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- 第2组: 第五个关节角度 + rand5（转换为弧度）。
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- 第3组: 第五个关节角度 - rand5（转换为弧度）。
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- 第4组: 第四个关节角度 + rand4（转换为弧度）。
|
||
- 第5组: 第四个关节角度 - rand4（转换为弧度）。
|
||
- 第6组: 第四个关节角度 + rand4，第五个关节角度 + rand5（转换为弧度）。
|
||
"""
|
||
original_angle = np.array(original_angle)
|
||
if original_angle.shape != (6,):
|
||
raise ValueError("输入的角度列表必须包含6个元素。")
|
||
original_angle[5] = 0.0
|
||
|
||
# 生成随机数
|
||
rand5 = random.uniform(-20*np.pi/180, 20*np.pi/180) # 第五个关节的随机数，范围可以调整
|
||
rand4 = random.uniform(-10*np.pi/180, 10*np.pi/180) # 第四个关节的随机数，范围可以调整
|
||
|
||
|
||
angle_array = []
|
||
|
||
# 1. 原始角度
|
||
angle_array.append(original_angle.copy())
|
||
# 4. += rand4
|
||
angle_c = original_angle.copy()
|
||
angle_c[3] -= rand4
|
||
angle_c[4] -= rand5
|
||
angle_array.append(angle_c)
|
||
|
||
# 5. -= rand4
|
||
angle_d = original_angle.copy()
|
||
angle_d[3] += rand4
|
||
angle_d[4] += rand5
|
||
angle_array.append(angle_d)
|
||
|
||
# 6. +=rand4，-= rand5
|
||
angle_e = original_angle.copy()
|
||
angle_e[3] += rand4
|
||
angle_e[4] -= rand5
|
||
angle_array.append(angle_e)
|
||
|
||
# 6. -= rand4，第五个关节 += rand5
|
||
angle_f = original_angle.copy()
|
||
angle_f[3] -= rand4
|
||
angle_f[4] += rand5
|
||
angle_array.append(angle_f)
|
||
|
||
# 6. -= rand4，第五个关节 += rand5
|
||
angle_g = original_angle.copy()
|
||
angle_g[3] -= rand4/2
|
||
angle_g[4] += rand5/2
|
||
angle_array.append(angle_g)
|
||
|
||
angle_h = original_angle.copy()
|
||
angle_h[3] += rand4/2
|
||
angle_h[4] -= rand5/2
|
||
angle_array.append(angle_h)
|
||
|
||
# 将所有角度从度转换为弧度
|
||
# modified_angles_rad = np.array([np.deg2rad(angles) for angles in angle_array])
|
||
# import pdb
|
||
# pdb.set_trace()
|
||
|
||
return angle_array
|
||
|
||
def traversal_position_direction(self,path):
|
||
|
||
for pose in path:
|
||
current_pose = self.arm.robot_rpc_client.getRobotInterface(self.arm.robot_name).getRobotState().getTcpPose()
|
||
print(f" elipse path is {pose}\n current pose is {current_pose}" )
|
||
code = self.arm.move_line( pose )
|
||
|
||
if code == -1:
|
||
print(f"运动到拍照位置失败:{pose}")
|
||
else:
|
||
joint = self.arm.robot_rpc_client.getRobotInterface(self.arm.robot_name).getRobotState().getJointPositions()
|
||
angle_list = self.generate_perturbed_joint_angles_rad(joint)
|
||
print(f"\n {angle_list}\n Angles Position ")
|
||
for joint in angle_list:
|
||
print(f"current joint: {joint}")
|
||
code = self.arm.move_joint(joint,4,wait=True)
|
||
if code != -1:
|
||
print(f"move success")
|
||
self.check_corners_add_sample()
|
||
|
||
return len(self.robot_poses)
|
||
|
||
def visualization(self,center,trajectory,rpy_values):
|
||
# 可视化
|
||
fig = plt.figure(figsize=(10, 8))
|
||
ax = fig.add_subplot(111, projection='3d')
|
||
|
||
# 绘制中心点
|
||
ax.scatter(*center, color='r', marker='o', s=100, label='Center Point')
|
||
|
||
# 绘制轨迹
|
||
ax.plot(trajectory[:, 0], trajectory[:, 1], trajectory[:, 2], color='b', label='Tool Trajectory')
|
||
|
||
# 可视化 RPY 值 (每隔几个点显示一个)
|
||
num_samples_rpy = 20
|
||
indices = np.linspace(0, len(rpy_values) - 1, num_samples_rpy, dtype=int)
|
||
for i in indices:
|
||
tool_point = trajectory[i]
|
||
roll, pitch, yaw = rpy_values[i]
|
||
vector = tool_point - center
|
||
# 将箭头的起始点移动到 tool_point
|
||
ax.quiver(tool_point[0], tool_point[1], tool_point[2],
|
||
vector[0], vector[1], vector[2],
|
||
length=0.2, color='g', alpha=0.7, label='Vector Center to Tool' if i == indices[0] else "")
|
||
# 将文本标签的位置移动到 tool_point 附近
|
||
ax.text(tool_point[0] + 0.05, tool_point[1] + 0.05, tool_point[2] + 0.05,
|
||
f'RPY:({math.degrees(roll):.2f},{math.degrees(pitch):.2f},{math.degrees(yaw):.2f})',
|
||
color='g', fontsize=8)
|
||
|
||
ax.set_xlabel('X')
|
||
ax.set_ylabel('Y')
|
||
ax.set_zlabel('Z')
|
||
ax.set_title('Cyclic Trajectory Above Center Point')
|
||
ax.legend()
|
||
ax.view_init(azim=-120, elev=30)
|
||
plt.grid(True)
|
||
plt.show()
|
||
|
||
# 打印 RPY 值 (示例)
|
||
# print("\nRoll, Pitch, Yaw values (degrees) along the trajectory:")
|
||
# for i, (r, p, y) in enumerate(rpy_values):
|
||
# if i % 10 == 0: # Print every 10th value
|
||
# print(f"Step {i}: Roll: {math.degrees(r):.2f}, Pitch: {math.degrees(p):.2f}, Yaw: {math.degrees(y):.2f}")
|
||
|
||
|
||
if __name__ == '__main__':
|
||
|
||
# 标定板的width 对应的角点的个数 6
|
||
# 标定板的height 对应的角点的个数 3
|
||
calibration_board_size = [9,6]
|
||
calibration_board_square_size = 0.020 #unit - meter
|
||
systemPath = "/home/jsfb/Documents/"
|
||
|
||
now = datetime.now()
|
||
date_time_str = now.strftime("%d_%H_%M")
|
||
folder_name = f"calibration_data_{date_time_str}"
|
||
systemPath+=folder_name
|
||
os.makedirs(systemPath, exist_ok=True)
|
||
|
||
calib = Calibration(calibration_board_size,calibration_board_square_size,systemPath)
|
||
calib.collect_data()
|
||
r, t = calib.calibrate()
|
||
print("旋转矩阵：")
|
||
print(r)
|
||
print("平移向量：")
|
||
print(t)
|
||
|
||
|
||
|
||
# arm = AuboC5()
|
||
# arm.init()
|
||
# aubo_algorithm = arm.robot_rpc_client.getRobotInterface(arm.robot_name).getRobotAlgorithm().inverseKinematics()
|
||
|
||
# # 查看 rpc_client 对象的所有属性和方法
|
||
# print(dir(aubo_algorithm))
|
||
|