网站怎么做 织梦,wordpress首页最新文章,wordpress next page,展览馆展示设计coppelia sim[V-REP]仿真实现 机器人于3D相机手眼标定与实时视觉追踪 二 zmq API接口python调用python获取3D相机的数据获取彩色相机的数据获取深度相机的数据用matpolit显示 python控制机器人运动直接控制轴的位置用IK运动学直接移动到末端姿态 相机内参的标定记录拍照点的位置… coppelia sim[V-REP]仿真实现 机器人于3D相机手眼标定与实时视觉追踪 二 zmq API接口python调用python获取3D相机的数据获取彩色相机的数据获取深度相机的数据用matpolit显示 python控制机器人运动直接控制轴的位置用IK运动学直接移动到末端姿态 相机内参的标定记录拍照点的位置标定板大小的及坐标的设置初始化获取相关的资源句柄 机器人运动参数设置进行拍照和内参标定内参标定结果及标定板的姿态显示进行手眼标定参数说明标定结果说明 Coppelia Sim (v-REP)仿真 机器人3D相机手眼标定与实时视觉追踪 一
zmq API接口python调用
在官方提供的例子里面已经包含了调用的例子程序把coppeliasim_zmqremoteapi_client 文件夹拷贝过来就直接可以用
import time
# from zmqRemoteApi import RemoteAPIClient
from coppeliasim_zmqremoteapi_client import RemoteAPIClientdef myFunc(input1, input2):print(Hello, input1, input2)return 21print(Program started)client RemoteAPIClient()
sim client.require(sim)# Create a few dummies and set their positions:
handles [sim.createDummy(0.01, 12 * [0]) for _ in range(50)]
for i, h in enumerate(handles):sim.setObjectPosition(h, -1, [0.01 * i, 0.01 * i, 0.01 * i])# Run a simulation in asynchronous mode:
sim.startSimulation()
while (t : sim.getSimulationTime()) 3:s fSimulation time: {t:.2f} [s] (simulation running asynchronously \to client, i.e. non-stepping)print(s)sim.addLog(sim.verbosity_scriptinfos, s)python获取3D相机的数据
获取彩色相机的数据 visionSensorHandle sim.getObject(/UR5/3D_camera)img, [resX, resY] sim.getVisionSensorImg(visionSensorHandle)img np.frombuffer(img, dtypenp.uint8).reshape(resY, resX, 3)# In CoppeliaSim images are left to right (x-axis), and bottom to top (y-axis)# (consistent with the axes of vision sensors, pointing Z outwards, Y up)# and color format is RGB triplets, whereas OpenCV uses BGR:img cv2.flip(cv2.cvtColor(img, cv2.COLOR_BGR2RGB), 0)获取深度相机的数据 deepSensorHandle sim.getObject(/UR5/3D_camera/depth)# 获取深度图像Deepdate sim.getVisionSensorDepth(deepSensorHandle, 1)# 解包为浮点数数组num_floats Deepdate[1][0] * Deepdate[1][1]depth_data struct.unpack(f{num_floats}f, Deepdate[0])# 将数据转为 numpy 数组depth_array np.array(depth_data)depth_image depth_array.reshape((Deepdate[1][1], Deepdate[1][0]))depth_image np.flipud(depth_image)deepcolor encoding.FloatArrayToRgbImage(depth_image, scale_factor256*3.5*1000)用matpolit显示
class ImageStreamDisplay:def __init__(self, resolution):# 创建一个包含两个子图的图形self.fig, (self.ax1, self.ax2) plt.subplots(1, 2, figsize(10, 6)) # 1行2列# 设置子图的标题self.ax1.set_title(RGB img)self.ax2.set_title(Depth img)# 初始化两个图像显示对象使用零数组作为占位符并设置animatedTrue以启用动画self.im_display1 self.ax1.imshow(np.zeros([resolution[1], resolution[0], 3]), animatedTrue)self.im_display2 self.ax2.imshow(np.zeros([resolution[1], resolution[0]]), animatedTrue, cmapgray, vmin0, vmax3.5)# 自动调整子图布局以避免重叠self.fig.tight_layout()# 开启交互模式plt.ion()# 显示图形plt.show()def displayUpdated(self, rgbBuffer1, rgbBuffer2):# 更新两个图像显示对象# 注意对于imshow通常使用set_data而不是set_arrayself.im_display1.set_data(rgbBuffer1)self.im_display2.set_data(rgbBuffer2)# plt.colorbar()# 刷新事件并暂停以更新显示self.fig.canvas.flush_events()plt.pause(0.01) # 暂停一小段时间以允许GUI更新display ImageStreamDisplay([640, 480])display.displayUpdated(img, depth_image)显示的效果
python控制机器人运动
直接控制轴的位置
def moveToConfig(robotColor, handles, maxVel, maxAccel, maxJerk, targetConf):sim robotColorcurrentConf []for i in range(len(handles)):currentConf.append(sim.getJointPosition(handles[i]))params {}params[joints] handlesparams[targetPos] targetConfparams[maxVel] maxVelparams[maxAccel] maxAccelparams[maxJerk] maxJerk# one could also use sim.moveToConfig_init, sim.moveToConfig_step and sim.moveToConfig_cleanupsim.moveToConfig(params)用IK运动学直接移动到末端姿态
def MovetoPose(robothandle, maxVel, maxAccel, maxJerk, targetpost):sim robothandleparams[ik] {tip: tipHandle, target: targetHandle}# params[object] targetHandleparams[targetPose] targetpostparams[maxVel] maxVelparams[maxAccel] maxAccelparams[maxJerk] maxJerksim.moveToPose(params)相机内参的标定
记录拍照点的位置
targetjoinPos1 [0 * math.pi / 180, 45 * math.pi / 180, -60 *math.pi / 180, 0 * math.pi / 180, 90 * math.pi / 180, 0 * math.pi / 180]
targetPos []targetPos1 [-0.1100547923916193, -0.01595811170705489, 0.8466254222789784,0.560910589456253, -0.5609503047415633, 0.4305463900323013, 0.43051582116844406]
targetPos2 [-0.16595811170705488, -0.01595811170705487, 0.8216254222789784,0.5417925804901749, -0.4495188049772251, 0.575726166263469, 0.415852167455231]
targetPos3 [-0.1100547923916193, -0.01595811170705487, 0.8216254222789784,3.21292597227468e-05, 0.7932742610364036, -0.6088644721541127, 1.7127530004424877e-05]
targetPos4 [-0.1100547923916193, -0.01595811170705489, 0.9216254222789784,1.4077336111962496e-05, 0.7932754374087434, -0.608862940314109, 1.085602215830202e-05]
targetPos5 [-0.1100547923916193, -0.015958111707054884, 0.8216254222789784,0.579152193496431, -0.5416388515007398, 0.4546027806731813, 0.40564319680902283]
targetPos6 [-0.1100547923916193, -0.015958111707054884, 0.8216254222789784,0.5415953148716405, -0.5791980963813808, 0.4056650408034863, 0.45457667640033966]
targetPos7 [-0.2100547923916193, -0.015958111707054898, 0.8816254222789784,0.5212515544329563, -0.5212938037943111, 0.4777945964218841, 0.47776763259657323]
targetPos8 [-0.15000547923916193, -0.015958111707054898, 0.8016254222789784,0.5212515544329563, -0.5212938037943111, 0.4777945964218841, 0.47776763259657323]
targetPos9 [-0.15000547923916193, -0.015958111707054898, 0.8016254222789784,0.5212515544329563, -0.5212938037943111, 0.4777945964218841, 0.47776763259657323]
targetPos10 [-0.1010000547923916193, -0.015958111707054898, 0.8016254222789784,0.5212515544329563, -0.5212938037943111, 0.4777945964218841, 0.47776763259657323]
targetPos11 []
targetPos12 []
targetPos.append(targetPos1)
targetPos.append(targetPos2)
targetPos.append(targetPos3)
targetPos.append(targetPos4)
targetPos.append(targetPos5)
targetPos.append(targetPos6)
targetPos.append(targetPos7)
targetPos.append(targetPos8)
targetPos.append(targetPos9)
targetPos.append(targetPos10)标定板大小的及坐标的设置
# 设置棋盘格规格内角点的数量
chessboard_size (10, 7)# 3D 世界坐标的点准备标定板的真实物理尺寸
square_size 0.015 # 假设每个格子的边长是15毫米可以根据实际情况调整
objp np.zeros((chessboard_size[0]*chessboard_size[1], 3), np.float32)
objp[:, :2] np.mgrid[0:chessboard_size[0],0:chessboard_size[1]].T.reshape(-1, 2) * square_size# 储存所有图片的3D点和2D点
objpoints [] # 3D 点 (真实世界空间坐标)
imgpoints [] # 2D 点 (图像平面中的点)
robot_poses_R [] # 10 个旋转矩阵
robot_poses_t [] # 10 个平移向量
camera_poses_R [] # 10 个相机的旋转矩阵
camera_poses_t [] # 10 个相机的平移向量初始化获取相关的资源句柄
targetHandle sim.getObject(/UR5/Target)
tipHandle sim.getObject(/UR5/Tip)
robotHandle sim.getObject(/UR5)visionSensorHandle sim.getObject(/UR5/3D_camera)
deepSensorHandle sim.getObject(/UR5/3D_camera/depth)
chessboardHandle sim.getObject(/Plane)jointHandles []
for i in range(6):jointHandles.append(sim.getObject(/UR5/joint, {index: i}))机器人运动参数设置
jvel 310 * math.pi / 180
jaccel 100 * math.pi / 180
jjerk 80 * math.pi / 180jmaxVel [jvel, jvel, jvel, jvel, jvel, jvel]
jmaxAccel [jaccel, jaccel, jaccel, jaccel, jaccel, jaccel]
jmaxJerk [jjerk, jjerk, jjerk, jjerk, jjerk, jjerk]vel 30
accel 1.0
jerk 1maxVel [vel, vel, vel, vel]
maxAccel [accel, accel, accel, accel]
maxJerk [jerk, jerk, jerk, jerk]进行拍照和内参标定
print(------Perform camera calibration------)
# 初始拍照位置
moveToConfig(sim, jointHandles, jmaxVel, jmaxAccel, jmaxJerk, targetjoinPos1)
index 0
for i in range(10):goalTr targetPos[i].copy()params {}params[ik] {tip: tipHandle, target: targetHandle}# params[object] targetHandleparams[targetPose] goalTrparams[maxVel] maxVelparams[maxAccel] maxAccelparams[maxJerk] maxJerksim.moveToPose(params)# 这里是尝试转动角度的# EulerAnglessim.getObjectOrientation(targetHandle,tipHandle)# # EulerAngles[1]15* math.pi / 180# EulerAngles[0]25* math.pi / 180# sim.setObjectOrientation(targetHandle,EulerAngles,tipHandle)# goalTrsim.getObjectPose(targetHandle,robotHandle)# # goalTr[0]goalTr[1]-0.15# params {}# params[ik] {tip: tipHandle, target: targetHandle}# # params[object] targetHandle# params[targetPose] goalTr# params[maxVel] maxVel# params[maxAccel] maxAccel# params[maxJerk] maxJerk# sim.moveToPose(params)img, [resX, resY] sim.getVisionSensorImg(visionSensorHandle)img np.frombuffer(img, dtypenp.uint8).reshape(resY, resX, 3)# In CoppeliaSim images are left to right (x-axis), and bottom to top (y-axis)# (consistent with the axes of vision sensors, pointing Z outwards, Y up)# and color format is RGB triplets, whereas OpenCV uses BGR:img cv2.flip(cv2.cvtColor(img, cv2.COLOR_BGR2RGB), 0)# imgcv2.cvtColor(img, cv2.COLOR_BGR2RGB)gray cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)# 找到棋盘格的角点ret, corners cv2.findChessboardCorners(gray, chessboard_size, None)# 如果找到角点添加到点集if ret True:objpoints.append(objp)imgpoints.append(corners)# 显示角点img cv2.drawChessboardCorners(img, chessboard_size, corners, ret)# 使用 solvePnP 获取旋转向量和平移向量# ret, rvec, tvec cv2.solvePnP(objpoints, imgpoints, camera_matrix, dist_coeffs)# 获取深度图像Deepdate sim.getVisionSensorDepth(deepSensorHandle, 1)# 解包为浮点数数组num_floats Deepdate[1][0] * Deepdate[1][1]depth_data struct.unpack(f{num_floats}f, Deepdate[0])# 将数据转为 numpy 数组depth_array np.array(depth_data)depth_image depth_array.reshape((Deepdate[1][1], Deepdate[1][0]))depth_image np.flipud(depth_image)deepcolor encoding.FloatArrayToRgbImage(depth_image, scale_factor256*3.5*1000)display.displayUpdated(img, depth_image)# 进行相机标定
ret, mtx, dist, rvecs, tvecs cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)# 计算标定的误差
total_error 0
errors []
# 遍历每张图像
for objp, imgp, rvec, tvec in zip(objpoints, imgpoints, rvecs, tvecs):# 将三维物体点转换为相机坐标系下的二维图像点projected_imgpoints, _ cv2.projectPoints(objp, rvec, tvec, mtx, dist)# 计算原始 imgpoints 和 projected_imgpoints 之间的误差error cv2.norm(imgp, projected_imgpoints, cv2.NORM_L2) / \len(projected_imgpoints)errors.append(error)total_error error# 计算所有图像的平均误差
mean_error total_error / len(objpoints)# 打印每幅图像的误差
for i, error in enumerate(errors):print(f图像 {i1} 的误差: {error})# 打印平均误差
print(f所有图像的平均误差: {mean_error})# 打印相机内参和畸变系数
print(Camera Matrix:\n, mtx)
print(Distortion Coefficients:\n, dist)
print(camera carlibraed Done)fig plt.figure()
ax fig.add_subplot(111, projection3d)内参标定结果及标定板的姿态显示
------Perform camera calibration------
图像 1 的误差: 0.02561960222938916
图像 2 的误差: 0.029812235820557646
图像 3 的误差: 0.025746131185086674
图像 4 的误差: 0.026949289915749124
图像 5 的误差: 0.02779773110640927
图像 6 的误差: 0.02970027985427542
图像 7 的误差: 0.02793374910087122
图像 8 的误差: 0.025625364208011148
图像 9 的误差: 0.02599937961290829
图像 10 的误差: 0.028105922281836906
所有图像的平均误差: 0.027328968531509484
Camera Matrix:[[581.55412313 0. 317.88732972][ 0. 581.24563369 240.0350863 ][ 0. 0. 1. ]]
Distortion Coefficients:[[-0.0309966 0.15304087 0.00125555 -0.00093788 -0.27333318]]
camera carlibraed Done进行手眼标定
参数说明
相机坐标系下的点可以根据内参标定的结果使用solvePnP获取棋盘格在相机坐标下的3D点用sim.getObjectPose获取到机器人的姿态
print(------Perform hand-eye calibration------)
# 初始拍照位置
moveToConfig(sim, jointHandles, jmaxVel, jmaxAccel, jmaxJerk, targetjoinPos1)for i in range(10):goalTr targetPos[i].copy()# goalTr[2] goalTr[2] - 0.2params {}params[ik] {tip: tipHandle, target: targetHandle}# params[object] targetHandleparams[targetPose] goalTrparams[maxVel] maxVelparams[maxAccel] maxAccelparams[maxJerk] maxJerksim.moveToPose(params)img, [resX, resY] sim.getVisionSensorImg(visionSensorHandle)img np.frombuffer(img, dtypenp.uint8).reshape(resY, resX, 3)# In CoppeliaSim images are left to right (x-axis), and bottom to top (y-axis)# (consistent with the axes of vision sensors, pointing Z outwards, Y up)# and color format is RGB triplets, whereas OpenCV uses BGR:img cv2.flip(cv2.cvtColor(img, cv2.COLOR_BGR2RGB), 0)# imgcv2.cvtColor(img, cv2.COLOR_BGR2RGB)gray cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)# 找到棋盘格的角点ret, corners cv2.findChessboardCorners(gray, chessboard_size, None)# 如果找到角点添加到点集if ret True:# 使用 solvePnP 获取旋转向量和平移向量ret, rvec, tvec cv2.solvePnP(objp, corners, mtx, dist)# 将 rvec 转换为旋转矩阵R, _ cv2.Rodrigues(rvec)camera_tvec_matrix tvec.reshape(-1)# 获取到机器人夹爪的姿态Targetpost sim.getObjectPose(tipHandle)pose_matrix sim.poseToMatrix(Targetpost)# 提取旋转矩阵 (3x3)robot_rotation_matrix np.array([[pose_matrix[0], pose_matrix[1], pose_matrix[2]],[pose_matrix[4], pose_matrix[5], pose_matrix[6]],[pose_matrix[8], pose_matrix[9], pose_matrix[10]]])# 提取平移向量 (P0, P1, P2)robot_tvec_matrix np.array([pose_matrix[3], pose_matrix[7], pose_matrix[11]])# 加入到手眼标定数据robot_poses_R.append(robot_rotation_matrix)robot_poses_t.append(robot_tvec_matrix)camera_poses_R.append(R)camera_poses_t.append(camera_tvec_matrix)img draw_axes(img, mtx, dist, rvec, tvec, 0.015*7)# 获取深度图像Deepdate sim.getVisionSensorDepth(deepSensorHandle, 1)# 解包为浮点数数组num_floats Deepdate[1][0] * Deepdate[1][1]depth_data struct.unpack(f{num_floats}f, Deepdate[0])# 将数据转为 numpy 数组depth_array np.array(depth_data)depth_image depth_array.reshape((Deepdate[1][1], Deepdate[1][0]))depth_image np.flipud(depth_image)deepcolor encoding.FloatArrayToRgbImage(depth_image, scale_factor256*3.5*1000)display.displayUpdated(img, depth_image)print(开始手眼标定...)
R_cam2gripper, t_cam2gripper cv2.calibrateHandEye(robot_poses_R, robot_poses_t, camera_poses_R, camera_poses_t, methodcv2.CALIB_HAND_EYE_TSAI
)camera_pose_intip sim.getObjectPose(visionSensorHandle, tipHandle)
camera_matrix_intip sim.poseToMatrix(camera_pose_intip)
eulerAngles sim.getEulerAnglesFromMatrix(camera_matrix_intip)print(相机到机器人末端的旋转矩阵\n, R_cam2gripper)
print(相机到机器人末端的平移向量\n, t_cam2gripper)标定结果说明
开始手眼标定...
相机到机器人末端的旋转矩阵[[ 0.00437374 -0.99998866 0.00188594][ 0.99998691 0.00436871 -0.00266211][ 0.00265384 0.00189756 0.99999468]]
相机到机器人末端的平移向量[[ 0.04918588][-0.00012231][ 0.00194761]]
手眼标定完成在虚拟环境中量出的结果和相机的标定结果由于图像的坐标系和Sim的坐标系的坐标轴是相差90°所有顺序不一样
接下来使用标定好的结果进行跟踪标定板的位置
