使用标定好的结果进行跟踪标定板的位置
坐标转换的步骤为:
1.图像坐标点转到相机坐标系下的点
2.相机坐标系下的点转为夹爪坐标系下的点
3.夹爪坐标系下的点转为机械手极坐标系下的点
跟踪的方式
1.采用标定板的第一个坐标点作为跟踪点
3.机器人每次移动到该点位,测试姿态是不是正确
python
# 测试标定的结果:
# 初始拍照位置
moveToConfig(sim, jointHandles, jmaxVel, jmaxAccel, jmaxJerk, targetjoinPos1)
# 10个拍照位置的验证
# for i in range(10):
while True:
goalTr = targetPos[i].copy()
# goalTr[2] = goalTr[2] - 0.2
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, dtype=np.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)
# img=cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# 获取深度图像
Deepdate = sim.getVisionSensorDepth(deepSensorHandle, 1)
num_floats = Deepdate[1][0] * Deepdate[1][1]
depth_data = struct.unpack(f'{num_floats}f', Deepdate[0])
depth_array = np.array(depth_data)
depth_image = depth_array.reshape((Deepdate[1][1], Deepdate[1][0]))
depth_image = np.flipud(depth_image)
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,使用第一个角点来转换为机器人的坐标
if ret == True:
u = corners[0][0][0]
v = corners[0][0][1]
Z = depth_image[int(v), int(u)]
img = cv2.drawChessboardCorners(img, chessboard_size, corners, ret)
ret, rvec, tvec = cv2.solvePnP(objp, corners, mtx, dist)
# 计算相机坐标系下的三维点
P_cam = pixel_to_camera_coordinates(u, v, Z, mtx)
print("相机坐标系下的三维点 P_cam:", P_cam)
# t_cam2gripper=t_cam2gripper.reshape(-1)
# 计算物体在手爪坐标系中的位置
# 计算点在末端坐标系下的坐标 P_end
P_end = np.dot(R_cam2gripper, P_cam) + t_cam2gripper.reshape(-1)
# sim.setObjectPosition(targetHandle, P_end,tipHandle)
# 计算点在基座坐标系下的坐标 P_base
tip_matrix = sim.getObjectMatrix(tipHandle)
# 提取旋转矩阵 R_end_to_base (3x3)
R_end_to_base = np.array([
[tip_matrix[0], tip_matrix[1], tip_matrix[2]],
[tip_matrix[4], tip_matrix[5], tip_matrix[6]],
[tip_matrix[8], tip_matrix[9], tip_matrix[10]]
])
# 提取平移向量 t_end_to_base (3x1)
t_end_to_base = np.array([
[tip_matrix[3]],
[tip_matrix[7]],
[tip_matrix[11]]
])
P_base = np.dot(R_end_to_base, P_end) + t_end_to_base.reshape(-1)
# sim.setObjectPosition(targetHandle, P_base)
Tip_pose = sim.getObjectPose(tipHandle)
# 将旋转向量转换为旋转矩阵
R_board_to_camera, _ = cv2.Rodrigues(rvec)
# 计算标定板相对于末端的旋转矩阵和平移向量
R_board_to_end = R_cam2gripper @ R_board_to_camera
t_board_to_end = R_cam2gripper @ tvec.flatten() + t_cam2gripper.flatten()
# 计算标定板相对于世界坐标系的旋转矩阵和平移向量
R_board_to_world = R_end_to_base @ R_board_to_end
t_board_to_world = R_end_to_base @ t_board_to_end + t_end_to_base.flatten()
chessboard_matrix = sim.getObjectMatrix(targetHandle)
cal_chessboard_matrix = np.array([R_board_to_world[0][0], R_board_to_world[0][1], R_board_to_world[0][2], t_board_to_world[0],
R_board_to_world[1][0], R_board_to_world[1][1], R_board_to_world[1][2], t_board_to_world[1],
R_board_to_world[2][0], R_board_to_world[2][1], R_board_to_world[2][2], t_board_to_world[2]])
sim.setObjectMatrix(targetHandle, cal_chessboard_matrix)
goalTr = Tip_pose.copy()
goalTr[0]=P_base[0]
goalTr[1]=P_base[1]
goalTr[2]=P_base[2]
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)
display.displayUpdated(img, depth_image)
测试的结果的