Struggeling with Jacobian Inverse Kinematics









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I am really struggling to find the right Jacobian for my 4DOF robot arm in 3D-space. First joint rotates around the y-axis, second around z-axis, third around z-axis and last one around the y-axis. All links are 1 unit long. My problem is probably the orientation vector zi because I know that my Forward Kinematics are correct. j1_pos-j4_pos are the positions of the joints. ee_pos is the end-effector. All positions are correct. My thought was just to multiply the respective axis vectors with the transformation matrices to get the orientation vectors zi.



Any advice would be much appreciated.



I am using the formula



J = [Jpi] = [zi x (pe - pi)]
[Joi] [ zi ]




def Jacobian(self,joint_angles):
jacobian = np.zeros((6,4))

j1_trans = self.link_transform_y(joint_angles[0])
j2_trans = self.link_transform_z(joint_angles[1])
j3_trans = self.link_transform_z(joint_angles[2])
j4_trans = self.link_transform_y(joint_angles[3])

ee_pos = (j1_trans*j2_trans*j3_trans*j4_trans)[0:3, 3]
j4_pos = (j1_trans*j2_trans*j3_trans)[0:3, 3]
j3_pos = (j1_trans*j2_trans)[0:3, 3]
j2_pos = (j1_trans)[0:3, 3]
j1_pos = np.zeros((3,1))

pos3D = np.zeros(3)

pos3D = (ee_pos-j1_pos).T
z0_vector = [0, 1, 0]
jacobian[0:3, 0] = np.cross(z0_vector, pos3D)
pos3D[0:3] = (ee_pos-j2_pos).T

z1_vector = (j1_trans*np.array([0, 0, 1, 0]).reshape(4,1))[0:3].T

jacobian[0:3, 1] = np.cross(z1_vector, pos3D)
pos3D[0:3] = (ee_pos-j3_pos).T

z2_vector = (j1_trans*j2_trans*np.array([0, 0, 1, 0]).reshape(4,1))[0:3].T

jacobian[0:3, 2] = np.cross(z2_vector, pos3D)
pos3D[0:3] = (ee_pos-j4_pos).T

z3_vector = (j1_trans*j2_trans*j3_trans*np.array([0, 1, 0, 0]).reshape(4,1))[0:3].T

jacobian[0:3, 3] = np.cross(z3_vector, pos3D)

jacobian[3:6, 0] = z0_vector
jacobian[3:6, 1] = z1_vector
jacobian[3:6, 2] = z2_vector
jacobian[3:6, 3] = z3_vector


return jacobian









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    down vote

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    I am really struggling to find the right Jacobian for my 4DOF robot arm in 3D-space. First joint rotates around the y-axis, second around z-axis, third around z-axis and last one around the y-axis. All links are 1 unit long. My problem is probably the orientation vector zi because I know that my Forward Kinematics are correct. j1_pos-j4_pos are the positions of the joints. ee_pos is the end-effector. All positions are correct. My thought was just to multiply the respective axis vectors with the transformation matrices to get the orientation vectors zi.



    Any advice would be much appreciated.



    I am using the formula



    J = [Jpi] = [zi x (pe - pi)]
    [Joi] [ zi ]




    def Jacobian(self,joint_angles):
    jacobian = np.zeros((6,4))

    j1_trans = self.link_transform_y(joint_angles[0])
    j2_trans = self.link_transform_z(joint_angles[1])
    j3_trans = self.link_transform_z(joint_angles[2])
    j4_trans = self.link_transform_y(joint_angles[3])

    ee_pos = (j1_trans*j2_trans*j3_trans*j4_trans)[0:3, 3]
    j4_pos = (j1_trans*j2_trans*j3_trans)[0:3, 3]
    j3_pos = (j1_trans*j2_trans)[0:3, 3]
    j2_pos = (j1_trans)[0:3, 3]
    j1_pos = np.zeros((3,1))

    pos3D = np.zeros(3)

    pos3D = (ee_pos-j1_pos).T
    z0_vector = [0, 1, 0]
    jacobian[0:3, 0] = np.cross(z0_vector, pos3D)
    pos3D[0:3] = (ee_pos-j2_pos).T

    z1_vector = (j1_trans*np.array([0, 0, 1, 0]).reshape(4,1))[0:3].T

    jacobian[0:3, 1] = np.cross(z1_vector, pos3D)
    pos3D[0:3] = (ee_pos-j3_pos).T

    z2_vector = (j1_trans*j2_trans*np.array([0, 0, 1, 0]).reshape(4,1))[0:3].T

    jacobian[0:3, 2] = np.cross(z2_vector, pos3D)
    pos3D[0:3] = (ee_pos-j4_pos).T

    z3_vector = (j1_trans*j2_trans*j3_trans*np.array([0, 1, 0, 0]).reshape(4,1))[0:3].T

    jacobian[0:3, 3] = np.cross(z3_vector, pos3D)

    jacobian[3:6, 0] = z0_vector
    jacobian[3:6, 1] = z1_vector
    jacobian[3:6, 2] = z2_vector
    jacobian[3:6, 3] = z3_vector


    return jacobian









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      I am really struggling to find the right Jacobian for my 4DOF robot arm in 3D-space. First joint rotates around the y-axis, second around z-axis, third around z-axis and last one around the y-axis. All links are 1 unit long. My problem is probably the orientation vector zi because I know that my Forward Kinematics are correct. j1_pos-j4_pos are the positions of the joints. ee_pos is the end-effector. All positions are correct. My thought was just to multiply the respective axis vectors with the transformation matrices to get the orientation vectors zi.



      Any advice would be much appreciated.



      I am using the formula



      J = [Jpi] = [zi x (pe - pi)]
      [Joi] [ zi ]




      def Jacobian(self,joint_angles):
      jacobian = np.zeros((6,4))

      j1_trans = self.link_transform_y(joint_angles[0])
      j2_trans = self.link_transform_z(joint_angles[1])
      j3_trans = self.link_transform_z(joint_angles[2])
      j4_trans = self.link_transform_y(joint_angles[3])

      ee_pos = (j1_trans*j2_trans*j3_trans*j4_trans)[0:3, 3]
      j4_pos = (j1_trans*j2_trans*j3_trans)[0:3, 3]
      j3_pos = (j1_trans*j2_trans)[0:3, 3]
      j2_pos = (j1_trans)[0:3, 3]
      j1_pos = np.zeros((3,1))

      pos3D = np.zeros(3)

      pos3D = (ee_pos-j1_pos).T
      z0_vector = [0, 1, 0]
      jacobian[0:3, 0] = np.cross(z0_vector, pos3D)
      pos3D[0:3] = (ee_pos-j2_pos).T

      z1_vector = (j1_trans*np.array([0, 0, 1, 0]).reshape(4,1))[0:3].T

      jacobian[0:3, 1] = np.cross(z1_vector, pos3D)
      pos3D[0:3] = (ee_pos-j3_pos).T

      z2_vector = (j1_trans*j2_trans*np.array([0, 0, 1, 0]).reshape(4,1))[0:3].T

      jacobian[0:3, 2] = np.cross(z2_vector, pos3D)
      pos3D[0:3] = (ee_pos-j4_pos).T

      z3_vector = (j1_trans*j2_trans*j3_trans*np.array([0, 1, 0, 0]).reshape(4,1))[0:3].T

      jacobian[0:3, 3] = np.cross(z3_vector, pos3D)

      jacobian[3:6, 0] = z0_vector
      jacobian[3:6, 1] = z1_vector
      jacobian[3:6, 2] = z2_vector
      jacobian[3:6, 3] = z3_vector


      return jacobian









      share|improve this question















      I am really struggling to find the right Jacobian for my 4DOF robot arm in 3D-space. First joint rotates around the y-axis, second around z-axis, third around z-axis and last one around the y-axis. All links are 1 unit long. My problem is probably the orientation vector zi because I know that my Forward Kinematics are correct. j1_pos-j4_pos are the positions of the joints. ee_pos is the end-effector. All positions are correct. My thought was just to multiply the respective axis vectors with the transformation matrices to get the orientation vectors zi.



      Any advice would be much appreciated.



      I am using the formula



      J = [Jpi] = [zi x (pe - pi)]
      [Joi] [ zi ]




      def Jacobian(self,joint_angles):
      jacobian = np.zeros((6,4))

      j1_trans = self.link_transform_y(joint_angles[0])
      j2_trans = self.link_transform_z(joint_angles[1])
      j3_trans = self.link_transform_z(joint_angles[2])
      j4_trans = self.link_transform_y(joint_angles[3])

      ee_pos = (j1_trans*j2_trans*j3_trans*j4_trans)[0:3, 3]
      j4_pos = (j1_trans*j2_trans*j3_trans)[0:3, 3]
      j3_pos = (j1_trans*j2_trans)[0:3, 3]
      j2_pos = (j1_trans)[0:3, 3]
      j1_pos = np.zeros((3,1))

      pos3D = np.zeros(3)

      pos3D = (ee_pos-j1_pos).T
      z0_vector = [0, 1, 0]
      jacobian[0:3, 0] = np.cross(z0_vector, pos3D)
      pos3D[0:3] = (ee_pos-j2_pos).T

      z1_vector = (j1_trans*np.array([0, 0, 1, 0]).reshape(4,1))[0:3].T

      jacobian[0:3, 1] = np.cross(z1_vector, pos3D)
      pos3D[0:3] = (ee_pos-j3_pos).T

      z2_vector = (j1_trans*j2_trans*np.array([0, 0, 1, 0]).reshape(4,1))[0:3].T

      jacobian[0:3, 2] = np.cross(z2_vector, pos3D)
      pos3D[0:3] = (ee_pos-j4_pos).T

      z3_vector = (j1_trans*j2_trans*j3_trans*np.array([0, 1, 0, 0]).reshape(4,1))[0:3].T

      jacobian[0:3, 3] = np.cross(z3_vector, pos3D)

      jacobian[3:6, 0] = z0_vector
      jacobian[3:6, 1] = z1_vector
      jacobian[3:6, 2] = z2_vector
      jacobian[3:6, 3] = z3_vector


      return jacobian






      python robotics inverse-kinematics






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      edited Nov 11 at 1:13









      Thomas Fritsch

      4,595121833




      4,595121833










      asked Nov 10 at 15:19









      Frederik Kelbel

      13




      13



























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