How to Make a Mobile Robot in Gazebo (ROS2 Foxy)

In this tutorial, we will learn how to make a model of a mobile robot in Gazebo from scratch. Our simulated robot will be a wheeled mobile robot. It will have two big wheels on each side and a caster wheel in the middle. Here is what you will build. In this case, I have the robot going in reverse (i.e. the big wheels are on the front of the vehicle):

wheeled-robot-gazebo

Prerequisites

Setup the Model Directory

Here are the official instructions, but we’ll walk through all the steps below. It is important to go slow and build your robotic models in small steps. No need to hurry. 

Create a folder for the model.

mkdir -p ~/.gazebo/models/my_robot

Create a model config file. This file will contain a description of the model.

gedit ~/.gazebo/models/my_robot/model.config

Add the following lines to the file, and then Save. You can see this file contains fields for the name of the robot, the version, the author (that’s you), your e-mail address, and a description of the robot.

<?xml version="1.0"?>
<model>
  <name>My Robot</name>
  <version>1.0</version>
  <sdf version='1.4'>model.sdf</sdf>

  <author>
   <name>My Name</name>
   <email>me@my.email</email>
  </author>

  <description>
    My awesome robot.
  </description>
</model>

Close the config file.

Now, let’s create an SDF (Simulation Description Format) file. This file will contain the tags that are needed to create an instance of the my_robot model. 

gedit ~/.gazebo/models/my_robot/model.sdf

Copy and paste the following lines inside the sdf file.

<?xml version='1.0'?>
<sdf version='1.4'>
  <model name="my_robot">
  </model>
</sdf>

Save the file, but don’t close it yet.

Create the Structure of the Model

Now we need to create the structure of the robot. We will start out by adding basic shapes. While we are creating the robot, we want Gazebo’s physics engine to ignore the robot. Otherwise the robot will move around the environment as we add more stuff on it.

To get the physics engine to ignore the robot, add this line underneath the <model name=”my_robot”> tag.

<static>true</static>

This is what you’re sdf file should look like at this stage:

1-add-static-tagJPG

Now underneath the <static>true</static> line, add these lines:

          <link name='chassis'>
            <pose>0 0 .1 0 0 0</pose>

            <collision name='collision'>
              <geometry>
                <box>
                  <size>.4 .2 .1</size>
                </box>
              </geometry>
            </collision>

            <visual name='visual'>
              <geometry>
                <box>
                  <size>.4 .2 .1</size>
                </box>
              </geometry>
            </visual>
          </link>

Here is how your sdf file should look now. 

2-here-is-how-should-look-nowJPG

Click Save and close the file.

What you have done is create a box. The name of this structure (i.e. link) is ‘chassis’. A chassis for a mobile robot is the frame (i.e. skeleton) of the vehicle.

The pose (i.e. position and orientation) of the geometric center of this box will be a position of (x = 0 meters, y = 0 meters, z = 0.1 meters) and an orientation of (roll = 0 radians, pitch = 0 radians, yaw = 0 radians). The pose of the chassis (as well as the post of any link in an sdf file) is defined relative to the model coordinate frame (which is ‘my_robot’).

Remember:

  • Roll (rotation about the x-axis)
  • Pitch (rotation about the y-axis)
  • Yaw (rotation about the z-axis)  

Inside the collision element of the sdf file, you specify the shape (i.e. geometry) that Gazebo’s collision detection engine will use. In this case, we want the collision detection engine to represent our vehicle as a box that is 0.4 meters in length, 0.2 meters in width, and 0.1 meters in height. 

Inside the visual tag, we place the shape that Gazebo’s rendering engine will use to display the robot. 

In most cases, the visual tag will be the same as the collision tag, but in some cases it is not.

For example, if your robot car has some complex geometry that looks like the toy Ferrari car below, you might model the collision physics of that body as a box but would use a custom mesh to make the robot model look more realistic, like the figure below.

ferrari_red_auto_sports_0

Now let’s run Gazebo so that we can see our model. Type the following command:

gazebo
3-launch-gazeboJPG

On the left-hand side, click the “Insert” tab.

On the left panel, click “My Robot”. You should see a white box. You can place it wherever you want.

4-white-boxJPG

Go back to the terminal window, and type CTRL + C to close Gazebo.

Let’s add a caster wheel to the robot. The caster wheel will be modeled as a sphere with a radius of 0.05 meters and no friction.

Relative to the geometric center of the white box (i.e. the robot chassis), the center of the spherical caster wheel will be located at x = -0.15 meters, y = 0 meters, and z = -0.05 meters.

Note that when we create a box shape, the positive x-axis points towards the front of the vehicle (i.e. the direction of travel). The x-axis is that red line below.

The positive y-axis points out the left side of the chassis. It is the green line below. The positive z-axis points straight upwards towards the sky.

gedit ~/.gazebo/models/my_robot/model.sdf

Add these lines after the first </visual> tag but before the </link> tag.

          <collision name='caster_collision'>
            <pose>-0.15 0 -0.05 0 0 0</pose>
            <geometry>
                <sphere>
                <radius>.05</radius>
              </sphere>
            </geometry>

            <surface>
              <friction>
                <ode>
                  <mu>0</mu>
                  <mu2>0</mu2>
                  <slip1>1.0</slip1>
                  <slip2>1.0</slip2>
                </ode>
              </friction>
            </surface>
          </collision>

          <visual name='caster_visual'>
            <pose>-0.15 0 -0.05 0 0 0</pose>
            <geometry>
              <sphere>
                <radius>.05</radius>
              </sphere>
            </geometry>
          </visual>

Click Save and close the file.

Now relaunch gazebo, and insert the model.

gazebo

Insert -> My Robot

Go back to the terminal window, and close gazebo by typing CTRL + C.

You can now see our robot has a brand new caster wheel located towards the rear of the vehicle.

5-robot-with-the-wheelJPG

Now let’s add the left wheel. We will model this as a cylinder with a radius of 0.1 meters and a length of 0.05 meters.

gedit ~/.gazebo/models/my_robot/model.sdf

Right after the </link> tag (but before the </model> tag, add the following lines. You can see that the wheel is placed at the front of the vehicle (x=0.1m), the left side of the vehicle (y=0.13m), and 0.1m above the surface (z=0.1m). It is a cylinder that is rotated 90 degrees (i.e. 1.5707 radians) around the model’s y axis and 90 degrees around the model’s z axis.

     <link name="left_wheel">
        <pose>0.1 0.13 0.1 0 1.5707 1.5707</pose>
        <collision name="collision">
          <geometry>
            <cylinder>
              <radius>.1</radius>
              <length>.05</length>
            </cylinder>
          </geometry>
        </collision>
        <visual name="visual">
          <geometry>
            <cylinder>
              <radius>.1</radius>
              <length>.05</length>
            </cylinder>
          </geometry>
        </visual>
      </link>

Launch Gazebo, and see how it looks. You can click the box at the top of the panel to see the robot from different perspectives.

7-different-perspectiveJPG
6-see-how-it-looksJPG
8-top-viewJPG
9-back-viewJPG

Now, let’s add a right wheel. Insert these lines into your sdf file right before the </model> tag. 

    <link name="right_wheel">
        <pose>0.1 -0.13 0.1 0 1.5707 1.5707</pose>
        <collision name="collision">
          <geometry>
            <cylinder>
              <radius>.1</radius>
              <length>.05</length>
            </cylinder>
          </geometry>
        </collision>
        <visual name="visual">
          <geometry>
            <cylinder>
              <radius>.1</radius>
              <length>.05</length>
            </cylinder>
          </geometry>
        </visual>
      </link>

Save it, close it, and open Gazebo to see your robot. Your robot has a body, a spherical caster wheel, and two big wheels on either side. 

10-robot-with-right-wheelJPG

Now let’s change the robot from static to dynamic. We need to add two revolute joints, one for the left wheel and one for the right wheel. Revolute joints (also known as “hinge joints”) are your wheels motors. These motors generate rotational motion to make the wheels move.

In the sdf file, first change this:

<static>true</static>

To this

<static>false</static>

Then add these lines right before the closing </model> tag.

      <joint type="revolute" name="left_wheel_hinge">
        <pose>0 0 -0.03 0 0 0</pose>
        <child>left_wheel</child>
        <parent>chassis</parent>
        <axis>
          <xyz>0 1 0</xyz>
        </axis>
      </joint>

      <joint type="revolute" name="right_wheel_hinge">
        <pose>0 0 0.03 0 0 0</pose>
        <child>right_wheel</child>
        <parent>chassis</parent>
        <axis>
          <xyz>0 1 0</xyz>
        </axis>
      </joint>

<child> defines the name of this joint’s child link.

<parent> defines the parent link.

<pose> describes the offset from the the origin of the child link in the frame of the child link.

<axis> defines the joint’s axis specified in the parent model frame. This axis is the axis of rotation. In this case, the two joints rotate about the y axis.

<xyz> defines the x, y, and z components of the normalized axis vector. 

You can learn more about sdf tags at the official website.

Save the sdf file, open Gazebo, and insert your model.

Click on your model to select it.

You will see six tiny dots on the right side of the screen. Click on those and drag your mouse to the left. It is a bit tricky to click on these as Gazebo can be quite sensitive. Just keep trying to drag those dots to the left using your mouse.

Under the “World -> Models” tab on the left, select the my_robot model. You should see two joints appear on the Joints tab on the right.

12-two-joints-appearJPG
11-two-joints-appearJPG

Under the Force tab, increase the force applied to each joint to 0.1 N-m. You should see your robot move around in the environment.

Congratulations! You have built a mobile robot in Gazebo.

What Are Parallel Manipulators?

Up until now, the robotic arms that we have been building are serial manipulators. Robotic arms like this one, for example, are called serial manipulators because each joint (i.e. servo motor) is attached to either the joint before it (via a link) or to the base.

However, with a parallel manipulator, the joints are not connected together. Instead, each joint is connected to both the base of the robot and to the end effector (e.g. vacuum suction cup).

The two main types of parallel manipulators are the Gough-Stewart Platform (pictured below), which is sometimes used in flight simulators and the delta robot (often used in factories for pick and place tasks along conveyor belts).

In the Gough-Stewart Platform type robots, all of the joints are prismatic (i.e. linear actuators…motors that generate motion only in a linear fashion). 

Hexapod0a
Image Source: Wikipedia

In delta robots, all of the joints are revolute (i.e. motors that generate rotational motion). Here is a video of a delta robot in action. It is performing a pick and place task.

The Ultimate Guide to Inverse Kinematics for 6DOF Robot Arms

Inverse kinematics is about calculating the angles of joints (i.e. angles of the servo motors on a robotic arm) that will cause the end effector of a robotic arm (e.g. robotics gripper, hand, vacuum suction cup, etc.) to reach some desired position (x, y, z) in 3D space. 

In this tutorial, we will learn about how to perform inverse kinematics for a six degree of freedom robotic arm. We will build from the work we did on this post where we used the graphical approach to inverse kinematics for a two degree of freedom SCARA-like robotic arm. This approach is also known as the analytical approach. It involves a lot of trigonometry and is fine for a robotic arm with three joints or less.

However, when we have a robotic arm with more than three degrees of freedom, we have to modify how we solve the inverse kinematics. 

In this tutorial, we’ll take a look at two approaches: an analytical approach and a numerical approach. We’ll then code these up in Python so that you can see how the calculations are done in actual code.

Real-World Applications

Prerequisites

Analytical Approach vs Numerical Approach to Inverse Kinematics

The analytical approach to inverse kinematics involves a lot of matrix algebra and trigonometry.

The advantage of this approach is that once you’ve drawn the kinematic diagram and derived the equations, computation is fast (compared to the numerical approach, which is iterative). You don’t have to make initial guesses for the joint angles like you do in the numerical approach (we’ll look at this approach later in this tutorial).

The disadvantage of the analytical approach is that the kinematic diagram and trigonometric equations are tedious to derive. Also, the solutions from one robotic arm don’t generalize to other robotic arms. You have to derive new equations for each new robotic arm you work with that has a different kinematic structure.  

Analytical Approach to Inverse Kinematics

In this section, we’ll explore one analytical approach to inverse kinematics (i.e. there are many approaches). 

Assumptions

In this approach, we first need to start off by making some assumptions. 

We have to assume that:

  • The first three joints of the robotic arm are the only ones that determine the position of the end effector. 
  • The last three joints (and any other joints after that) determine the orientation of the end effector.

Overall Steps

Here are the steps for calculating inverse kinematics for a six degree of freedom robotic arm.

Step 1: Draw the kinematic diagram of just the first three joints, and perform inverse kinematics using the graphical approach.

Step 2: Compute the forward kinematics on the first three joints to get the rotation of joint 3 relative to the global (i.e. base) coordinate frame. The outcome of this step will yield a matrix rot_mat_0_3, which means the rotation of frame 3 relative to frame 0 (i.e. the global coordinate frame).

Step 3: Calculate the inverse of rot_mat_0_3.

Step 4: Compute the forward kinematics on the last three joints, and extract the part that governs the rotation. This rotation matrix will be denoted as rot_mat_3_6.

Step 5: Determine the rotation matrix from frame 0 to 6 (i.e. rot_mat_0_6).

Step 6: Taking our desired x, y, and z coordinates as input, use the inverse kinematics equations from Step 1 to calculate the angles for the first three joints. 

Step 7: Given the joint angles from Step 6, use the rotation matrix to calculate the values for the last three joints of the robotic arm.

Let’s run through an example.

Draw the Kinematic Diagram of Just the First Three Joints and Do Inverse Kinematics

First, let’s draw the kinematic diagram for our entire robot. If you need a refresher on how to draw kinematic diagrams, check out this tutorial.

Our robotic arm will have a cylindrical-style base (i.e. range of motion resembles a cylinder) and a spherical wrist (i.e. range of motion resembles a sphere). Here is its kinematic diagram:

5-spherical-robotJPG

Here is the kinematic diagram of just the first three joints:

6-half-spherical-robotJPG

Let’s do inverse kinematics for the diagram above using the graphical approach to inverse kinematics. We first need to draw the aerial view of the diagram above.

7-spherical-robotJPG

From the aerial view, we can see that we have two equations that come out of that.

  • θ2 = tan-1(y/x)
  • d3 = sqrt(x2 + y2) – a3 – a4

Now, let’s draw the side view of the robotic arm.

8-spherical-robotJPG

We can see from the above image that:

  • d1 = z – a1 – a2d

Calculate rot_mat_0_3

For this step, you can either use this method or the Denavit-Hartenberg method to calculate the rotation matrix of frame 3 relative to frame 0. Either of those methods will yield the following rotation matrix:

rot-mat-0-3

Calculate the Inverse of rot_mat_0_3

Now that we know rot_mat_0_3 (which we defined in the previous section), we need to take its inverse. The reason for this is due to the following expression:

rot-mat-0-6

We can left-multiply the inverse of the rotation matrix we found in the previous section to solve for rot_mat_3_6.

rot-mat-equations

Calculate rot_mat_3_6

To calculate the rotation of frame 6 relative to frame 3, we need to go back to the kinematic diagram we drew earlier.

11-spherical-robotJPG

Using either the rotation matrix method or Denavit-Hartenberg, here is the rotation matrix you get when you consider just the frames from 3 to 6. 

10-rot-mat-3-6JPG

Determine rot_mat_0_6

Now, let’s determine the rotation matrix of frame 6 relative to frame 0, the global coordinate frame.

11-spherical-robotJPG-1

In this part, we need to determine what we want the orientation of the end effector to be so that we can define the rotation matrix of frame 6 relative to frame 0. We can choose any rotation matrix as long as it is a valid rotation matrix (I’ll explain what “valid” means below).

Imagine you want the end effector to point upwards towards the sky. In this case, z6 will point in the same direction as z0. Accordingly, if you can imagine z6 pointing straight upwards, x6 would be oriented in the opposite direction as x0, and y6 would be oriented in the opposite direction as y0. Our rotation matrix is thus:

12-rot-mat-0-6JPG

The rotation matrix above is valid. The reason it is valid is because the length (also known as “norm” or “magnitude”) of each row and each column is 1. The length of a column or row is the square root of the sum of the squared elements.

For example, looking at column 1, we have:

13-square-rootJPG

Write Python Code

Open up your favorite Python IDE or wherever you like to write Python code. 

Create up a new Python script. Call it inverse_kinematics_6dof_v1.py

We want to set a desired position and orientation (relative to the base frame) for the end effector of the robotic arm and then have the program calculate the servo angles necessary to move the end effector to that position and orientation.

Write the following code. These expressions and matrices below (that we derived earlier) will be useful to you as you go through the code. Don’t be intimidated at how long the code is. Just copy and paste it into your favorite Python IDE or text editor, and read through it one line at a time.

14-equations-for-codeJPG
#######################################################################################
# Progam: Inverse Kinematics for a 6DOF Robotic Arm Using an Analytical Approach
# Description: Given a desired end position (x, y, z) of the end effector of a robot, 
#   and a desired orientation of the end effector (relative to the base frame),
#   calculate the joint angles (i.e. angles for the servo motors).
# Author: Addison Sears-Collins
# Website: https://automaticaddison.com
# Date: October 19, 2020
# Reference: Sodemann, Dr. Angela 2020, RoboGrok, accessed 14 October 2020, <http://robogrok.com/>
#######################################################################################

import numpy as np # Scientific computing library 

# Define the desired position of the end effector
# This is the target (goal) location.
x = 4.0
y = 2.0

# Calculate the angle of the second joint
theta_2 = np.arctan2(y,x)
print(f'Theta 2 = {theta_2} radians\n')

# Define the desired orientation of the end effector relative to the base frame 
# (i.e. global frame)
# This is the target orientation.
# The 3x3 rotation matrix of frame 6 relative to frame 0
rot_mat_0_6 = np.array([[-1.0, 0.0, 0.0],
                        [0.0, -1.0, 0.0],
                        [0.0, 0.0, 1.0]])

# The 3x3 rotation matrix of frame 3 relative to frame 0
rot_mat_0_3 = np.array([[-np.sin(theta_2), 0.0, np.cos(theta_2)],
                        [np.cos(theta_2), 0.0, np.sin(theta_2)],
                        [0.0, 1.0, 0.0]])

# Calculate the inverse rotation matrix
inv_rot_mat_0_3 = np.linalg.inv(rot_mat_0_3)

# Calculate the 3x3 rotation matrix of frame 6 relative to frame 3
rot_mat_3_6 = inv_rot_mat_0_3 @ rot_mat_0_6
print(f'rot_mat_3_6 = {rot_mat_3_6}')

# We know the equation for rot_mat_3_6 from our pencil and paper
# analysis. The simplest term in that matrix is in the third column,
# third row. The value there in variable terms is cos(theta_5).
# From the printing above, we know the value there. Therefore, 
# cos(theta_5) = value in the third row, third column of rot_mat_3_6, which means...
# theta_5 = arccosine(value in the third row, third column of rot_mat_3_6)
theta_5 = np.arccos(rot_mat_3_6[2, 2])
print(f'\nTheta 5 = {theta_5} radians')

# Calculate the value for theta_6
# We'll use the expression in the third row, first column of rot_mat_3_6.
# -sin(theta_5)cos(theta_6) = rot_mat_3_6[2,0]
# Solving for theta_6...
# rot_mat_3_6[2,0]/-sin(theta_5) = cos(theta_6)
# arccosine(rot_mat_3_6[2,0]/-sin(theta_5)) = theta_6
theta_6 = np.arccos(rot_mat_3_6[2, 0] / -np.sin(theta_5))
print(f'\nTheta 6 = {theta_6} radians')

# Calculate the value for theta_4 using one of the other
# cells in rot_mat_3_6. We'll use the second row, third column.
# cos(theta_4)sin(theta_5) = rot_mat_3_6[1,2]
# cos(theta_4) = rot_mat_3_6[1,2] / sin(theta_5)
# theta_4 = arccosine(rot_mat_3_6[1,2] / sin(theta_5))
theta_4 = np.arccos(rot_mat_3_6[1,2] / np.sin(theta_5))
print(f'\nTheta 4 = {theta_4} radians')

# Check that the angles we calculated result in a valid rotation matrix
r11 = -np.sin(theta_4) * np.cos(theta_5) * np.cos(theta_6) - np.cos(theta_4) * np.sin(theta_6)
r12 = np.sin(theta_4) * np.cos(theta_5) * np.sin(theta_6) - np.cos(theta_4) * np.cos(theta_6)
r13 = -np.sin(theta_4) * np.sin(theta_5)
r21 = np.cos(theta_4) * np.cos(theta_5) * np.cos(theta_6) - np.sin(theta_4) * np.sin(theta_6)
r22 = -np.cos(theta_4) * np.cos(theta_5) * np.sin(theta_6) - np.sin(theta_4) * np.cos(theta_6)
r23 = np.cos(theta_4) * np.sin(theta_5)
r31 = -np.sin(theta_5) * np.cos(theta_6)
r32 = np.sin(theta_5) * np.sin(theta_6)
r33 = np.cos(theta_5)

check_rot_mat_3_6 = np.array([[r11, r12, r13],
                              [r21, r22, r23],
                              [r31, r32, r33]])

# Original matrix
print(f'\nrot_mat_3_6 = {rot_mat_3_6}')

# Check Matrix
print(f'\ncheck_rot_mat_3_6 = {check_rot_mat_3_6}\n')

# Return if Original Matrix == Check Matrix
rot_minus_check_3_6 = rot_mat_3_6.round() - check_rot_mat_3_6.round()
zero_matrix = np.array([[0, 0, 0],
                        [0, 0, 0],
                        [0, 0, 0]])
matrices_are_equal = np.array_equal(rot_minus_check_3_6, zero_matrix)

# Determine if the solution is valid or not
# If the solution is valid, that means the end effector of the robotic 
# arm can reach that target location.
if (matrices_are_equal):
  valid_matrix = "Yes"
else:
  valid_matrix = "No"  
print(f'Is this solution valid?\n{valid_matrix}\n')

Here is the output:

output

References

Sodemann, Dr. Angela 2020, RoboGrok, accessed 14 October 2020, <http://robogrok.com/>

Inverse Kinematics Using the Pseudoinverse Jacobian Method (Numerical Approach)

Let’s take a look at a numerical approach to inverse kinematics. There are a number of approaches, but in this section will explore one that is quite popular in industry and academia: the Pseudoinverse Jacobian Method.

This method is called “numerical” because it involves iteration to calculate the joint angles from the desired end effector position. 

Full Code in Python

Here is the full code. I named the program inv_kinematics_using_pseudo_jacobian.py. I’ll explain each piece step-by-step below.

#######################################################################################
# Progam: Inverse Kinematics for a Robotic Arm Using the Pseudoinverse of the Jacobian
# Description: Given a desired end position (x, y, z) of the end effector of a robot, 
#   calculate the joint angles (i.e. angles for the servo motors).
# Author: Addison Sears-Collins
# Website: https://automaticaddison.com
# Date: October 15, 2020
#######################################################################################

import numpy as np # Scientific computing library 

def axis_angle_rot_matrix(k,q):
    """
    Creates a 3x3 rotation matrix in 3D space from an axis and an angle.

    Input
    :param k: A 3 element array containing the unit axis to rotate around (kx,ky,kz) 
    :param q: The angle (in radians) to rotate by

    Output
    :return: A 3x3 element matix containing the rotation matrix
    
    """
    
    #15 pts 
    c_theta = np.cos(q)
    s_theta = np.sin(q)
    v_theta = 1 - np.cos(q)
    kx = k[0]
    ky = k[1]
    kz = k[2]	
	
    # First row of the rotation matrix
    r00 = kx * kx * v_theta + c_theta
    r01 = kx * ky * v_theta - kz * s_theta
    r02 = kx * kz * v_theta + ky * s_theta
    
    # Second row of the rotation matrix
    r10 = kx * ky * v_theta + kz * s_theta
    r11 = ky * ky * v_theta + c_theta
    r12 = ky * kz * v_theta - kx * s_theta
	
    # Third row of the rotation matrix
    r20 = kx * kz * v_theta - ky * s_theta
    r21 = ky * kz * v_theta + kx * s_theta
    r22 = kz * kz * v_theta + c_theta
	
    # 3x3 rotation matrix
    rot_matrix = np.array([[r00, r01, r02],
                           [r10, r11, r12],
                           [r20, r21, r22]])
						   
    return rot_matrix

def hr_matrix(k,t,q):
    '''
    Create the Homogenous Representation matrix that transforms a point from Frame B to Frame A.
    Using the axis-angle representation
    Input
    :param k: A 3 element array containing the unit axis to rotate around (kx,ky,kz) 
    :param t: The translation from the current frame (e.g. Frame A) to the next frame (e.g. Frame B)
    :param q: The rotation angle (i.e. joint angle)

    Output
    :return: A 4x4 Homogenous representation matrix
    '''
    # Calculate the rotation matrix (angle-axis representation)
    rot_matrix_A_B = axis_angle_rot_matrix(k,q)
	
    # Store the translation vector t
    translation_vec_A_B = t

    # Convert to a 2D matrix
    t0 = translation_vec_A_B[0]
    t1 = translation_vec_A_B[1]
    t2 = translation_vec_A_B[2]
    translation_vec_A_B = np.array([[t0],
                                    [t1],
                                    [t2]])
									
    # Create the homogeneous transformation matrix
    homgen_mat = np.concatenate((rot_matrix_A_B, translation_vec_A_B), axis=1) # side by side

    # Row vector for bottom of homogeneous transformation matrix
    extra_row_homgen = np.array([[0, 0, 0, 1]])

    # Add extra row to homogeneous transformation matrix	
    homgen_mat = np.concatenate((homgen_mat, extra_row_homgen), axis=0) # one above the other
 	    
    return homgen_mat

class RoboticArm:
    def __init__(self,k_arm,t_arm):
        '''
        Creates a robotic arm class for computing position and velocity.

        Input
        :param k_arm: A 2D array that lists the different axes of rotation (rows) for each joint.
        :param t_arm: A 2D array that lists the translations from the previous joint to the current joint
	                  The first translation is from the global (base) frame to joint 1 (which is often equal to the global frame)
	                  The second translation is from joint 1 to joint 2, etc.
        '''
        self.k = np.array(k_arm)
        self.t = np.array(t_arm)
        assert k_arm.shape == t_arm.shape, 'Warning! Improper definition of rotation axes and translations'
        self.N_joints = k_arm.shape[0]

    def position(self,Q,index=-1,p_i=[0,0,0]):
        '''
        Compute the position in the global (base) frame of a point given in a joint frame
        (default values will assume the input position vector is in the frame of the last joint)
        Input
        :param p_i: A 3 element vector containing a position in the frame of the index joint
        :param index: The index of the joint frame being converted from (first joint is 0, the last joint is N_joints - 1)

        Output
        :return: A 3 element vector containing the new position with respect to the global (base) frame
        '''
        # The position of this joint described by the index
        p_i_x = p_i[0]
        p_i_y = p_i[1]
        p_i_z = p_i[2]
        this_joint_position = np.array([[p_i_x],
                                        [p_i_y],
                                        [p_i_z],
									    [1]])

        # End effector joint
        if (index == -1):
          index = self.N_joints - 1
		
        # Store the original index of this joint		
        orig_joint_index = index

        # Store the result of matrix multiplication
        running_multiplication = None
		
        # Start from the index of this joint and work backwards to index 0
        while (index >= 0):
		  
          # If we are at the original joint index
          if (index == orig_joint_index):
            running_multiplication = hr_matrix(self.k[index],self.t[index],Q[index]) @ this_joint_position
          # If we are not at the original joint index
          else:	
            running_multiplication = hr_matrix(self.k[index],self.t[index],Q[index]) @ running_multiplication
		  
          index = index - 1
		
        # extract the points
        px = running_multiplication[0][0]
        py = running_multiplication[1][0]
        pz = running_multiplication[2][0]		
        
        position_global_frame = np.array([px, py, pz])
		
        return position_global_frame

    def pseudo_inverse(self,theta_start,p_eff_N,goal_position,max_steps=np.inf):
        '''
        Performs the inverse kinematics using the pseudoinverse of the Jacobian

        :param theta_start: An N element array containing the current joint angles in radians (e.g. np.array([np.pi/8,np.pi/4,np.pi/6]))
	    :param p_eff_N: A 3 element vector containing translation from the last joint to the end effector in the last joints frame of reference
        :param goal_position: A 3 element vector containing the desired end position for the end effector in the global (base) frame
        :param max_steps: (Optional) Maximum number of iterations to compute 

        Output
        :return: An N element vector containing the joint angles that result in the end effector reaching xend (i.e. the goal)
        '''
        v_step_size = 0.05
        theta_max_step = 0.2
        Q_j = theta_start # Array containing the starting joint angles
        p_end = np.array([goal_position[0], goal_position[1], goal_position[2]]) # desired x, y, z coordinate of the end effector in the base frame
        p_j = self.position(Q_j,p_i=p_eff_N)  # x, y, z coordinate of the position of the end effector in the global reference frame
        delta_p = p_end - p_j  # delta_x, delta_y, delta_z between start position and desired final position of end effector
        j = 0 # Initialize the counter variable
        
        # While the magnitude of the delta_p vector is greater than 0.01 
        # and we are less than the max number of steps
        while np.linalg.norm(delta_p) > 0.01 and j<max_steps:
            print(f'j{j}: Q[{Q_j}] , P[{p_j}]') # Print the current joint angles and position of the end effector in the global frame
            
            # Reduce the delta_p 3-element delta_p vector by some scaling factor 
            # delta_p represents the distance between where the end effector is now and our goal position.			
            v_p = delta_p * v_step_size / np.linalg.norm(delta_p) 

            # Get the jacobian matrix given the current joint angles
            J_j = self.jacobian(Q_j,p_eff_N)
             
            # Calculate the pseudo-inverse of the Jacobian matrix
            J_invj = np.linalg.pinv(J_j)
			
            # Multiply the two matrices together
            v_Q = np.matmul(J_invj,v_p)

            # Move the joints to new angles
            # We use the np.clip method here so that the joint doesn't move too much. We
            # just want the joints to move a tiny amount at each time step because 
            # the full motion of the end effector is nonlinear, and we're approximating the
            # big nonlinear motion of the end effector as a bunch of tiny linear motions.
            Q_j = Q_j + np.clip(v_Q,-1*theta_max_step,theta_max_step)#[:self.N_joints]

            # Get the current position of the end-effector in the global frame
            p_j = self.position(Q_j,p_i=p_eff_N)

            # Increment the time step			
            j = j + 1

            # Determine the difference between the new position and the desired end position
            delta_p = p_end - p_j

        # Return the final angles for each joint
        return Q_j


    def jacobian(self,Q,p_eff_N=[0,0,0]):
        '''
        Computes the Jacobian (just the position, not the orientation)

        :param Q: An N element array containing the current joint angles in radians
        :param p_eff_N: A 3 element vector containing translation from the last joint to the end effector in the last joints frame of reference

        Output
        :return: A 3xN 2D matrix containing the Jacobian matrix
        '''
        # Position of the end effector in global frame
        p_eff = self.position(Q,-1,p_eff_N)
        
        first_iter = True
		
        jacobian_matrix = None
		
        for i in range(0, self.N_joints):
          if (first_iter == True):

            # Difference in the position of the end effector in the global frame
            # and this joint in the global frame
            p_eff_minus_this_p = p_eff - self.position(Q,index=i)
			
            # Axes
            kx = self.k[i][0]
            ky = self.k[i][1]
            kz = self.k[i][2]
            k = np.array([kx, ky, kz])
			
            px = p_eff_minus_this_p[0]
            py = p_eff_minus_this_p[1]
            pz = p_eff_minus_this_p[2]
            p_eff_minus_this_p = np.array([px, py, pz])
			
            this_jacobian = np.cross(k, p_eff_minus_this_p)	
 
            # Convert to a 2D matrix
            j0 = this_jacobian[0]
            j1 = this_jacobian[1]
            j2 = this_jacobian[2]
            this_jacobian = np.array([[j0],
                                      [j1],
                                      [j2]])			
            jacobian_matrix = this_jacobian
            first_iter = False
          else:
            p_eff_minus_this_p = p_eff - self.position(Q,index=i)
			
            # Axes
            kx = self.k[i][0]
            ky = self.k[i][1]
            kz = self.k[i][2]
            k = np.array([kx, ky, kz])
			
            # Difference between this joint's position and end effector's position
            px = p_eff_minus_this_p[0]
            py = p_eff_minus_this_p[1]
            pz = p_eff_minus_this_p[2]
            p_eff_minus_this_p = np.array([px, py, pz])
			
            this_jacobian = np.cross(k, p_eff_minus_this_p)	

            # Convert to a 2D matrix
            j0 = this_jacobian[0]
            j1 = this_jacobian[1]
            j2 = this_jacobian[2]
            this_jacobian = np.array([[j0],
                                      [j1],
                                      [j2]])			
            jacobian_matrix = np.concatenate((jacobian_matrix, this_jacobian), axis=1) # side by side  		    

        return jacobian_matrix

def main():
  '''Given a two degree of freedom robotic arm and a desired end position of the end effector,
     calculate the two joint angles (i.e. servo angles).
  '''

  # A 2D array that lists the different axes of rotation (rows) for each joint
  # Here I assume our robotic arm has two joints, but you can add more if you like.
  # k = kx, ky, kz 
  k = np.array([[0,0,1],[0,0,1]])
	
  # A 2D array that lists the translations from the previous joint to the current joint
  # The first translation is from the base frame to joint 1 (which is equal to the base frame)
  # The second translation is from joint 1 to joint 2
  # t = tx, ty, tz
  # These values are measured with a ruler based on the kinematic diagram
  # This tutorial teaches you how to draw kinematic diagrams:
  # https://automaticaddison.com/how-to-assign-denavit-hartenberg-frames-to-robotic-arms/
  a1 = 4.7
  a2 = 5.9
  a3 = 5.4
  a4 = 6.0
  t = np.array([[0,0,0],[a2,0,a1]])

  # Position of end effector in joint 2 (i.e. the last joint) frame
  p_eff_2 = [a4,0,a3]
	
  # Create an object of the RoboticArm class
  k_c = RoboticArm(k,t)

  # Starting joint angles in radians (joint 1, joint 2)
  q_0 = np.array([0,0])
	
  # desired end position for the end effector with respect to the base frame of the robotic arm
  endeffector_goal_position = np.array([4.0,10.0,a1 + a4])

  # Display the starting position of each joint in the global frame
  for i in np.arange(0,k_c.N_joints):
    print(f'joint {i} position = {k_c.position(q_0,index=i)}')

  print(f'end_effector = {k_c.position(q_0,index=-1,p_i=p_eff_2)}')
  print(f'goal = {endeffector_goal_position}') 

  # Return joint angles that result in the end effector reaching endeffector_goal_position
  final_q = k_c.pseudo_inverse(q_0, p_eff_N=p_eff_2, goal_position=endeffector_goal_position, max_steps=500)
	
  # Final Joint Angles in degrees	
  print('\n\nFinal Joint Angles in Degrees')
  print(f'Joint 1: {np.degrees(final_q[0])} , Joint 2: {np.degrees(final_q[1])}')

if __name__ == '__main__':
  main()

Here is the output:

4-pretty-much-equalJPG

axis_angle_rot_matrix(k,q)

There are multiple ways to describe rotation in a robotic system. In previous tutorial, I have covered some of those methods:

Another way to describe rotation in a robotic system is to use the Axis-Angle representation. With this representation, any orientation of a robotic system in three dimensions is equivalent to a rotation about a fixed axis k through angle θ.

1-axis-angleJPG
2-axis-and-angleJPG

For example, suppose you have a base frame of a robotic arm. This frame is Frame 0 (Joint 1).

2-first-joint

The next frame (i.e. the next joint or servo motor) would be Frame 1 (Joint 2). Frame 1 of the robotic arm would be rotated around axis k by angle θ1

3-add-angle
4-end-effector-is-here

In most of my robotics work, I’ve assumed that rotation at any joint is around the z axis. So, in this code, I’ve assumed that axis k is the following vector.

k = (kx, ky, kz) = (0, 0, 1)

With the rotation around that axis being θ (e.g. θ1, θ2, θ3, etc.).

Given axis-angle representation, the equivalent 3×3 rotation matrix Rk(θ) is as follows:

15-rot-matrixJPG

This equation above is what you see implemented in the code.

hr_matrix(k,t,q)

This code creates the homogeneous representation (also called “transformation”) matrix. A homogeneous rotation matrix combines the rotation and translation (i.e. displacement) of one coordinate frame relative to another coordinate frame into a single 4 row x 4 column matrix.

1-homogeneous-n-1-nJPG

class RoboticArm

This class represents the robotic arm that we want to control.

__init__(self,k_arm,t_arm)

This piece of code is the constructor for the robotic arm class. This constructor initializes the data fields of the robotic arm of interest. These data fields include the number of joints, the rotation axes for each joint, and the measured lengths (i.e. translations) between each joint.

position(self,Q,index=-1,p_i=[0,0,0])

This piece of code uses hr_matrix(k,t,q) to calculate the position in the global (base) frame of a point given in a joint frame. 

pseudo_inverse(self,theta_start,p_eff_N,goal_position,max_steps=np.inf)

This piece of code performs inverse kinematics using the pseudoinverse of the Jacobian. What does that mean? Let me explain now how this process works.

You start off with the joints of the robotic arm at arbitrary angles (by convention I typically set them to 0 degrees). 

The end effector of the robotic arm is located at some arbitrary position in 3D space. 

You then iterate. At each step of the iteration, you

1. Calculate the distance between the current end effector position and the desired goal end effector position. This value (which is a vector…a list of numbers) is often called “delta p”.

2. Reduce the delta p vector by some scaling factor so that it is really small. We only want the end effector to move by a small amount at each time step because, for small motions, we can approximate the displacement of the end effector as linear. Doing this makes the math easier, enabling us to use the Jacobian matrix. The Jacobian matrix is, at its core, is a matrix of partial derivatives

Remember, forward kinematics (i.e. the motion of revolute joints like servo motors) is nonlinear and would typically involve sines and cosines like we saw in the Analytical IK method), but in this case, we make linear approximations to that nonlinear motion.

16-nonlinear-motionJPG

3. Calculate the Jacobian matrix J. 

4, Calculate the pseudoinverse of the Jacobian matrix. The pseudoinverse of the Jacobian matrix is calculated because the regular inverse (i.e. J-1 which we looked at in a previous tutorial) fails if a matrix is not square (i.e. a square matrix is a matrix with the same number of columns and rows). The pseudoinverse can invert a non-square matrix.

5. Multiply the pseudoinverse of the Jacobian matrix and the scaled delta p vector together to calculate the desired tiny change in the joint values. 

6. Update each joint angle by the amount of that tiny change.

7. The resulting motion from updating the joint values should bring the end effector just a little bit closer to your desired goal position.

Keep repeating the steps above again and again until the end effector has reached the desired goal position (or rather “close enough”). 

In a nutshell, at each iteration, we have the end effector take tiny steps towards the goal. We only stop when the end effector reaches the goal position.

I like to set the maximum number of iterations around 500, but you’ll often get the end effector to reach the desired goal position long before that many iterations.

Once the end effector is close enough, return the final joint angles. For a real robotic arm, these are the angles that each servo motor would need to be for the end effector of the arm to be at the goal position.

jacobian(self,Q,p_eff_N=[0,0,0])

In this piece of code, we calculate the Jacobian matrix (position part on the top half…not the orientation part on the bottom half).

17-jacobianJPG

d0n is the displacement from the origin of the global coordinate frame to the origin of the end of the end effector.

For a revolute joint (like a servo motor), the change in the linear velocity of the end effector is the cross product of the axis of revolution k (which is made up of 3 elements, kx, ky, and kz) and a 3-element position vector from the joint to the end effector.

For example, given a robotic arm with two joints (i.e. two servo motors), the Jacobian is calculated as follows:

Jv = [Jv1, Jv2]

Where:

18-jacobianJPG
  • Jv1 is the first column of the Jacobian matrix.
  • Jv2 is the second column of the Jacobian matrix.
  • k1 is the 3-element axis for the first joint (e.g. (0, 0, 1)).
  • k2 is the 3-element axis for the second joint (e.g. (0, 0, 1)).
  • peff is a 3-element vector that represents the position of the end effector in the base frame of the robotic arm.
  • pB1 is the position of joint 1 relative to the base frame.
  • pB2 is the position of joint 2 relative to the base frame.

main()

Given a two degree of freedom robotic arm and a desired end position of the end effector, we calculate the two joint angles (i.e. servo angles).

If you see this line of code, you’ll see that I want the robotic arm to go to x = 4.0, y = 10.0.

endeffector_goal_position = np.array([4.0,10.0,a1 + a4])

In fact, these coordinates are the same goal coordinates I set in this inverse kinematics project, where I did an analytical approach to inverse kinematics on a real robotic arm.

In that post, I set the goal destination to x = 4.0 and y = 10.0. If you uncomment the Arduino code on that tutorial and run it, you’ll see that the Serial Monitor outputs the final joint angles (that get the end effector of the robotic arm to the goal destination) as:

θ1 = 42.8 

θ2 = 50.3

13-add-y-axesJPG

This is pretty much equal to what we get with the Pseudoinverse Jacobian approach from our Python code earlier in this section. 

4-pretty-much-equalJPG

Conclusion

What I’ve shown in this tutorial are two popular methods for calculating the inverse kinematics of a robotic arm. There are other methods out there like Cyclic Coordinate Descent, the Damped Least Squares method, and the Jacobian Transpose method. What method you choose for your project depends on your personal preference and what you’re trying to achieve. 

That’s it! Keep building!