# Python Robotics Jacobian - read description

Robot Control

Inverse Kinematics - Velocity control

From the previous task you can acquire an estimation of the joint states and

the location of the correct target. With this information we can construct

a Jacobian on how the robot's state will change given changes to the joints.

Therefore, through combining these two pieces of information, we can use a

technique called Inverse Kinematics (IK) to tell the robot how to reach the

desired state.

Simply put, given the joint angles of the links, where is the end e ec-

tor's position, and how does the change in these angles a ect the end

e ector's position minimise the error between actual and desired locations.

This part will involve implementing IK of the four link robot. As

stated in section A, two of the links rotate around the y-axis and the other

two around the z -axis. In the labs you constructed Jacobians that used

the rotation matrix for the z -axis, and here you will also need to use the

rotation matrix corresponding to the y-axis.

You should focus rst on implementing this in the velocity control

mode. This can be done by setting the mode via:

self.env.controlMode="VEL"

The whole aim will to be to reach the right target and remain stable

until the target (the star target) moves to a new location.

Inverse Kinematics - Gravity compensated torque con-

trol

Once you have implemented IK, you will add gravity. This will mean that

you will need to calculate the torques required to overcome gravity. Each

link has a weight of 1kg.

Uncomment line 34 to include gravity:

This will set gravity to be in the z -axis

8

You should reimplement the IK as a task space proportional-derivative (PD)

controller with gravity compensation to reach the same target using the

torque mode:

self.env.controlMode="TORQUE"

To do this you will have to enable gravity by adding this before the

loop in the go method:

It is best to rst nd the torques needed to move the robot to the

correct position without gravity initially. Once the robot is performing IK

required.

You can then explore this system by adjusting the parameters of the

PD controller to observe if you can speed up the system without reducing

accuracy.

Diculty with vision

If you had diculty with the previous section, you may use the ground

being:

 [login to view URL] truth joint angles

 [login to view URL] truth joint velocities

 [login to view URL] valid target

You will need to report if you are using these values instead.

Expected outcome

By the end your robot should be moving to the correct target. Not only this

you should be able to report quantitatively:

 Accuracy of the arm movement

9

 Time it takes to reach the target

 Di erences with and without gravity

 The a ects of the PD parameters

For some of the above points it is perhaps best to use graphs to illustrate

potential trends within the data. You can see how many times your arm

reaches the valid target with:

And the number of times your robot goes for the invalid target with:

To get a measurement of time you should multiply the step size with the

number of timesteps. In your code it should look like this:

number of timesteps*[login to view URL]

The output will be in seconds.

Skills: Python, Robotics

( 142 reviews ) Sharjah, United Arab Emirates

Project ID: #18189120

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