Optimal trajectory generation of various English alphabet using deep
learning model for 3 R manipulator
Abstract
The recent era of Industry 4.0 extensively utilizes manipulators
for various critical automation tasks. Handling a manipulator robot is a
complex task. This research employs a 3-R (revolute) manipulator robot
to achieve optimal joint trajectories for drawing different alphabets
and shapes. The solution to inverse kinematics (IK) is essential;
therefore, an artificial neural network (ANN) based model is used. The
forward and backward reaching inverse kinematics (FABRIK) technique
provides stability, though it is computationally intensive and
time-consuming. To design a low-cost hybrid model, ANN combined with
FABRIK is proposed to compute IK. Using the proposed model, coordinates
for different alphabets and shapes within the confined workspace were
calculated. We have developed a 3R (revolute) manipulator that utilizes
deep learning (DL) and IK. The ANN automatically obtains specific
end-effector coordinates. Due to the non-linear characteristics of IK’s
mathematical model, inverse kinematics is a time-consuming procedure,
making it difficult to provide a mathematical solution. Our primary goal
is to reduce processing costs by using ANN and FABRIK with the IK model
to solve IK problems and obtain coordinates for any form or alphabet
within a given workspace. To ensure a safe region is selected, this
model combines the control barrier function (CBF) with the Lyapunov
function. The accuracy of the model exceeds 99.5 percent. We have
calculated the mean square error (MSE) as 1.66, the root mean square
error (RMSE) as 1.25, and the mean absolute error (MAE) as 0.96 for our
model. The error between the model’s predicted and actual coordinates
also demonstrates letter coordinates and shapes drawn using a physical
3R manipulator model. As a result, this approach can be used to
accurately estimate the angles in complex 3DoF inverse kinematics
models.
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