Description

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Under the mentorship of Prof. Vijay Bhaskar Semwal, this ambitious project addresses the inverse kinematics challenge for a 3-Link Planar Robot Manipulator. The goal was to adeptly convert the end-effector's position in the Cartesian space into a corresponding set of joint angles.

Functionality

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Once the model is trained, it takes in coordinates and orientation of points on the desired trajectory within the Cartesian space. Utilizing a neural network, it then determines the joint parameters to position the end effector accurately along the specified trajectory

Significance

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These methods become inadequate when the exact geometry is indeterminate. This project introduces a groundbreaking solution by harnessing neural networks to deduce the inverse kinematics transformation. It presents a resilient alternative when traditional techniques are unfeasible or fall short in precisionTraditional solutions for the inverse kinematics problem rely heavily on manipulator geometry.

Project Repository

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GitHub Repository Go To

 

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A work by Anuj Shah