Visualized Kinematics for a Motoman Robot

Project Purpose & Goal

The primary purpose of this project was to gain a deeper understanding of the motion mechanics and kinematics of the Motoman robot. The goal was to utilize the computational power and flexibility of Python and C to compute and visualize both inverse and forward kinematic models. Through this, my aim was to ascertain the efficiency of the robot's motion and trajectory, allowing for possible refinements and performance enhancements in future applications.

Explanation

Kinematics is the study of motion without considering its causes. In robotics, it plays a vital role in predicting and controlling robot behavior. By employing the Denavit-Hartenberg (DH) parameters, a standardized method to define the joint geometry of mechanical systems, tackling the complexities of the Motoman robot's motion.

Inverse kinematics focused on deciphering joint parameters when the position of the end-effector (the robot's moving part) was known. Conversely, forward kinematics enabled predicting the end-effector's position from given joint parameters. Integrating Python and C, ensured precise calculations and clear trajectory visualizations.

Problems

Thought Process

Computation Complexity: Handling the mathematical intricacies of the DH parameters required meticulous attention, ensuring that the computations did not lead to unrealistic robot motions.

Trajectory Visualization: Translating abstract kinematic data into understandable and insightful visualizations was initially challenging, especially in representing intricate motion paths.

Physical vs. Theoretical Discrepancies: There were instances where the calculated kinematic outcomes did not align perfectly with real-world robotic movements, demanding recalibration and refinement of our models.

I was approached to assist in determining the DH parameters of the robot. Seizing this opportunity allowed me to enhance my mechatronics knowledge and enrich my AI proficiency.

Here are my key takeaways:

1. I began by familiarizing myself with the foundational principles of robotics and kinematics, establishing a robust foundation for the project.

2. Understanding the computational requirements, I chose Python for its adaptability in mathematical modeling and C for its rapid performance in kinematics computations.

3. Emphasizing visualization and iterative analysis was pivotal. As I worked through the calculations, visualizing the robot's trajectory consistently highlighted anomalies, facilitating timely refinements to the models.