Straight-forward use Inverse Kinematics library, taking axis, lengths and a goal position and solves the inverse kinematics using Jacobian Gradient Descent.
Stemmed from prefering Matrix Exponential for robotic transformations and a more elegant jacobian calculation.
The code here is very general and not optimized (change Eigen::VectorXd and Eigen::Dynamic to your robot to gain speed). This program will run for revolute joints of any kind and can be simply adapted to prismatic joints.
- Simplicity: Just specify axes, lengths, and a goal position.
- Flexibility: Compatible with revolute joints of any kind and easily adaptable for prismatic joints (simply adapt twist calculation)
- Matrix Exponential: Inspired by a preference for Matrix Exponential methods for robotic transformations and a refined approach to Jacobian calculation, offering a more elegant and general solution than Denavit-Hartenberg parameters.
- General and Adaptable: The provided code is intentionally general, allowing you to customize and optimize it for your specific robotic setup to achieve enhanced performance.
- Set Up Your Environment: Ensure that you have Eigen installed, as this library relies on Eigen for matrix and vector operations.
- Integrate with Your Project: Import the Minimal Inverse Kinematics library into your project.
- Configure Your Robot Parameters: Customize the library's settings, such as
Eigen::VectorXdandEigen::Dynamic, to match your robot's specifications for improved efficiency. - Define Your Goal: Change the axis, lengths, and goal position for your robotic arm.
- Solve: Execute the solver to obtain the inverse kinematics solution for your robot.
I put a pre-existing robot as a starter code.