This project provides tools to compute the forward kinematics of serial robotic manipulators using Denavit-Hartenberg (DH) parameters. It supports both numerical and symbolic calculations, making it useful for both simulation and analytical studies.

• Compute forward kinematics numerically using NumPy
• Compute forward kinematics symbolically using SymPy
• Unified class interface for both calculation types
• Example scripts for quick testing

• forward_kinematics_dh.py: Numeric DH forward kinematics functions (NumPy)
• forward_kinematics_dh_symbolic.py: Symbolic DH forward kinematics functions (SymPy)
• forward_kinematics_dh_class.py: Unified class with both numeric and symbolic methods
• example.py: Example usage of numeric and symbolic functions (separate functions)
• example2.py: Example usage of the unified class for both numeric and symbolic calculations

1. Clone the repository

   git clone <your-repo-url> cd dh_forward_kinematics_python

2. Install dependencies This project requires numpy and sympy. Install them with:

   pip install numpy sympy

3. Run examples
   • For basic function usage:

     python example.py

   • For class-based usage:

     python example2.py

• Numeric calculation:
  • Provide a list of DH parameters (theta, d, a, alpha) for each joint as numbers.
  • The code computes the transformation matrix using NumPy.
• Symbolic calculation:
  • Provide DH parameters as SymPy symbols or expressions.
  • The code computes the transformation matrix symbolically, allowing for analytical manipulation.
• Unified class:
  • The ForwardKinematicsDH class provides both numeric() and symbolic() static methods for easy switching between calculation types.

from forward_kinematics_dh_class import ForwardKinematicsDH import numpy as np import sympy as sp # Numeric dh_params = [[np.pi/4, 0, 1, 0], [np.pi/4, 0, 1, 0]] H = ForwardKinematicsDH.numeric(dh_params) print(H) # Symbolic th1, th2 = sp.symbols('th1 th2') a1, a2 = sp.symbols('a1 a2') dh_params_sym = [[th1, 0, a1, 0], [th2, 0, a2, 0]] H_sym = ForwardKinematicsDH.symbolic(dh_params_sym) sp.pprint(H_sym) 

MIT