Dynamic Modeling of Robots: Implementations

This lecture covers the various implementation approaches for dynamic modeling of robots, focusing on reducing computational cost and optimizing hardware knowledge. It discusses sensor information, simplification techniques, and the development of dynamic models using Lagrange and Newton-Euler approaches. The instructor explains the iterative implementation of formulas, look-up tables, and real-time calculations of speed and acceleration vectors. Emphasis is placed on the balance between memory size and calculations, as well as the compensation of errors introduced by simplifications. The lecture concludes with a comparison of Lagrange and Newton-Euler methods and their applications in simulation and control.