MATLAB Code Implementation: Simulation Example of 6-DOF Robot

MATLAB Simulation Example: Analysis and Implementation of 6-DOF Robot

As a powerful scientific computing and simulation platform, MATLAB is widely used in robot modeling and control. 6-DOF robots are commonly employed in industrial and research scenarios due to their high flexibility and extensive motion range. This article demonstrates how to implement kinematics and dynamics simulation for 6-DOF robots using MATLAB.

Kinematics Modeling Kinematic analysis forms the foundation of robot simulation, primarily involving forward and inverse kinematics. Forward kinematics calculates the end-effector's position and orientation from joint angles, while inverse kinematics determines joint angles from a desired end-effector pose. In MATLAB implementation, the Denavit-Hartenberg (D-H) parameter method is typically used to establish the robot's kinematic model by defining geometric relationships between links. Transformation matrices are constructed through sequential multiplication to obtain the final end-effector pose using functions like `dhmatrix` or custom transformation functions.

Dynamics Analysis Dynamics studies forces and torques acting on robots during motion, primarily using Newton-Euler or Lagrangian methods. The Newton-Euler method recursively computes velocities, accelerations, and joint forces/torques through forward-backward iterations, suitable for real-time control simulation. The Lagrangian method derives dynamic equations based on energy conservation principles, ideal for offline analysis and optimization. MATLAB's Symbolic Math Toolbox can automate the derivation of dynamic equations using symbolic variables and differentiation functions like `diff()`, simplifying complex formula handling.

Trajectory Planning and Simulation After completing kinematics and dynamics modeling, the next step involves planning robot motion trajectories. Common methods include polynomial interpolation, spline curves, and optimization-based approaches. MATLAB provides extensive function libraries such as `trapz` for trapezoidal velocity profiles or `interp1` for interpolation between waypoints to generate smooth joint angle trajectories. Combined with Robotics System Toolbox functions like `waypointTrajectory`, users can simulate robot motion processes and validate algorithm effectiveness through built-in visualization tools.

Visualization and Result Analysis MATLAB's powerful graphics capabilities enable real-time observation of robot motion states. Using `plot3` for basic 3D plotting or Robotics System Toolbox's `show` function, users can render 3D robot models and dynamically display motion trajectories. Additionally, by analyzing joint torque, velocity, and other data through plotting functions like `subplot` and `animation`, users can evaluate control algorithm performance and optimize robot motion efficiency through quantitative metrics calculation.

Implementing 6-DOF robot simulation in MATLAB not only deepens understanding of robotic principles but also establishes foundations for practical control system development through hands-on coding experience with industry-standard tools.