Artificial Potential Field Method for Robot Path Planning

In robot path planning, the artificial potential field method is a widely used algorithm. This approach is based on principles from physics where obstacles in the robot's environment are modeled as negative charges, while the robot itself is treated as a positively charged particle. The algorithm calculates both attractive forces (toward the goal) and repulsive forces (from obstacles) to guide the robot's movement. MATLAB serves as an ideal platform for implementing this algorithm through numerical computation and visualization capabilities. The implementation typically involves defining attraction potential functions using Euclidean distance metrics to the target position, while repulsive potential functions utilize distance thresholds from obstacles. Key MATLAB functions include vector calculations for force components, gradient descent for path optimization, and visualization tools for plotting the robot's trajectory through the potential field. By developing and debugging MATLAB code, we can effectively implement the artificial potential field method, creating a simulation where the robot navigates toward its goal while avoiding obstacles through continuous force vector calculations and position updates. The code structure generally includes initialization parameters, obstacle definitions, force calculation loops, and real-time path visualization components.