MATLAB Potential Field Algorithm for Robotic Path Planning

The MATLAB Potential Field Algorithm is a grid-based path planning method that generates a two-dimensional potential energy map by calculating and analyzing the influence of environmental obstacles, enabling optimized path planning for autonomous vehicles. This algorithm simulates physical potential field theory, treating the vehicle as a charged particle that adjusts its position and velocity to reach the destination with minimal cost. Key implementation aspects include: generating attractive potentials toward the goal using Euclidean distance calculations, creating repulsive potentials from obstacles through inverse distance functions, and combining these fields using vector summation. The algorithm's strength lies in its ability to handle both static and dynamic obstacles through real-time potential field updates, making it highly suitable for autonomous driving and robotic navigation applications. MATLAB implementations typically involve grid map initialization, potential field matrix computation using gradient descent optimization, and path smoothing techniques to ensure feasible trajectories.