MATLAB Road Modeling and Vehicle Obstacle Avoidance Based on Artificial Potential Field

The MATLAB-based road modeling and vehicle obstacle avoidance method using Artificial Potential Field (APF) is a widely employed path planning technique in robotics navigation and autonomous driving applications. This approach simulates the concept of electric potential fields from physics, constructing attractive and repulsive fields within the road environment to guide vehicles safely around obstacles toward target destinations.

### Fundamental Principles The core concept of Artificial Potential Field method treats the target point as an attractive source and obstacles as repulsive sources. The attractive field pulls the vehicle toward the goal, while the repulsive field pushes it away from obstacles. By calculating the gradient of the combined potential field, the vehicle's travel direction can be determined.

Attractive Field: Typically implemented using distance-proportional functions, such as U_att = 0.5 * k * (d^2), where k represents the attractive gain coefficient and d denotes the distance between vehicle and target. Repulsive Field: Constructed around obstacles using functions like U_rep = 0.5 * η * (1/d_obs - 1/d_0)^2, where η is the repulsive gain coefficient, d_obs indicates the vehicle-to-obstacle distance, and d_0 defines the obstacle's influence radius.

### MATLAB Implementation Key Points When implementing this algorithm in MATLAB, several critical steps require attention:

Road Environment Modeling: Utilize grid-based methods or continuous coordinate systems to represent road layouts and obstacle distributions, facilitating efficient potential field calculations. Potential Field Superposition: Perform vector superposition of potential fields from target points and all obstacles to generate global potential field distribution maps. Path Generation: Apply gradient descent methods to guide vehicle movement along the potential field's descending direction, incorporating techniques like random perturbations or virtual target points to address local minima issues. Dynamic Obstacle Avoidance: For moving obstacles, implement real-time repulsive field updates and dynamic path replanning algorithms.

### Advantages and Limitations Advantages: High computational efficiency suitable for real-time applications; intuitive physical model facilitates easy understanding and implementation. Limitations: Potential entrapment in local minima leading to planning failures; parameter sensitivity (e.g., k, η coefficients) in complex environments.

This method holds significant value in autonomous driving simulations and UAV navigation applications. Integration with complementary algorithms like A* or RRT can further enhance system robustness and performance.