Obstacle Avoidance Robot using Artificial Potential Field Method

In two-dimensional environments, the Artificial Potential Field (APF) method is a commonly used approach for robot obstacle avoidance. This method constructs field functions where the robot is treated as a particle and the surrounding environment is modeled as a potential field. The robot experiences force effects within this potential field, enabling collision avoidance with obstacles. The APF method is widely applied in robot navigation and path planning applications. The implementation typically involves calculating attractive forces toward the target destination and repulsive forces from obstacles. Key functions include: - Field function calculation using inverse distance relationships - Gradient computation for force direction determination - Vector summation of attractive and repulsive forces - Velocity and position updates based on resultant forces The core algorithm can be implemented in MATLAB or Python using vector mathematics, where each obstacle contributes a repulsive force inversely proportional to the distance squared, while the target generates an attractive force. Proper parameter tuning ensures smooth navigation without local minima issues.