APF and PF Source Code Implementation

This documentation presents the source code implementations for both APF (Artificial Potential Field) and PF (Particle Filter) methods, representing two classical approaches in their respective domains. For comprehensive understanding of these algorithms, users need to set up the appropriate environment and execute the provided code. The implementations can be run directly in the command window, where output results facilitate comparative analysis between the two methods. The APF algorithm typically implements obstacle avoidance and path planning through attractive and repulsive potential fields, while PF employs sequential Monte Carlo methods for state estimation through particle propagation and resampling. Users can examine key functions such as potential field calculation in APF and importance sampling in PF to understand their core mechanisms. For deeper investigation, users should reference relevant literature and research findings to conduct detailed studies on both methods' advantages, limitations, and application scenarios. The code structure allows for parameter adjustments to observe algorithm behavior under different configurations. Although this resource provides functional source code for both APF and PF algorithms, thorough comprehension of these methods requires additional theoretical study and practical experimentation with various test cases to fully appreciate their operational characteristics and performance boundaries.