MATLAB Simulation of Vehicle Obstacle Avoidance Using Chaotic-Optimized Artificial Potential Field Method

In this article, we present a MATLAB simulation implementation for vehicle obstacle avoidance using the chaotic-optimized artificial potential field method. We begin by examining the principles and advantages of chaotic optimization algorithms, detailing how they enhance traditional artificial potential field approaches for obstacle avoidance. The implementation includes gradient-based optimization functions that dynamically adjust repulsive and attractive potential field parameters.

Next, we demonstrate how the MATLAB simulation code models vehicle kinematics and implements real-time path planning through potential field calculations. The code features parameter tuning modules that systematically optimize obstacle avoidance performance through iterative chaotic sequences. We analyze simulation outcomes and provide enhancement recommendations, including adaptive weighting algorithms and collision detection improvements to further refine vehicle navigation capabilities.

This comprehensive guide combines theoretical foundations with practical MATLAB implementation, offering complete source code to facilitate deeper understanding of chaotic optimization techniques in autonomous navigation systems.