Educational Approach to Trajectory Prediction for Dynamic Obstacle Avoidance Using Curve Fitting Methods

This paper present a comprehensive approach to predicting the trajectories of moving obstacles for dynamic obstacle avoidance in mobile robotics. Traditional methods often rely on complex machine learning models; however, this paper explores a more accessible and mathematically intuitive approach using second-order curve fitting. By analyzing past movements of obstacles, we can approximate future trajectories with curves such as parabolas, ellipses, and hyperbolas. This method not only simplifies calculations but also provides explicit mathematical representations of predicted paths, facilitating easier integration into navigation algorithms. Through detailed explanations and simulations, we demonstrate the effectiveness of this approach and its potential for enhancing the safety and efficiency of mobile robotic navigation in dynamic environments. This paper aims to equip students and practitioners with the knowledge and tools necessary to implement and understand trajectory prediction using fundamental mathematical techniques.