Pursuit Algorithm

Pursuit algorithms are iterative optimization methods aiming to find the best approximation of a target, whether it's a path for autonomous vehicles, a desired formation for multiple agents, or an optimal feature subset for machine learning. Current research focuses on improving the efficiency and accuracy of these algorithms, particularly through adaptations like incorporating uncertainty handling (e.g., using unscented transforms), developing novel variants for specific applications (e.g., regulated pure pursuit for robotics, adaptive lookahead pure pursuit for racing), and exploring connections to submodularity for theoretical guarantees. These advancements have significant implications for various fields, including robotics, autonomous driving, and signal processing, by enabling more robust, efficient, and interpretable solutions to complex optimization problems.