  This article presents a comprehensive review of control algorithms used in
mobile robotics, a field in constant evolution. Mobile robotics has seen
significant advances in recent years, driven by the demand for applications in
various sectors, such as industrial automation, space exploration, and medical
care. The review focuses on control algorithms that address specific challenges
in navigation, localization, mapping, and path planning in changing and unknown
environments. Classical approaches, such as PID control and methods based on
classical control theory, as well as modern techniques, including deep learning
and model-based planning, are discussed in detail. In addition, practical
applications and remaining challenges in implementing these algorithms in
real-world mobile robots are highlighted. Ultimately, this review provides a
comprehensive overview of the diversity and complexity of control algorithms in
mobile robotics, helping researchers and practitioners to better understand the
options available to address specific problems in this exciting area of study.
