Multi-Sensor Fusion and Deep Learning: A New Frontier in Stabilization Algorithm Optimization

Abstract

The rapid development of automation technology has highlighted the importance of stabilization algorithms in ensuring the stability and functionality of smart devices. These algorithms are critical for maintaining balance and precision, especially in applications such as unmanned aerial vehicles (UAVs), handheld devices, and autonomous systems. This research focuses on the application of stabilization algorithms across various domains, analyzing their principles, advantages, limitations, and potential optimization strategies. The study employs a literature review to investigate the integration of multi-sensor fusion and deep learning techniques to improve system stability. It shows that multi-sensor fusion significantly enhances system robustness by mitigating external disturbances, while deep learning improves autonomous decision-making in dynamic environments, particularly in UAV path planning and task execution. The findings confirm the crucial role of stabilization algorithms in modern systems and provide insights into future optimization directions. By combining deep learning with sensor fusion, future systems are expected to achieve greater stability and autonomy, contributing to the advancement of UAVs and other intelligent systems.