Research on optimization of autonomous navigation algorithms for unmanned systems based on deep learning
基于深度学习的无人系统自主导航算法优化研究

摘要

基于深度学习的无人系统自主导航算法优化研究旨在提高无人系统在复杂环境中的导航精度与鲁棒性。传统的导航算法在动态和不确定环境中往往表现不佳，无法有效处理实时环境变化。本文提出一种新的深度学习方法，通过训练神经网络模型来优化无人系统的路径规划和定位策略，从而提升导航效率。通过实验验证，所提算法在实际应用中比传统算法具有更高的精度和适应性。本研究为无人系统自主导航技术的发展提供了新的思路和优化方案，具有重要的理论与应用价值。

Abstract

The research on optimizing autonomous navigation algorithms for unmanned systems based on deep learning aims to improve the navigation accuracy and robustness of unmanned systems in complex environments. Traditional navigation algorithms often perform poorly in dynamic and uncertain environments, failing to effectively handle real-time environmental changes. This paper proposes a new deep learning method that optimizes the path planning and positioning strategies of unmanned systems by training neural network models, thereby enhancing navigation efficiency. Experimental verification shows that the proposed algorithm has higher accuracy and adaptability than traditional algorithms in practical applications. This research provides new ideas and optimization schemes for the development of autonomous navigation technology for unmanned systems, and has important theoretical and application value.