基于脑机接口的无人机控制系统研究综述

A survey of brain-computer interface-based unmanned aerial vehicle control systems

中文关键词  脑机接口  脑电信号  无人机  运动控制

英文关键词  brain-computer interface  electroencephalography  unmanned aerial vehicle  motion control

中文摘要

      无人机具有响应快速、部署灵活等独特优势, 在军事及民用领域拥有广阔的应用前景. 随着脑–机接口 (BCI)技术的迅猛发展, 其在无人机控制中的应用得到了愈发广泛的关注. 基于BCI的无人机控制技术可直接通过 解码大脑信号实现人与无人机的自然交互, 是智能无人系统领域的前沿课题. 对基于BCI的无人机控制系统研究进 行全面综述. 首先, 概述基于BCI的无人机控制系统结构; 其次, 根据控制信号的数量, 将现有基于BCI的无人机控制 系统划分为单模态和混合式两类, 并分析各类系统在实际应用中的优势与不足; 然后, 从特征提取、模式分类和多 模态融合3个方面梳理基于BCI的无人机控制系统关键技术; 最后, 讨论基于BCI的无人机控制系统所面临的挑战, 并展望其未来发展趋势, 为相关技术研究提供新的思路和方向.

英文摘要

      Unmanned aerial vehicles (UAVs) have wide application prospects in both military and civilian fields due to their unique advantages such as rapid response and flexible deployment. With the rapid development of brain-computer interface (BCI) technology, the application of BCI in UAV control has received increasing attention. BCI-based UAV control technology can achieve natural interaction between humans and UAVs by directly decoding brain signals, making it a hot topic in the field of intelligent unmanned systems. This paper provides a comprehensive review of BCI-based UAV control systems. First, the structure of BCI-based UAV control system is introduced. Second, the existing BCI-based UAV control systems are classified into single-modal BCIs and hybrid BCIs based on the number of control signals, and their advantages and disadvantages in practical applications are analyzed. Then, the key techniques of BCI-based UAV control systems are summarized from three aspects: feature extraction, pattern classification, and multimodal fusion. Finally, the challenges of BCI-based UAV control systems are discussed, and future development trends are outlined to provide new ideas and directions for related technology research.