通过脑电信号实现移动机器人实时障碍物检测与规避

Real-Time Mobile Robot Obstacles Detection and Avoidance Through EEG Signals.

作者信息

Omer Karameldeen, Ferracuti Francesco, Freddi Alessandro, Iarlori Sabrina, Vella Francesco, Monteriù Andrea

机构信息

Department of Information Engineering, Università Politecnica delle Marche, 60131 Ancona, Italy.

Mechanical Department, University of Khartoum, Khartoum 11115, Sudan.

出版信息

Brain Sci. 2025 Mar 30;15(4):359. doi: 10.3390/brainsci15040359.

DOI:10.3390/brainsci15040359

PMID:40309849

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12025689/

Abstract

BACKGROUND/OBJECTIVES: The study explores the integration of human feedback into the control loop of mobile robots for real-time obstacle detection and avoidance using EEG brain-computer interface (BCI) methods. The goal is to assess the possible paradigms applicable to the most current navigation system to enhance safety and interaction between humans and robots.

METHODS

The research explores passive and active brain-computer interface (BCI) technologies to enhance a wheelchair-mobile robot's navigation. In the passive approach, error-related potentials (ErrPs), neural signals triggered when users comment or perceive errors, enable automatic correction of the robot navigation mistakes without direct input or command from the user. In contrast, the active approach leverages steady-state visually evoked potentials (SSVEPs), where users focus on flickering stimuli to control the robot's movements directly. This study evaluates both paradigms to determine the most effective method for integrating human feedback into assistive robotic navigation. This study involves experimental setups where participants control a robot through a simulated environment, and their brain signals are recorded and analyzed to measure the system's responsiveness and the user's mental workload.

RESULTS

The results show that a passive BCI requires lower mental effort but suffers from lower engagement, with a classification accuracy of 72.9%, whereas an active BCI demands more cognitive effort but achieves 84.9% accuracy. Despite this, task achievement accuracy is higher in the passive method (e.g., 71% vs. 43% for subject S2) as a single correct ErrP classification enables autonomous obstacle avoidance, whereas SSVEP requires multiple accurate commands.

CONCLUSIONS

This research highlights the trade-offs between accuracy, mental load, and engagement in BCI-based robot control. The findings support the development of more intuitive assistive robotics, particularly for disabled and elderly users.

摘要

背景/目的：本研究探索如何将人类反馈整合到移动机器人的控制回路中，以便使用脑电图脑机接口（BCI）方法进行实时障碍物检测和避障。目标是评估适用于当前导航系统的可能范式，以提高人机之间的安全性和交互性。

方法

本研究探索被动和主动脑机接口（BCI）技术，以增强轮椅移动机器人的导航能力。在被动方法中，错误相关电位（ErrPs），即用户评论或感知错误时触发的神经信号，能够在无需用户直接输入或命令的情况下自动纠正机器人导航错误。相比之下，主动方法利用稳态视觉诱发电位（SSVEPs），用户通过专注于闪烁刺激来直接控制机器人的运动。本研究评估了这两种范式，以确定将人类反馈整合到辅助机器人导航中最有效的方法。本研究涉及实验设置，参与者通过模拟环境控制机器人，并记录和分析他们的脑信号，以测量系统的响应能力和用户的心理负荷。

结果

结果表明，被动BCI所需的心理努力较低，但参与度较低，分类准确率为72.9%，而主动BCI需要更多的认知努力，但准确率达到84.9%。尽管如此，被动方法的任务完成准确率更高（例如，受试者S2的准确率为71%，而主动方法为43%），因为单个正确的ErrP分类能够实现自主避障，而SSVEP需要多个准确的命令。