基于bci的异构机器人群运动控制系统

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI:10.1109/CCIS57298.2022.10016398

Zhenguo Huo, Tao Deng, Zhiyan Dong, L. Zhang, Lan Niu

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引用次数: 1

摘要

提出了一种构建脑机交互(BCI)运动控制系统的方法，该系统利用人的脑电波信号来控制异质机器人群体中不同类型个体的运动。通过建立基于稳态视觉诱发电位(SSVEP)的脑机接口(BCI)系统、用于监控机器人运行状态和外部环境的虚拟现实(VR)双场景以及由无人机和车辆组成的异质机器人群，实现了人类脑波信号对异质机器人群的直接运动控制。实验结果表明，在模拟复杂避障场景下，被试可以应用该系统实现异质机器人群的运动控制，避障成功率达到90%。该系统为传统的异构机器人群运动系统提供了一种新的控制方法，也为如何应对复杂多变的应用场景提供了新的解决方案，对提高系统的感知能力、决策能力和智能水平具有重要意义。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A BCI-Based Motion Control System for Heterogeneous Robot Swarm

The study presents a method to construct a brain-computer interaction (BCI) motion control system that uses human brainwave signals to control the motion of different kinds of individuals in a heterogeneous robot swarm. Through establishing the BCI system based on Stable State Visual Evoked Potential (SSVEP), the virtual reality (VR) twin scene for monitoring the operating state and the external environment of robots and the heterogeneous robot swarm composed of unmanned drones and vehicles, the direct motion control of human brain wave signals on heterogeneous robot swarms is realized. The experimental results present that the subjects can apply the system to achieve motion control of a heterogeneous robot swarm in a simulated complex obstacle avoidance scenario with a 90% obstacle avoidance success rate. The system provides a new control method for traditional heterogeneous robot swarm motion systems, and also provides a new solution for how to deal with sophisticated and versatile application scenarios, which is significant for improving the system's perception capability, decision-making ability, and intelligence level.

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来源期刊

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)

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