This paper presents the development of a brain–machine interfacing teleoperation control framework of a mobile robot using quadrupole potential function (QPF). The online brain–computer interface is based on steady-state visually evoked potentials, which employs the multivariate synchronization index classification algorithm to decode the human electroencephalograph (EEG) signals. In this way, human intentions can be recognized and EEG control commands can be produced for the mobile robot. Besides, a novel artificial potential function named QPF is designed to produce the potential fields to parameterize the EEG control commands. The classification results of the EEG signal are related to the distribution of the potential fields, and the collision-free path to a target position is automatically planned by the potential field. Such semi-automatic design builds the direct mapping from human intentions to the mobile robot’s behavior and reduces mental effort or exhaustion on subject’s part. Besides, based on the QPF with the capability of handling nonholonomic constraints, the integrated system can not only achieve obstacle avoidance but is also able to automatically stabilize the robot to a target configuration. Extensive experiment studies were conducted on several subjects to demonstrate the effectiveness of the proposed approaches.

中文翻译：

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基于四极势函数的非完整移动机器人大脑遥操作控制

本文介绍了使用四极势函数（QPF）的移动机器人脑机接口遥操作控制框架的开发。在线脑机接口基于稳态视觉诱发电位，采用多元同步指数分类算法对人类脑电图 (EEG) 信号进行解码。通过这种方式，可以识别人类意图并为移动机器人生成脑电图控制命令。此外，设计了一种名为 QPF 的新型人工势函数来产生势场来参数化 EEG 控制命令。脑电信号的分类结果与势场的分布有关，势场自动规划到目标位置的无碰撞路径。这种半自动设计建立了从人类意图到移动机器人行为的直接映射，并减少了主体的脑力劳动或疲惫。此外，基于具有处理非完整约束能力的 QPF，集成系统不仅可以实现避障，还可以自动将机器人稳定到目标配置。对几个主题进行了广泛的实验研究，以证明所提出方法的有效性。集成系统不仅可以实现避障，还可以自动将机器人稳定到目标配置。对几个主题进行了广泛的实验研究，以证明所提出方法的有效性。集成系统不仅可以实现避障，还可以自动将机器人稳定到目标配置。对几个主题进行了广泛的实验研究，以证明所提出方法的有效性。