NAO Robot Walking Control System Based on Motor Imagery

Brain-computer interface (BCI) technology refers to the spontaneous imagination of a certain mode of movement in the brain to communicate with the computer or control external equipment. This technology helps the limb rehabilitation of paralysed and stroke patients. BCI system will fix electrodes on the experimenter's brain. This paper designs a brain-computer interface system based on motor imagery, and uses it to control the movement of NAO robot. The system is mainly divided into three modules: signal acquisition, signal processing and robot control. In the aspect of signal acquisition, EEG cap, conductive paste, EEG amplifier and the designed upper computer software system are used to realize the design. Computer storage and processing of data. In signal processing, this paper designs a signal feature method based on Common Spatial Pattern (CSP) and Local Characteristic Scale Decomposition (LCD), and then classifies the features into commands to control NAO robots. In the aspect of communication control, the instruction coding is set in advance, and the classification result is set as the instruction to control the different actions of the robot. Socket communication is carried out by client-server mode using UDP protocol. The experimental results of the study also obtained a high accuracy.

[1]  Mingming Zhang,et al.  Effectiveness of robot-assisted therapy on ankle rehabilitation – a systematic review , 2013, Journal of NeuroEngineering and Rehabilitation.

[2]  Tom Carlson,et al.  Comparing shared control approaches for alternative interfaces: A wheelchair simulator experiment , 2017, 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[3]  Qingsong Ai,et al.  Robust Iterative Feedback Tuning Control of a Compliant Rehabilitation Robot for Repetitive Ankle Training , 2017, IEEE/ASME Transactions on Mechatronics.

[4]  Angelika Peer,et al.  Goal-recognition-based adaptive brain-computer interface for navigating immersive robotic systems , 2017, Journal of neural engineering.

[5]  Lin Zhang,et al.  An FDES-Based Shared Control Method for Asynchronous Brain-Actuated Robot , 2016, IEEE Transactions on Cybernetics.

[6]  F. L. D. Silva,et al.  Event-related EEG/MEG synchronization and desynchronization: basic principles , 1999, Clinical Neurophysiology.

[7]  Ricardo Chavarriaga,et al.  Long-Term Stable Control of Motor-Imagery BCI by a Locked-In User Through Adaptive Assistance , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[8]  Qingsong Ai,et al.  Bio-Inspired Design and Iterative Feedback Tuning Control of a Wearable Ankle Rehabilitation Robot , 2016, J. Comput. Inf. Sci. Eng..

[9]  Wei Meng,et al.  Recent development of mechanisms and control strategies for robot-assisted lower limb rehabilitation , 2015 .

[10]  M. Moghavvemi,et al.  Development of a steady state visual evoked potential (SSVEP)-based brain computer interface (BCI) system , 2007, 2007 International Conference on Intelligent and Advanced Systems.

[11]  Luzheng Bi,et al.  A shared controller for brain-controlled assistive vehicles , 2016, 2016 IEEE International Conference on Advanced Intelligent Mechatronics (AIM).

[12]  Qingsong Ai,et al.  An EMG-based force prediction and control approach for robot-assisted lower limb rehabilitation , 2014, 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[13]  Chunfang Liu,et al.  A hybrid EEG-based BCI for robot grasp controlling , 2017, 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[14]  Qingsong Ai,et al.  A Subject-Specific EMG-Driven Musculoskeletal Model for Applications in Lower-Limb Rehabilitation Robotics , 2016, Int. J. Humanoid Robotics.