Biomimetic Adaptive Pure Pursuit Control for Robot Path Tracking Inspired by Natural Motion Constraints.

The essence of biomimetics in human-computer interaction (HCI) is the inspiration derived from natural systems to drive innovations in modern-day technologies. With this in mind, this paper introduces a biomimetic adaptive pure pursuit (A-PP) algorithm tailored for the four-wheel differential drive robot (FWDDR). Drawing inspiration from the intricate natural motions subjected to constraints, the FWDDR's kinematic model mirrors non-holonomic constraints found in biological entities. Recognizing the limitations of traditional pure pursuit (PP) algorithms, which often mimic a static behavioral approach, our proposed A-PP algorithm infuses adaptive techniques observed in nature. Integrated with a quadratic polynomial, this algorithm introduces adaptability in both lateral and longitudinal dimensions. Experimental validations demonstrate that our biomimetically inspired A-PP approach achieves superior path-following accuracy, mirroring the efficiency and fluidity seen in natural organisms.

Teleoperation control of a wheeled mobile robot based on Brain-machine Interface.

This paper presents a novel teleoperation system using Electroencephalogram (EEG) to control the motion of a wheeled mobile robot (WMR). Different from the other traditional motion controlling method, the WMR is braked with the EEG classification results. Furthermore, the EEG will be induced by using the online BMI (Brain Machine Interface) system, and adopting the non-intrusion induced mode SSVEP (steady state visually evoked potentials). Then, user's motion intention can be recognized by canonical correlation analysis (CCA) classifier, which will be converted into motion commands of the WMR. Finally, the teleoperation technique is utilized to manage the information of the movement scene and adjust the control instructions based on the real-time information. Bezier curve is used to parameterize the path planning of the robot, and the trajectory can be adjusted in real time by EEG recognition results. A motion controller based on error model is proposed to track the planned trajectory by using velocity feedback control, providing excellent track tracking performance. Finally, the feasibility and performance of the proposed teleoperation brain-controlled WMR system are verified using demonstration experiments.

Control of a Robotic Arm With an Optimized Common Template-Based CCA Method for SSVEP-Based BCI.

Recently, the robotic arm control system based on a brain-computer interface (BCI) has been employed to help the disabilities to improve their interaction abilities without body movement. However, it's the main challenge to implement the desired task by a robotic arm in a three-dimensional (3D) space because of the instability of electroencephalogram (EEG) signals and the interference by the spontaneous EEG activities. Moreover, the free motion control of a manipulator in 3D space is a complicated operation that requires more output commands and higher accuracy for brain activity recognition. Based on the above, a steady-state visual evoked potential (SSVEP)-based synchronous BCI system with six stimulus targets was designed to realize the motion control function of the seven degrees of freedom (7-DOF) robotic arm. Meanwhile, a novel template-based method, which builds the optimized common templates (OCTs) from various subjects and learns spatial filters from the common templates and the multichannel EEG signal, was applied to enhance the SSVEP recognition accuracy, called OCT-based canonical correlation analysis (OCT-CCA). The comparison results of offline experimental based on a public benchmark dataset indicated that the proposed OCT-CCA method achieved significant improvement of detection accuracy in contrast to CCA and individual template-based CCA (IT-CCA), especially using a short data length. In the end, online experiments with five healthy subjects were implemented for achieving the manipulator real-time control system. The results showed that all five subjects can accomplish the tasks of controlling the manipulator to reach the designated position in the 3D space independently.

The MindGomoku: An Online P300 BCI Game Based on Bayesian Deep Learning.

In addition to helping develop products that aid the disabled, brain-computer interface (BCI) technology can also become a modality of entertainment for all people. However, most BCI games cannot be widely promoted due to the poor control performance or because they easily cause fatigue. In this paper, we propose a P300 brain-computer-interface game (MindGomoku) to explore a feasible and natural way to play games by using electroencephalogram (EEG) signals in a practical environment. The novelty of this research is reflected in integrating the characteristics of game rules and the BCI system when designing BCI games and paradigms. Moreover, a simplified Bayesian convolutional neural network (SBCNN) algorithm is introduced to achieve high accuracy on limited training samples. To prove the reliability of the proposed algorithm and system control, 10 subjects were selected to participate in two online control experiments. The experimental results showed that all subjects successfully completed the game control with an average accuracy of 90.7% and played the MindGomoku an average of more than 11 min. These findings fully demonstrate the stability and effectiveness of the proposed system. This BCI system not only provides a form of entertainment for users, particularly the disabled, but also provides more possibilities for games.

Hardness Recognition of Robotic Forearm Based on Semi-supervised Generative Adversarial Networks.

The hardness recognition is of great significance to tactile sensing and robotic control. The hardness recognition methods based on deep learning have demonstrated a good performance, however, a huge amount of manually labeled samples which require lots of time and labor costs are necessary for the training of deep neural networks. In order to alleviate this problem, a semi-supervised generative adversarial network (GAN) which requires less manually labeled samples is proposed in this paper. First of all, a large number of unlabeled samples are made use of through the unsupervised training of GAN, which is used to provide a good initial state to the following model. Afterwards, the manually labeled samples corresponding to each hardness level are individually used to train the GAN, of which the architecture and initial parameter values are inherited from the unsupervised GAN, and augmented by the generator of trained GAN. Finally, the hardness recognition network (HRN), of which the main architecture and initial parameter values are inherited from the discriminator of unsupervised GAN, is pretrained by a large number of augmented labeled samples and fine-tuned by manually labeled samples. The hardness recognition result can be obtained online by importing the tactile data captured by the robotic forearm into the trained HRN. The experimental results demonstrate that the proposed method can significantly save the manual labeling work while providing an excellent recognition precision for hardness recognition.

Adaptive Neural Control of a Kinematically Redundant Exoskeleton Robot Using Brain-Machine Interfaces.

In this paper, a closed-loop control has been developed for the exoskeleton robot system based on brain-machine interface (BMI). Adaptive controllers in joint space, a redundancy resolution method at the velocity level, and commands that generated from BMI in task space have been integrated effectively to make the robot perform manipulation tasks controlled by human operator's electroencephalogram. By extracting the features from neural activity, the proposed intention decoding algorithm can generate the commands to control the exoskeleton robot. To achieve optimal motion, a redundancy resolution at the velocity level has been implemented through neural dynamics optimization. Considering human-robot interaction force as well as coupled dynamics during the exoskeleton operation, an adaptive controller with redundancy resolution has been designed to drive the exoskeleton tracking the planned trajectory in human brain and to offer a convenient method of dynamics compensation with minimal knowledge of the dynamics parameters of the exoskeleton robot. Extensive experiments which employed a few subjects have been carried out. In the experiments, subjects successfully fulfilled the given manipulation tasks with convergence of tracking errors, which verified that the proposed brain-controlled exoskeleton robot system is effective.