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P300 is a brain–computer interface (BCI) modality which reflects brains’ processes in stimulus events. Visual stimuli are usually used to elicit event-related P300 components. However, depending on different subjects’ conditions and their current cerebral loads, P300 components occur at posterior to stimuli from 250 to 600 ms roughly. These subjects’ dependent variations affect the performance of BCI. Thus, an estimation model that estimated an appropriate interval for P300 feature extraction is discussed in this paper. An interval type-2 fuzzy logic system trained by artificial bee-colony algorithm was used to find the latency of elicited P300 with a certain range by means of steady-state visually evoked potential. A support vector machine classifier was adopted to classify extracted epochs into target and non-target stimuli. Seven subjects were involved in experiments. Results showed that the performance of information transfer rate was improved by 1.28 % on average if the proposed latency-estimation approach was introduced.

Improving the independent living ability of people who have suffered spinal cord injuries (SCIs) is essential for their quality of life. Brain-computer interfaces (BCIs) provide promising solutions for people with high-level SCIs. This paper proposes a novel and practical P300-based hybrid stimulus-on-device (SoD) BCI architecture for wireless networking applications. Instead of a stimulus-on-panel architecture (SoP), the proposed SoD architecture provides an intuitive control scheme. However, because P300 recognitions rely on the synchronization between stimuli and response potentials, the variation of latency between target stimuli and elicited P300 is a concern when applying a P300-based BCI to wireless applications. In addition, the subject-dependent variation of elicited P300 affects the performance of the BCI. Thus, an adaptive model that determines an appropriate interval for P300 feature extraction was proposed in this paper. Hence, this paper employed the artificial bee colony- (ABC-) based interval type-2 fuzzy logic system (IT2FLS) to deal with the variation of latency between target stimuli and elicited P300 so that the proposed P300-based SoD approach would be feasible. Furthermore, the target and nontarget stimuli were identified in terms of a support vector machine (SVM) classifier. Experimental results showed that, from five subjects, the performance of classification and information transfer rate were improved after calibrations (86.00% and 24.2 bits/ min before calibrations; 90.25% and 27.9 bits/ min after calibrations).

This paper presents an oscillometric blood pressure (BP) measurement approach based on the active control schemes of cuff pressure. Compared with conventional electronic BP instruments, the novelty of the proposed BP measurement approach is to utilize a variable volume chamber which actively and stably alters the cuff pressure during inflating or deflating cycles. The variable volume chamber is operated with a closed-loop pressure control scheme, and it is activated by controlling the piston position of a single-acting cylinder driven by a screw motor. Therefore, the variable volume chamber could significantly eliminate the air turbulence disturbance during the air injection stage when compared to an air pump mechanism. Furthermore, the proposed active BP measurement approach is capable of measuring BP characteristics, including systolic blood pressure (SBP) and diastolic blood pressure (DBP), during the inflating cycle. Two modes of air injection measurement (AIM) and accurate dual-way measurement (ADM) were proposed. According to the healthy subject experiment results, AIM reduced 34.21% and ADM reduced 15.78% of the measurement time when compared to a commercial BP monitor. Furthermore, the ADM performed much consistently (i.e., less standard deviation) in the measurements when compared to a commercial BP monitor.

A high efficient time-shift correlation algorithm was proposed to deal with the peak time uncertainty of P300 evoked potential for a P300-based brain-computer interface (BCI). The time-shift correlation series data were collected as the input nodes of an artificial neural network (ANN), and the classification of four LED visual stimuli was selected as the output node. Two operating modes, including fast-recognition mode (FM) and accuracy-recognition mode (AM), were realized. The proposed BCI system was implemented on an embedded system for commanding an adult-size humanoid robot to evaluate the performance from investigating the ground truth trajectories of the humanoid robot. When the humanoid robot walked in a spacious area, the FM was used to control the robot with a higher information transfer rate (ITR). When the robot walked in a crowded area, the AM was used for high accuracy of recognition to reduce the risk of collision. The experimental results showed that, in 100 trials, the accuracy rate of FM was 87.8% and the average ITR was 52.73 bits/min. In addition, the accuracy rate was improved to 92% for the AM, and the average ITR decreased to 31.27 bits/min. due to strict recognition constraints.

Biped humanoid robots present perception and locomotion capabilities similar to those of human beings. Eye-hand-leg coordination is a basic and required capability for a healthy person, and eye-hand-leg coordination performs various skill-oriented tasks. In this paper, the skill-oriented tasks for playing a basketball game were implemented for a child-size humanoid robot (HuroEvolution) through the use of eye-hand-leg coordination. The “eye” function is responsible for active perceptions in environments, the “hand” function is for gripping and manipulating objects to perform specific tasks, and the “leg” function is used to transport the biped robot to a desired location for the execution of tasks. Coordination among the eye, hand, and leg was achieved using predefined scenarios for various skill-oriented tasks. To utilize flexible and systematic skill-oriented task implementations, a Petri net-based wireless sensor node architecture (PN-WSNA) was employed in this project to define the task scenarios. The control scenario of eye-hand-leg coordination was realized by aggregating fundamental PN-WSNA scenario modules for fast realizations. The coordination model can be further evaluated theoretically to investigate its propensity for avoiding system deadlocks. Finally, the skill-oriented eye-hand-leg coordination tasks for realizing the Federation of International Robot-Soccer Association HuroCup League basketball games were successfully demonstrated to validate our approaches.

A high efficient time-shift correlation algorithm was proposed to deal with the peak time uncertainty of P300 evoked potential for a P300-based brain-computer interface (BCI). The time-shift correlation series data were collected as the input nodes of an artificial neural network (ANN), and the classification of four LED visual stimuli was selected as the output node. Two operating modes, including fast-recognition mode (FM) and accuracy-recognition mode (AM), were realized. The proposed BCI system was implemented on an embedded system for commanding an adult-size humanoid robot to evaluate the performance from investigating the ground truth trajectories of the humanoid robot. When the humanoid robot walked in a spacious area, the FM was used to control the robot with a higher information transfer rate (ITR). When the robot walked in a crowded area, the AM was used for high accuracy of recognition to reduce the risk of collision. The experimental results showed that, in 100 trials, the accuracy rate of FM was 87.8% and the average ITR was 52.73 bits/min. In addition, the accuracy rate was improved to 92% for the AM, and the average ITR decreased to 31.27 bits/min. due to strict recognition constraints.

This paper discusses the trajectory planning problem of a four-degrees-of-freedom (4-DOF) robotic manipulator with similar upper limb structures of human beings. The spatial motions of the end-effector (i.e., wrist) can be desired in terms of controlling four motors of the robotic manipulator; hence, such a configuration results in the redundancy problem. In general, Jacobian solutions perform linear approximations of inverse kinematics problems with redundancy conditions. With human beings, the same wrist position can be found from different limb postures, and these postures depend on different motion scenarios such as writing words, waving hands and shaking hands. Nevertheless, Jacobian solutions are hardly to realize specific limb motion scenarios of human beings. Therefore, this paper proposes a supervised neural network based robotic manipulator trajectory planner which constructs limb motion characteristic models according to relative joint posture features with respect to different motion scenarios. The motion features are further used to provide an auxiliary condition for eliminating the redundancy problem of the inverse kinematics as well as to meet specific motion scenarios. Finally, several trajectory planning results were evaluated in terms of the Jacobian and neural network approaches respectively by using an optical motion capture system. Experiment results demonstrated that the proposed neural network based control system performs similar motion behaviors when compared to Jacobian approaches based on the same test trajectory and motion scenario.

One of the most challenging research topics in robotics is the wall climbing robot. The wall climbing robots are able to perform dangerous operation, such as cleaning the high-rise building. This paper present Trajectory Tracking of a Wheeled Wall Climbing Robot Using PID Controller. To make sure that the wheels can be always well contacted to the wall without sacrificing robot's mobility is the most important consideration for controlling the wall-climbing robot. To evaluate the performance of the robot and the proposed controller, this paper conducted experiments of several trajectory tracking. Practically, encoder sensor provides the spatial posture information for realizing PID control schemes. The sensor is connected to a field-programmable gate array (FPGA) based onboard motion controller to generate control inputs for wheel motors and suction motor according to a specific trajectory. Finally, real tests were performed with the trajectories of circle, triangle and rectangle. The results were evaluated according to the measurement of the accuracy of trajectory.

This paper presents the trajectory tracking approach for a wall-climbing robot by using adaptive control schemes. The most important consideration for controlling the wall-climbing robot is to make sure that the wheels can be always well contacted to the wall regardless of the slope conditions without sacrificing robot's mobility. To consider different slope conditions of the wall, this paper proposes adaptive control schemes to alter the vacuum force so that different gravity effects can be properly dealt with. Practically, pressure and IMU sensors are used to provide the vacuum force and spatial posture information for realizing adaptive control schemes. Finally, MATLAB simulations and real tests for dealing with different surface slope conditions were performed with the triangle trajectories.

In this paper, a kid size humanoid robot, HuroEvolution, is presented. The HuroEvolution is developed for two autonomous robot competitions of RoboCup 2010 and FIRA 2010. In order to perform various competition functions, the robot structure, sensor modules, control systems and software programs are designed to meet the competition requirements of participated games. In addition to the brief introductions of the mechanical structure, sensor modules and control system, this paper focuses on the illustrations of autonomy for various skill-oriented functions. Our solutions are developed based on autonomous sensing and decision approaches. The autonomous sensing approach is realized in terms of typical sensor fusion techniques, which combine the information collected from a web camera and a 3-axis magneto-resistive sensor. The fused sensor information is further used for skill-oriented autonomous decision systems which are responsible of navigations and task executions. Currently, the HuroEvolution is capable of playing the games of soccer, weightlifting, obstacle run, sprint, marathon and basketball. Finally, the proposed skill-oriented functions were verified in the RoboCup 2010 and FIRA 2010 competitions.

This paper describes an electrooculography (EOG) based human-wheelchair interface for wheelchair users. A pair of electrodes which measures the eye-gaze direction of users is desired as wheelchair manipulation commands. In addition to EOG based wheelchair manipulations, this paper also introduces ultrasonic arrays for detecting distances to actively avoid collisions. In order to simplify setup procedures of using this system, two electrodes are mounted on two side arms of eyeglasses respectively that are possible to have tight contacts with skins around eyes. Therefore, wheelchair movements are capable of following eye-gaze directions. On the other hand, an analog biopotential signal amplifier and a laptop computer are used to develop the proposed EOG based human-wheelchair interface controller. Finally, experimental results demonstrate the operations of EOG based human-wheelchair interface.