International Journal of Humanoid Robotics

This paper presents a mechatronic platform consists of a stimulator and a prosthesis hand holder, which aims at extrinsic flexible tactile sensor calibration and performance test for prosthesis hand application. The former is driven by a linear motor and the latter is driven by the combination of one 3 dimensions of freedom (DoF) motion stage and two rotation stages. A procedure is developed to calibrate flexible tactile sensors attached on the anthropomorphic prosthetic hand, whose surface is curved. Although the flexible tactile sensors can be easily integrated on to the mentioned curved surface due to its flexibility and compliance, they are usually flat fabricated and characterized under the working of flat situation. Curved surface sensor characterization is not directly available from the manufacturer. Furthermore, when tactile sensors are applied, there would be an elastic layer covered on top for protection and improvement of grasping ability. This elastic material essentially deteriorates the characteristics of the sensor under it, which is another important reason for establishing a platform to re-calibrate tactile sensor mounted on prosthetic hand. In this paper, two kinds of commercial tactile sensors are involved which are force sensing resistor (FSR) representing static measurement sensor and polyvinylidene fluoride (PVDF) sensor representing dynamic measurement sensor where a rigid rod with polydimethylsiloxane (PDMS) covered imitates prosthetic finger. The methodology of calibration compares the measured data obtained from tactile sensor with the one from a load cell fixed on the stimulator. The procedure of receptive-field measurement for tactile sensor on the prosthetic hand is shown and the equation for estimating the position and amplitude of surface load on top of elastic cover is established. Different combinations of tactile sensors are implemented on the rigid substance and two motion patterns of stimulator are performed on them, which are indentation and sliding. The performance of developed platform is also analyzed and it shows the ability of testing flexible tactile sensor for prosthesis hand.

Surface electromyography (sEMG)-based hand motion recognition has a variety of promising applications. While a person performs different hand motions, commands can be extracted to control external devices, such as prosthetic hands, tablets and so forth. The acquisition of discriminative sEMG signals determines the accuracy of intended control commands extraction. This paper develops an 16-channel sEMG signal acquisition system with a novel electrode configuration that is specially designed to collect sEMG on the forearm. Besides, to establish the relationship between multichannel sEMG signals and hand motions, a 2D EMG map is designed. Inspired from the electromyographic (EMG) map, this paper proposes an EMG feature named differential root mean square (DRMS) that somewhat takes the relationship between neighboring EMG channels into account. In the task of four hand motion discrimination by K-means and fuzzy C-means, DRMS outperforms traditional root mean square (RMS) by 29.0% and 36.8%, respectively. The findings of this paper support and guide the use of sEMG techniques to investigate sEMG-based hand motion recognition.

Previous research applied sliding mode control with a sliding perturbation observer (SMCSPO) algorithm as a robust controller to control a surgical robotic instrument and reported that reaction force loaded on the tip can be estimated similarly by the sliding perturbation observer (SPO). However, some factors, such as friction, in which it is difficult to find the model parameters beforehand, can have an effect on the reaction force estimation because the factors are included in the estimated perturbation. This paper addresses the SPO based reaction force estimation method to extract a pure reaction force on a surgical robot instrument in the case of including Coulomb friction due to the operation of cable-pulley structure. Coulomb friction can be estimated experimentally and compensated for from the estimated perturbation. An experimental evaluation was performed to prove the suggested estimation method. The results show that SPO can be substituted for sensors to measure the reaction force. This estimated reaction force will be used to realize the haptic function by sending the reaction force to a master device for a surgeon. The results will help to create surgical benefit such as shortening the practice time of a surgeon and providing haptic information to the surgeon by using it as haptic signal to protect an organ by forming a force boundary.

Flickering source is an indispensable component in steady-state visual evoked potentials (SSVEPs)-based brain–computer interface (BCI), and its background severely influences the potentials evoked by the repetitive stimuli. In this paper, we investigated the problem under three different backgrounds in the context of the SSVEP-BCI-based robot car control, including black screen, static scene and dynamic scene of the environment. In the ten subjects experiment, we found significant decrease in SSVEP amplitude in dynamic scene condition compared to the reference condition black screen (p < 0.05), which resulted in classification accuracy decrease as evaluated by 10-fold cross validation. However, our proposed experiment paradigm has shown that training with static scene or dynamic scene condition could well compensate this performance drop and improve the online robot car control with real-time video feedback. The addressed problem in our application would provide some valuable suggestions when translating the SSVEP-BCI from laboratory exploration into practical usages.

This paper proposes a method calculating joint velocities of a robot which moves the end effector at desired velocity where some of the joint motions are constrained. It is an extension of the Resolved Motion Rate Control (RMRC) method which has been used in cases where there is no constraint on the motion of the joints. The proposed method is called the extended RMRC (E-RMRC). Though the E-RMRC is expressed in a simple form, application of the E-RMRC to a specific robot system is not straightforward and sometimes calls for elaboration. So, the paper describes the application of the E-RMRC to the motion of a mobile manipulator. The example explains how the proposed method is applied to find the joint rate to move the end effector of the mobile manipulator through a desired trajectory while the trajectory of the mobile base is constrained. The application is tested and verified through simulation and experiments.

Control of two-wheel mobile robots (TWMRs) is quite a challenging subject for researchers and educators. Control performance of TWMRs is to satisfy both stable balancing and position tracking simultaneously. When the TWMR is moving fast in forward direction with a proportional-derivative (PD) control method, fast movement to the desired position can be achieved. However, larger oscillations in both the balancing angle and position occur. The time-delayed control (TDC) method reduces the oscillation, but its response is relatively slow. The goal of this paper is to provide a solution to satisfy both stable balancing and position for fast forward movements. This paper presents a control fusion approach between a PD control method and a TDC method to make the performance better. Two controllers are fused together with different weighting factors on the basis of a sigmoidal function to satisfy the control performance. Experimental studies are conducted to validate the proposed control approach.

In this paper, we propose a force-resisting balance control strategy for a walking biped robot subject to an unknown continuous external force. We assume that the biped robot has 12 degrees of freedom (DOFs) with position-controlled joint motors, and that the unknown continuous external force is applied to the pelvis of the biped robot in the single support phase (SSP) walking gait. The suggested balance control strategy has three phases. Phase 1 is to recognize the application of an unknown external force using only zero moment point (ZMP) sensors. Phase 2 is to control the joint motors according to a method that uses a genetic algorithm and the linear interpolation technique. Against an external continuous force, the robot retrieves the pre-calculated solutions and executes the desired torques with interpolation performed in real time. Phase 3 is to make the biped robot move from the SSP to the double support phase (DSP), rejecting external disturbances using the sliding mode controller. The strategy is verified by numerical simulations and experiments.