As for biological mechanisms, which provide a specific functional behavior, the kinematic synthesis is not so simply applicable without deep considerations on requirements, such as the ideal trajectory, fine force control along the trajectory, and possible minimization of the energy consumption. An important approach is the comparison of acknowledged mechanisms to mimic the function of interest in a simplified manner. It helps to consider why the motion trajectory is generated as an optimum, arising from a hidden biological principle on adaptive capability for environmental changes. This study investigated with systematic methods of forward and inverse kinematics known as multibody dynamics (MBD) before going to the kinematic synthesis to explore what the ideal end-effector coordinates are. In terms of walking mechanisms, there are well-known mechanisms, yet the efficacy is still unclear. The Chebyshev linkage with four links is the famous closed-loop system to mimic a simple locomotion, from the 19th century, and recently the Theo Jansen mechanism bearing 11 linkages was highlighted since it exhibited a smooth and less-energy locomotive behavior during walking demonstrations in the sand field driven by wind power. Coincidentally, Klann (1994) emphasized his closed-loop linkage with seven links to mimic a spider locomotion. We applied MBD to three walking linkages in order to compare factors arising from individual mechanisms. The MBD-based numerical computation demonstrated that the Chebyshev, Klann, and Theo Jansen mechanisms have a common property in acceleration control during separate swing and stance phases to exhibit the walking behavior, while they have different tendencies in the total energy consumption and energy-efficacy measured by the ‘specific resistance’. As a consequence, this study for the first time revealed that specific resistances of three linkages exhibit a proportional relationship to the walking speed, which is consistent with human walking and running, yet interestingly it is not consistent with older walking machines, like ARL monopod I, II. The results imply a similarity between biological evolution and robot design, in that the Chebyshev mechanism provides the simplest walking motion with fewer linkages and the Theo Jansen mechanism realizes a fine profile of force changes along the trajectory to reduce the energy consumption acceptable for a large body size by increasing the number of links.

This study presents an effective artificial neural network (ANN) approach to combine measurements from inertial measurement units (IMUs) and time-of-flight (TOF) measurements from an ultra-wideband (UWB) system with OptiTrack Motion Capture System (OptiT-MCS) data to guarantee the positioning accuracy of motion tracking in indoor environments. The proposed fusion approach unifies the following advantages of both technologies: high data rates from the MCS, and global translational precision from the inertial measurement unit (IMU)/UWB localization system. Consequently, it leads to accurate position estimates when compared with data from the IMU/UWB system relative to the OptiT-MCS reference system. The calibrations of the positioning IMU/UWB and MCS systems are utilized in real-time movement with a diverse set of motion recordings using a mobile robot. The proposed neural network (NN) approach experimentally revealed accurate position estimates, giving an enhancement average mean absolute percentage error (MAPE) of 17.56% and 7.48% in the X and Y coordinates, respectively, and the coefficient of correlation R greater than 99%. Moreover, the experimental results prove that the proposed NN fusion is capable of maintaining high accuracy in position estimates while preventing drift errors from increasing in an unbounded manner, implying that the proposed approach is more effective than the compared approaches.

Electroencephalography (EEG) is effectively employed to describe cognitive patterns corresponding to different tasks of motor functions for brain–computer interface (BCI) implementation. Explicit information processing is necessary to reduce the computational complexity of practical BCI systems. This paper presents an entropy-based approach to select effective EEG channels for motor imagery (MI) classification in brain–computer interface (BCI) systems. The method identifies channels with higher entropy scores, which is an indication of greater information content. It discards redundant or noisy channels leading to reduced computational complexity and improved classification accuracy. High entropy means a more disordered pattern, whereas low entropy means a less disordered pattern with less information. The entropy of each channel for individual trials is calculated. The weight of each channel is represented by the mean entropy of the channel over all the trials. A set of channels with higher mean entropy are selected as effective channels for MI classification. A limited number of sub-band signals are created by decomposing the selected channels. To extract the spatial features, the common spatial pattern (CSP) is applied to each sub-band space of EEG signals. The CSP-based features are used to classify the right-hand and right-foot MI tasks using a support vector machine (SVM). The effectiveness of the proposed approach is validated using two publicly available EEG datasets, known as BCI competition III–IV(A) and BCI competition IV–I. The experimental results demonstrate that the proposed approach surpasses cutting-edge techniques.

The paper presents a novel framework for including social competence in pedagogical assistant robots based on recent social neuroscience investigations. An educational model describing the interplay of social interaction and social observation in typical children and children with autistic spectrum conditions (ASC) is proposed. The behavioral results of a study of human gaze movements while viewing a robot tutor are briefly presented. The implications for design of adaptive and intuitive robotic tutoring systems as pedagogical assistants for typical children and children with special educational needs are discussed.