Brain Machine Interface (BMI) technology is a promising technology for the rehabilitation of patients with serious paralytic impairments. In particular, BMI using Electrocorticograms (ECoGs) provides an interesting new possibility because of its long-term stability, low degree of invasiveness, high spatial-temporal resolution and high signal per noise ratio (S/N ratio). Nevertheless, there are no BMIs with ECoG for devices that incorporate many degrees of freedom, such as the artificial hand. In this paper, as a preliminary experiment, we constructed a signal processing module and decision of movement of external devices such as a robot arm. We observed the self-feeding motions of a monkey (Macaca fuscata) employing ECoG electrodes. We applied the Fast Fourier Transform to the ECoGs and performed bandpass filtering to obtain amplitudes of a few frequency bands. We then implemented Linear Discriminant Analysis (LDA) to classify the motion patterns into four classes. Then, we decided the movement of the robot arm from the cumulative incidences of these classes. We have achieved two noteworthy results, namely a discrimination ratio between 53–61% and a delay of beginning times of ±0.15 seconds.
The rapid advancement of robotics has enabled the robots to serve physical help for elderly people in everyday life. This paper investigates what types of robot elderly the most willing to go shopping with. We investigate the effect into following two factors: conversation and robot-type. Conversation means the no purpose talking which human often do. For robot-type, we prepare humanoid and cart robot. To investigate the effect of these factors, a field experiment was conducted in a real supermarket where 24 elderly participants shopped with robots. The experimental results revealed that they prefer a conversational humanoid as a shopping assistant partner.
This paper presents a learning mechanism that enables co-development of vocal imitation and lexicon acquisition by integrating multimodal information based on subjective consistency. Infant-caregiver interaction is often assumed in modeling infant development. A caregiver is basically assumed to react to an infant in a teaching manner, for example, imitating the learner's voice and labelling an object that it is looking at. However, such tendency is not always expected. Subjective consistency is introduced to judge whether to believe the observed experiences (external input) as a reliable signal for learning. The learning mechanism estimates the outputs of one layer by combining that of other layers and an external input. Based on the proposed mechanism, a simulated infant robot learns associations from its caregiver's phonemes, its own phonemes and objects to co-develop the vocal imitation and lexicon acquisition. The result of computer simulation indicates that the proposed mechanism realized mappings for vocal imitation and lexicon acquisition even when a caregiver does not always react to a learner in a teaching manner.
This paper proposes a new straightforward control method which is applicable to the reaching movement by a three degrees-of-freedom redundant manipulator in the two dimensional vertical plane with the gravitational force. This control law does not include any apparent compensator for the gravity, and not contain the Jacobian matrices. In the formulation of the arm, nonlinear springs and a tendon-driven structure are implemented in the arm according to antagonistic mechanism like the upper limb structure of the human. The proposed controller is designed based on the joint space of the robot, as a result, the singular point on the motion pathway does not appear. Finally, in the simulation of precise positioning control of the end effector, we reveal that the desired trajectories of each joint are all virtual physical quantity, to which the joint is not required to converge through the reaching movement.
This paper deals with evaluating objective Falling Risk and locomotion selection algorithm based on Falling Risk and moving efficiency. A robot evaluates Falling Risk as an Indicator of uncertainty using Bayesian Network based on the error and environment information obtained from sensors. For objectivity of Falling Risk, we have a robot learn conditional probability table and Bayesian Network model by simulation. Locomotion selection model during walking is Semi‐Markov Decision Process, so a robot update locomotion reward that is based on moving velocity as moving efficiency and Falling Risk until the swing leg land. When the swing leg land, the locomotion, having the maximum locomotion reward, is selected by greedy algorithm. As a result, a robot realize the move of environment that is difficult to move with only single locomotion on maintaining the maximum moving efficiency.
This research aims at the development of a new functional robot skin on which micro suction cups are integrated to realize two functions of skin: “adaptive adhesion to rough/curved surfaces” and “anisotropic adhesion”. This skin will be applied to various robot mechanisms such as robot hands and soles of wall-climbing robot feet. This paper reports the design concept of this skin and shows its potential experimentally. The experimental results are promising, which shows high adhesion force even on rough surfaces and asymmetric adhesion properties.
Powered wheelchair is a mobility commonly used for people with disabilities. Recently, to improve the safety and efficiency of powered wheelchairs, various systems with control assistance are proposed. However most of the systems suppose joystick as input device and doesn’t consider about people with difficulties using it. Not all devices alternative to joystick have enough operability compared to joystick. In this paper, considering corridor passing as verification environment, an action control method to drive through the corridor safely and efficiently for wheelchairs controlled by devices with low operability is presented. To achieve safe and efficient driving, proposed method considers the passenger’s input commands and combines the environmental information to select effective direction and speed for the wheelchair instead of passenger. Moreover for environment recognition, corridor detection algorithm is also proposed. To verify the effectiveness of proposed method, several simulations and experiments were carried out.