The purpose of the present study is to control human biological rhythm and life cycle by optimization of awakening timing. We developed a wearable interface for controlling awakening time named "BRAC (biological ...
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The purpose of the present study is to control human biological rhythm and life cycle by optimization of awakening timing. We developed a wearable interface for controlling awakening time named "BRAC (biological rhythm based awakening timing controller)". BRAC could estimate bio-rhythm by pulse wave from finger tip and send awake signal to user. An ordinary alarm clock operates according to set times that have to be set in advance. However, humans have a rhythm in their sleep, which affects one's sleep depth and wake-up timing. We consider the simplest way to control or reset human's bio-rhythm or life style is to optimize the awakening timing and the sleeping hours. We examined the relationship between controlling awakening timing based on autonomous nerve rhythm and equilibrium function. Our findings suggest indicate that the prototype "BRAC" could evaluate user's biological rhythm and awakes user at the time optimized for physical function of equilibrium.
The present work introduces a study about the use of a deep learning tool to tackle the visual localization. The approach proposed consists in developing a Convolutional Neural Network (CNN) with the aim of addressing...
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We describe a means of human robot interaction based not on natural language but on "quasi symbols," which represent sensory-motor dynamics in the task and/or environment. It thus overcomes a key problem of ...
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We describe a means of human robot interaction based not on natural language but on "quasi symbols," which represent sensory-motor dynamics in the task and/or environment. It thus overcomes a key problem of using natural language for human-robot interaction - the need to understand the dynamic context. The quasi-symbols used are motion primitives corresponding to the attractor dynamics of the sensory-motor flow. These primitives are extracted from the observed data using the recurrent neural network with parametric bias (RNNPB) model. Binary representations based on the model parameters were implemented as quasi symbols in a humanoid robot, Robovie. The experiment task was robot-arm operation on a table. The quasi-symbols acquired by learning enabled the robot to perform novel motions. A person was able to control the arm through speech interaction using these quasi-symbols. These quasi symbols formed a hierarchical structure corresponding to the number of nodes in the model. The meaning of some of the quasi-symbols depended on the context, indicating that they are useful for human-robot interaction.
Jumping and landing movements are characterized by large instantaneous forces, short duration, and a high uncertainty concerning take off and landing points. Such characteristics make conventional types of control and...
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Jumping and landing movements are characterized by large instantaneous forces, short duration, and a high uncertainty concerning take off and landing points. Such characteristics make conventional types of control and robot design inadequate. Here we present an approach to realize motor control of jumping and landing which exploits the synergy between control and mechanical structure. Our experimental system is a pneumatically actuated bipedal robot called "Mowgli". Mowgli's artificial musculoskeletal system consists of six McKibben pneumatic muscle actuators including bi-articular muscle and two legs with hip, knee, and ankle joints. Mowgli can reach jump heights of more than 50% of its body height and can land softly. Our results show a proximo-distal sequence of joint extensions during jumping despite simultaneous motor activity. Extensions in the whole body motion are caused by the compliance and the natural dynamics of the legs. In addition to the experiments with the real robot, we also simulated two types of open loop controllers for vertical jumping with disturbance. We found that the model controlled by open loop motor command through a muscle-tendon mechanism could jump robustly. The simulation results demonstrate the contribution of the artificial musculoskeletal system as a physical feedback loop in explosive movements.
A novel 9 rules self-tuning PD+I fuzzy logic controller applicable for a class of nonlinear plants is proposed in this paper. The controller comprises of three separate fuzzy logic controllers with each uses minimum n...
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A novel 9 rules self-tuning PD+I fuzzy logic controller applicable for a class of nonlinear plants is proposed in this paper. The controller comprises of three separate fuzzy logic controllers with each uses minimum number of rules and the output scaling factor is tuned automatically depending on the tracking error dynamic conditions. The controller is applied to a two-link revolute robot for the tracking control. Simulation results show that the robustness and tracking performance of the proposed controller is comparable to standard PD+I fuzzy logic controller at low and medium speed motions. However, the performance of the proposed new design far exceeds the standard design at high speed motions.
Vision, as a key perceptional capability for cognitive systems relates to rather difficult problems -such as visual object recognition, representation, categorization, and scene understanding. State-of-the-art solutio...
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Vision, as a key perceptional capability for cognitive systems relates to rather difficult problems -such as visual object recognition, representation, categorization, and scene understanding. State-of-the-art solutions, using object appearance based models, already reached certain maturity. They achieve excellent recognition performance and provide learning structures that are subsequently utilized for object recognition and tracking. However, in context of object topology understanding for cognitive tasks, these methods cannot be directly compared with human performance, because it is obvious that appearance based methods do not contribute to understanding of structures in 3D. Research findings from infant psychology and animal investigation give evidence for using hierarchical models of object representation, based on image primitives e.g. such as edges, corners, shading or homogeneity of object colors. It is the objective of this paper to present an approach based on both, findings from biological studies and cognitive science, as enablers for autonomous cognitive investigation of natural scenes and their understanding. We present the architecture of a compound cognitive framework and its first behavioral level with the implementation of a vision model of the mammalian striate visual cortex in five layers. The proposed implementation is exemplified with an object similar to the Necker cube.
This paper presents a novel modular climbing caterpillar named ZC-I. After a related survey on the topic, a systematical summarizing on basic functions provided by this system is given. ZC-I features fast-building mec...
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This paper presents a novel modular climbing caterpillar named ZC-I. After a related survey on the topic, a systematical summarizing on basic functions provided by this system is given. ZC-I features fast-building mechanical structure and low-frequency vibrating passive attachment principle. Active joints actuated by RC servos endow the connecting modules with the ability of changing shapes in two dimensions. After that the discussion focuses on the various locomotion capabilities. Linear movement, turning movement, lateral movement, rotating and rolling movement are achieved based on an inspired control model to produce rhythmic motion. In the end a conclusion and future work are given.
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