System for robot control inspired by the functionality and organisation of the human nervous system

This paper presents a model of a control system for robot systems inspired by the functionality and organisation of human neuroregulatory system. Our model was specified using software agents within a formal framework and implemented through Web Services. This approach allows the implementation of the control logic of a robot system with relative ease, in an incremental way, using the addition of new control centres to the system as its behaviour is observed or needs to be detailed with greater precision, without the need to modify existing functionality. The tests performed verify that the proposed model has the general characteristics of biological systems together with the desirable features of software, such as robustness, flexibility, reuse and decoupling.

Decomposition of human behavior using model of competencies: application to robotic control system

For many years, humans and machines have shared the same physical space. To facilitate their interaction with humans, their social integration and for more rational behavior has been sought that the robots demonstrate human-like behavior. For this it is necessary to understand how human behavior is generated, discuss what tasks are performed and how relate to themselves, for subsequent implementation in robots. In this paper, we propose a model of competencies based on human neuroregulator system for analysis and decomposition of behavior into functional modules. Using this model allow separate and locate the tasks to be implemented in a robot that displays human-like behavior. As an example, we show the application of model to the autonomous movement behavior on unfamiliar environments and its implementation in various simulated and real robots with different physical configurations and physical devices of different nature. The main result of this work has been to build a model of competencies that is being used to build robotic systems capable of displaying behaviors similar to humans and consider the specific characteristics of robots.

Model of Competencies for Decomposition of Human Behavior: Application to Control System of Robots

Humans and machines have shared the same physical space for many years. To share the same space, we want the robots to behave like human beings. This will facilitate their social integration, their interaction with humans and create an intelligent behavior. To achieve this goal, we need to understand how human behavior is generated, analyze tasks running our nerves and how they relate to them. Then and only then can we implement these mechanisms in robotic beings. In this study, we propose a model of competencies based on human neuroregulator system for analysis and decomposition of behavior into functional modules. Using this model allow separate and locate the tasks to be implemented in a robot that displays human-like behavior. As an example, we show the application of model to the autonomous movement behavior on unfamiliar environments and its implementation in various simulated and real robots with different physical configurations and physical devices of different nature. The main result of this study has been to build a model of competencies that is being used to build robotic systems capable of displaying behaviors similar to humans and consider the specific characteristics of robots.

Selected characteristics of gait in women of different ages

In our research we have attempted to find out and compare force and time characteristics of gait in women. We measured and compare women in two age groups - 18-30 (group 1) a 46-60 (group 2). The average height of both groups was same, 167 cm, while the average weight and average body mass index were different (group 1 - weight 62 kg, BMI 22,3, group 2 - weight 68 kg, BMI 24,6). For measuring, pressure shoe insoles were used (Pedar Mobile, Novel Munich, 99 sensors, 100 Hz). Each person had three attempts: two trial attempts, the third one was measured. For observation, we selected three stances of each leg, always between the third and eighth stride. We measured force characteristics F1, F2, F3 and time characteristics t, t1, t2, t3. Significant differences between both groups were found in t3 on the left leg (time between peak force in active part of stance and peak force in passive part of stance). With applied force (F1, F2, F3) during stance, after recalculating per a kilogram of weight, no statistically significant differences were found.