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Social Odometry: Imitation Based Odometry in Collective Robotics
Alvaro Gutierrez,Alexandre Campo,Francisco C. Santos,Felix Monasterio-Huelin
International Journal of Advanced Robotic Systems , 2009,
Abstract: The improvement of odometry systems in collective robotics remains an important challenge for several applications. Social odometry is an online social dynamics which confers the robots the possibility to learn from the others. Robots neither share any movement constraint nor access to centralized information. Each robot has an estimate of its own location and an associated confidence level that decreases with distance traveled. Social odometry guides a robot to its goal by imitating estimated locations, confidence levels and actual locations of its neighbors. This simple online social form of odometry is shown to produce a self-organized collective pattern which allows a group of robots to both increase the quality of individuals' estimates and efficiently improve their collective performance.
Complete Low-Cost Implementation of a Teleoperated Control System for a Humanoid Robot  [PDF]
Andrés Cela,J. Javier Yebes,Roberto Arroyo,Luis M. Bergasa,Rafael Barea,Elena López
Sensors , 2013, DOI: 10.3390/s130201385
Abstract: Humanoid robotics is a field of a great research interest nowadays. This work implements a low-cost teleoperated system to control a humanoid robot, as a first step for further development and study of human motion and walking. A human suit is built, consisting of 8 sensors, 6 resistive linear potentiometers on the lower extremities and 2 digital accelerometers for the arms. The goal is to replicate the suit movements in a small humanoid robot. The data from the sensors is wirelessly transmitted via two ZigBee RF configurable modules installed on each device: the robot and the suit. Replicating the suit movements requires a robot stability control module to prevent falling down while executing different actions involving knees flexion. This is carried out via a feedback control system with an accelerometer placed on the robot’s back. The measurement from this sensor is filtered using Kalman. In addition, a two input fuzzy algorithm controlling five servo motors regulates the robot balance. The humanoid robot is controlled by a medium capacity processor and a low computational cost is achieved for executing the different algorithms. Both hardware and software of the system are based on open platforms. The successful experiments carried out validate the implementation of the proposed teleoperated system.
A CORBA-Based Control Architecture for Real-Time Teleoperation Tasks in a Developmental Humanoid Robot
Hanafiah Yussof,Genci Capi,Yasuo Nasu,Mitsuhiro Yamano
International Journal of Advanced Robotic Systems , 2011,
Abstract: This paper presents the development of new Humanoid Robot Control Architecture (HRCA) platform based on Common Object Request Broker Architecture (CORBA) in a developmental biped humanoid robot for real‐time teleoperation tasks. The objective is to make the control platform open for collaborative teleoperation research in humanoid robotics via the internet. Meanwhile, to generate optimal trajectory generation in bipedal walk, we proposed a real time generation of optimal gait by using Genetic Algorithms (GA) to minimize the energy for humanoid robot gait. In addition, we proposed simplification of kinematical solutions to generate controlled trajectories of humanoid robot legs in teleoperation tasks. The proposed control systems and strategies was evaluated in teleoperation experiments between Australia and Japan using humanoid robot Bonten‐Maru. Additionally, we have developed a user‐ friendly Virtual Reality (VR) user interface that is composed of ultrasonic 3D mouse system and a Head Mounted Display (HMD) for working coexistence of human and humanoid robot in teleoperation tasks. The teleoperation experiments show good performance of the proposed system and control, and also verified the good performance for working coexistence of human and humanoid robot.
An Embedded System for Tracking Human Motion and Humanoid Interfaces  [cached]
Ming-June Tsai,Hung-Wen Lee, Trinh-Ngoc Chau, Chia-Hong Chao
International Journal of Automation and Smart Technology , 2012, DOI: 10.5875/ausmt.v2i4.144
Abstract: The aim of this research is using embedded CPU to develop a human motion tracking system and construct a motion replication interface for a humanoid robot. In the motion tracking system, we use a CPLD (Complex Programmable Logic Device) which is built in a central control unit (CCU) to generate synchronous signals for all the periphery devices and control the data flow from CCD boards to a PC via a USB chip. An embedded DSP on the CCD board is adopted to control the CCD exposure and conduct image processing. The peak position of exposure was computed by the on-board DSP within sub-pixel accuracy. In the construction of a motion replication interface, the same CCU is used to generate the PWM signals to drive the motors of the humanoid robot. All of the respective firmware coding methods are discussed in this article.
Humanoid Robot With Vision Recognition Control System  [PDF]
Cosmin Basca,Mihai Talos,Remus Brad
Computer Science , 2014,
Abstract: This paper presents a solution to controlling humanoid robotic systems. The robot can be programmed to execute certain complex actions based on basic motion primitives. The humanoid robot is programmed using a PC. The software running on the PC can obtain at any given moment information about the state of the robot, or it can program the robot to execute a different action, providing the possibility of implementing a complex behavior. We want to provide the robotic system the ability to understand more on the external real world. In this paper we describe a method for detecting ellipses in real world images using the Randomized Hough Transform with Result Clustering. Real world images are preprocessed, noise reduction, greyscale transform, edge detection and finaly binarization in order to be processed by the actual ellipse detector. After all the ellipses are detected a post processing phase clusters the results.
Future Robotics Database Management System along with Cloud TPS  [PDF]
Vijaykumar S,Saravanakumar S G
Computer Science , 2011,
Abstract: This paper deals with memory management issues of robotics. In our proposal we break one of the major issues in creating humanoid. . Database issue is the complicated thing in robotics schema design here in our proposal we suggest new concept called NOSQL database for the effective data retrieval, so that the humanoid robots will get the massive thinking ability in searching each items using chained instructions. For query transactions in robotics we need an effective consistency transactions so by using latest technology called CloudTPS which guarantees full ACID properties so that the robot can make their queries using multi-item transactions through this we obtain data consistency in data retrievals. In addition we included map reduce concepts it can splits the job to the respective workers so that it can process the data in a parallel way.
Affective Simulation, Imitation, and the Motor Mirror System
Sergio Levi
Perspectives : International Postgraduate Journal of Philosophy , 2008,
Abstract: In the first part of this paper I draw a comparison between the phenomenon of affective simulation and the process of mirroring believed to involve the motor system. By analyzing both the similarities and the differences I set out to explain what the motor mirror system might be for. The idea that motor mirroring is simply a species of embodied simulation was mainstream when the mirror neurons seemed to be too limited in scope to underpin imitation. By the end of the paper I come to suggest that learning and genuine cases of transfer of skill provide better candidates for the main function of the motor mirror system.
Embedded Distributed Vision System for Humanoid Soccer Robot  [cached]
Francisco Blanes
Journal of Physical Agents , 2011,
Abstract: Computer vision is one of the most challenging applications in sensor systems since the signal is complex from spatial and logical point of view. Due to these characteristics vision applications require high computing resources, which makes them especially difficult to use in embedded systems, like mobile robots with reduced amount memory and computing power. In this work a distributed architecture for humanoid visual control is presented using specific nodes for vision processing cooperating with the main CPU to coordinate the movements of the exploring behaviours. This architecture provides additional computing resources in a reduced area, without disturbing tasks related with low level control (mainly kinematics) with the ones involving vision processing algorithms. The information is exchanged allowing to linking control loops between both nodes.
Counter-Imitation the Speaker`s Recognition and the Reconfirmation System Based on GMM  [PDF]
Zhou Ping,Xinxing Jing
Information Technology Journal , 2007,
Abstract: It is the foundation of network and information system to strengthen the security for improving status authentication. The veins of voice recognition has merit of no memory, no lose and easy to operate and so on. It can reconfirm the status of the speaker based on counter-imitation recognition and reconfirmation the system so that it can safeguard the security of information and the order. Counter-imitation the speaker’s reconfirmation system take the policy-making way which identified the speaker applies to the recognition of confirmation stage. The experimental result had proved the validity and the usability of counter-imitates deliberatively the speaker to reconfirm method.
HCBPM: An Idea toward a Social Learning Environment for Humanoid Robot  [PDF]
Fady Alnajjar,Abdul Rahman Hafiz,Kazuyuki Murase
Journal of Robotics , 2010, DOI: 10.1155/2010/241785
Abstract: To advance robotics toward real-world applications, a growing body of research has focused on the development of control systems for humanoid robots in recent years. Several approaches have been proposed to support the learning stage of such controllers, where the robot can learn new behaviors by observing and/or receiving direct guidance from a human or even another robot. These approaches require dynamic learning and memorization techniques, which the robot can use to reform and update its internal systems continuously while learning new behaviors. Against this background, this study investigates a new approach to the development of an incremental learning and memorization model. This approach was inspired by the principles of neuroscience, and the developed model was named “Hierarchical Constructive Backpropagation with Memory” (HCBPM). The validity of the model was tested by teaching a humanoid robot to recognize a group of objects through natural interaction. The experimental results indicate that the proposed model efficiently enhances real-time machine learning in general and can be used to establish an environment suitable for social learning between the robot and the user in particular. 1. Introduction Developing a complete humanoid robot controller inspired by the principles of neuroscience remains a challenging task for researchers in the field of robotics [1]. The difficulties with developing such a system can be grouped into three major levels, as diagrammatically shown in Figure 1. Level 1 represents a simple mechanism for human-robot interaction, which relies mainly on robotic vision, speech recognition, and sensor-motor interaction. Level 2 represents a dynamic mechanism for learning and memorization, which provides the robot with means to learn and teach, and which can gradually evolve to a level where the robot can develop cognition. Level 3 represents a mechanism for homeostasis, which provides the robot with sufficient internal stability to survive longer in highly changeable environments. Figure 1: Suggested operation principles for an integrated controller for humanoid robots. In our previous work [2, 3], we have proposed a model for improving robotic vision through dynamic edge detection, which contributes positively to the human-robot interaction stage (part of Level 1 in Figure 1). Continuing this series of studies, we focus here on issues related to the enhancement of the learning and memorization capabilities of humanoid robots (part of Level 2 in Figure 1). In reviewing the recent achievements in robotic research, it becomes
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