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A stochastic self-reconfigurable modular robot with mobility control

A stochastic self-reconfigurable modular robot with mobility...

Joint of the 13th Annual Conference on Towards Autonomous Robotic systems, TAROS 2012 and the 15th Annual FIRA RoboWorld Congress

作者： Ding, Runxiao Eastwood, Paul Mondada, Francesco Groß, Roderich Natural Robotics Lab. Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom LSRO École Polytechnique Fédérale de Lausanne Lausanne Switzerland

ISBN: (纸本)9783642325267

Recently, a variety of robotic systems have been studied under the umbrella term of modular reconfigurable robotics [7,6]. Unlike conventional fixedmorphology robots, whose performance is usually confined by the operating environment and their own size, modular reconfigurable robots have the potential advantage of being able to change their morphology by rearranging the connectivity of their modular units [3]. This project investigates the development and improvement of a self-reconfigurable modular robot designed in previous work to study the emergence of robotic life-forms in a controllable physical environment [4]. The system contains 7 cm x 7 cm square robot units. Each unit is externally propelled and moves stochastically on an air table [2,1]. Each unit can establish, and break, up to four physical connections with other units by means of swiveling permanent magnets [4,1]. Connected units can communicate using infra-red transceivers. © 2012 Springer-Verlag.

关键词： Stochastic systems

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Segregation in Swarms of e-puck Robots Based On the Brazil Nut Effect 12

Segregation in Swarms of e-puck Robots Based On the Brazil N...

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International Conference on Autonomous Agents and Multiagent systems

作者： Jianing Chen Melvin Gauci Michael J. Price Roderich Gross Natural Robotics Lab Department of Automatic Control and Systems Engineering The University of Sheffield

ISBN: (纸本)9781627481564

When a mixture of particles with different attributes undergoes vibration, a segregation pattern is often observed. For example, in muesli cereal packs, the largest particles-the Brazil nutstend to end up at the top. For this reason, the phenomenon is known as the Brazil nut effect. In previous research, an algorithm inspired by this effect was designed to produce segregation patterns in swarms of simulated agents that move on a horizontal plane. In this paper, we adapt this algorithm for implementation on robots with directional vision. We use the e-puck robot as a platform to test our implementation. In a swarm of epucks, different robots mimic disks of different sizes (larger than their physical dimensions). The motion of every robot is governed by a combination of three components: (i) attraction towards a point, which emulates the effect of a gravitational pull, (ii) random motion, which emulates the effect of vibration, and (iii) repulsion from nearby robots, which emulates the effect of collisions between disks. The algorithm does not require robots to discriminate between other robots; yet, it is capable of forming annular structures where the robots in each annulus represent disks of identical size. We report on a set of experiments performed with a group of 20 physical e-pucks. The results obtained in 100 trials of 20 minutes each show that the percentage of incorrectly-ordered pairs of disks from different groups decreases as the size ratio of disks in different groups is increased. In our experiments, this percentage was, on average, below 0.5% for size ratios from 3.0 to 5.0. Moreover, for these size ratios, all segregation errors observed were due to mechanical failures that caused robots to stop moving.

关键词： Brazil nut effect Collective intelligence Emergent behavior Robot disks cultivators swarms Brazil Nuts directional vision algorithms muesli Mechanical failure segregation

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Relating postural synergies to low-D muscular activations: Towards bio-inspired control of robotic hands

Relating postural synergies to low-D muscular activations: T...

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： P. T. Katsiaris P. K. Artemiadis K. J. Kyriakopoulos Control Systems Lab National Technical University of Athens ASU Human-Oriented Robotics and Control Lab Arizona State University USA

Studying human motor control has received increased attention during the past decades. Both the design and control of robotic artifacts may benefit from observation of human behavior. In this paper a novel method for capturing the dynamic behavior of the human hand is presented. The low dimensional kinematics of the human hand, including the wrist, and the low dimensional representation of the muscular activations were correlated through a linear time invariant (LTI) state space model. A linear output regulation controller was used in order to drive a simulated hand and the resulting trajectories were compared with the experimentally captured trajectories.

关键词： Humans Kinematics Trajectory Aerospace electronics Robots Electromyography Muscles

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Haptic object recognition for multi-fingered robot hands

Haptic object recognition for multi-fingered robot hands

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International Symposium on Haptic Interfaces for Virtual Environment and Teleoperator systems (HAPTICS)

作者： Stefan Escaida Navarro Nicolas Gorges Heinz Wörn Julian Schill Tamim Asfour Rüdiger Dillmann Institute for Process Control and Robotics Karlsruhe Institute of Technology Germany Institute for Process Control and Robotics Karlsruhe Institute of Technology Humanoids and Intelligence Systems Lab Karlsruhe Institute of Technology Germany

In this paper, we present an approach for haptic object recognition and its evaluation on multi-fingered robot hands. The recognition approach is based on extracting key features of tactile and kinesthetic data from multiple palpations using a clustering algorithm. A multi-sensory object representation is built by fusion of tactile and kinesthetic features. We evaluated our approach on three robot hands and compared the recognition performance using object sets consisting of daily household objects. Experimental results using the five-fingered hand of the humanoid robot ARMAR, the three-fingered Schunk Dexterous Hand 2 and a parallel Gripper are performed. The results show that the proposed approach generalizes to different robot hands.

关键词： Vectors Grippers Tactile sensors Haptic interfaces

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Navigation functions learning from experiments: Application to anthropomorphic grasping

Navigation functions learning from experiments: Application ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Ioannis F. Filippidis Kostas J. Kyriakopoulos Panagiotis K. Artemiadis Control Systems Lab Department of Mechanical Engineering National and Technical University of Athens Zografou Greece ASU Human-Oriented Robotics and Control Lab School for Engineering of Matter Transport and Energy Ira A. Fulton Schools of Engineering Arizona State University USA

This paper proposes a method to construct Navigation Functions (NF) from experimental trajectories in an unknown environment. We want to approximate an unknown obstacle function and then use it within an NF. When navigating the same destinations with the experiments, this NF should produce the same trajectories as the experiments. This requirement is equivalent to a partial differential equation (PDE). Solving the PDE yields the unknown obstacle function, expressed with spline basis functions. We apply this new method to anthropomorphic grasping, producing automatic trajectories similar to the observed ones. The grasping experiments were performed for a set of different objects, Principal Component Analysis (PCA) allows reduction of the configuration space dimension, where the learning NF method is then applied.

关键词： Noise measurement Trajectory Splines (mathematics) Robots Grasping Principal component analysis Humans

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Recurrent neural state estimation in domains with long-term dependencies 20th

Recurrent neural state estimation in domains with long-term ...

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20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2012

作者： Duell, Siegmund Weichbrodt, Lina Hans, Alexander Udluft, Steffen Siemens AG Corporate Technology Intelligent Systems and Control Otto-Hahn-Ring 6 Munich81739 Germany Berlin University of Technology Machine Learning Franklinstr. 28-29 Berlin10587 Germany Otto-von-Guericke-University Magdeburg P.O.Box 4120 Magdeburg39016 Germany Ilmenau University of Technology Neuroinformatics and Cognitive Robotics Lab P.O.Box 100565 Ilmenau98684 Germany

ISBN: (纸本)9782874190490

This paper presents a state estimation approach for reinforcement learning (RL) of a partially observable Markov decision process. It is based on a special recurrent neural network architecture, the Markov decision process extraction network with shortcuts (MPEN-S). In contrast to previous work regarding this topic, we address the problem of long-term dependencies, which cause major problems in many real-world applications. The architecture is designed to model the reward-relevant dynamics of an environment and is capable to condense large sets of continuous observables to a compact Markovian state representation. The resulting estimate can be used as input for RL methods that assume the underlying system to be a Markov decision process. Although the approach was developed with RL in mind, it is also useful for general prediction tasks. © 2012, *** publication. All rights reserved.

关键词： State estimation

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PANDORA: Persistent Autonomy through Learning, Adaptation, Observation and Re-planning

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IFAC Proceedings Volumes 2012年第5期45卷 367-372页

作者： David M. Lane Francesco Maurelli Tom Larkworthy Darwin Caldwell Joaquim Salvi Maria Fox Konstantinos Kyriakopoulos Ocean Systems Lab Heriot-Watt University Edinburgh UK Advanced Robotics Department Italian Institute of Technology (IIT) Genova Italy Depart. d'Arquitectura i Tecnologia de Computadors Campus Montilivi Universitat de Girona Spain Department of Informatics King's College London UK Control Systems Laboratory National Technical University of Athens (NTUA) Greece

Autonomous robots are not very good at being autonomous. Operating in real environments, they easily get stuck, often ask for help, and generally succeed only when attempting simple tasks in well-known situations. PANDORA is a three year project that will develop and evaluate new computational methods to make underwater robots Persistently Autonomous, significantly reducing the frequency of assistance requests. The key to this is an ability to recognise failure and respond to it, at all levels of abstraction and time constant. Under the guidance of major industrial players, validation tasks of Inspection, cleaning and valve turning will be trialled with partners’ AUVs in Scotland and Spain.

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Learning human reach-to-grasp strategies: Towards EMG-based control of robotic arm-hand systems

Learning human reach-to-grasp strategies: Towards EMG-based ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Minas V. Liarokapis Panagiotis K. Artemiadis Pantelis T. Katsiaris Kostas J. Kyriakopoulos Elias S. Manolakos Control Systems Lab School of Mechanical Engineering National Technical University of Athens Athens Greece ASU Human-Oriented Robotics and Control (HORC) Lab School for Engineering of Matter Transport and Energy Ira A. Fulton Schools of Engineering Arizona State University Tempe AZ USA Dept. of Informatics & Telecommunications University of Athens (UOA) Greece

Reaching and grasping of objects in an everyday-life environment seems so simple for humans, though so complicated from an engineering point of view. Humans use a variety of strategies for reaching and grasping anything from the simplest to the most complicated objects, achieving high dexterity and efficiency. This seemingly simple process of reach-to-grasp relies on the complex coordination of the musculoskeletal system of the upper limbs. In this paper, we study the muscular co-activation patterns during a variety of reach-to-grasp motions, and we introduce a learning scheme that can discriminate between different strategies. This scheme can then classify reach-to-grasp strategies based on the muscular co-activations. We consider the arm and hand as a whole system, therefore we use surface ElectroMyoGraphic (sEMG) recordings from muscles of both the upper arm and the forearm. The proposed scheme is tested in extensive paradigms proving its efficiency, while it can be used as a switching mechanism for task-specific motion and force estimation models, improving EMG-based control of robotic arm-hand systems.

关键词： Electromyography Muscles Support vector machines Grasping Training Accuracy Humans

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Learning Task-Specific Models for Reach to Grasp Movements: Towards EMG-based Teleoperation of Robotic Arm-Hand systems

Learning Task-Specific Models for Reach to Grasp Movements: ...

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IEEE RAS & EMBS International Conference on Biomedical robotics and Biomechatronics

作者： Minas V. Liarokapis Panagiotis K. Artemiadis Pantelis T. Katsiaris Kostas J. Kyriakopoulos Control Systems Lab School of Mechanical Engineering National Technical University of Athens ASU Human-Oriented Robotics and Control (HORC) Lab School for Engineering of Matter

ISBN: (纸本)9781457711992

A learning scheme based on Random Forests is used to decode the EMG activity of 16 muscles of the human arm-hand system to a continuous representation of kinematics in reach-to-grasp movements in 3D space. Classification methods are used to discriminate between significantly different reach to grasp strategies, formulating a switching mechanism that may trigger the use of position and object-specific decoding models (task-specificity). These task-specific models can achieve better estimation results than the general models for the kinematics of different reach-to-grasp movements. The efficacy of the proposed methodology is assessed through a strict validation procedure, based on everyday life reach-to-grasp scenarios and data not previously seen during training. Finally, for demonstration purposes, the authors teleoperate an arm-hand model in the OpenRave simulation environment using the estimated from the EMG signals human motion.

关键词： ElectroMyoGraphy (EMG) EMG-Based Tele-operation Robotic Arm-Hand System Learning Scheme Model Switching Random Forests

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Persistent autonomy: the challenges of the PANDORA project

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IFAC Proceedings Volumes 2012年第27期45卷 268-273页

作者： David M. Lane Francesco Maurelli Petar Kormushev Marc Carreras Maria Fox Konstantinos Kyriakopoulos Ocean Systems Lab Heriot-Watt University Edinburgh UK Advanced Robotics Department Italian Institute of Technology (IIT) Genova Italy Depart. d'Arquitectura i Tecnologia de Computadors Campus Montilivi Universitat de Girona Spain Department of Informatics King's College London UK Control Systems Laboratory National Technical University of Athens (NTUA) Greece

PANDORA is a three year project that is developing new computational methods to make underwater robots Persistently Autonomous, significantly reducing the frequency of assistance requests. The aim of the project is to extend the range of tasks that can be carried on autonomously and increase their complexity while reducing the need for operator assistances. Dynamic adaptation to the change of conditions is very important while addressing autonomy in the real world and not just in well-known situation. The key of Pandora is the ability to recognise failure and respond to it, at all levels of abstraction. Under the guidance of major industrial players, validation tasks of inspection, cleaning and valve turning will be trialled with partners’ AUVs in Scotland and Spain.

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