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IEEE International Conference on robotics and Automation (ICRA)

作者： Barkan Ugurlu Jody A. Saglia Nikos G. Tsagarakis Darwin G. Caldwell Control Systems Laboratory Toyota Technological Institute Nagoya Nagoya Japan Department of Advanced Robotics Istituto Italiano di Tecnologia Genoa Italy

It is known that bipedal robots with passive compliant structures have obvious advantages over stiff robots, as they are able to handle the potential energy management. Therefore, this paper is aimed at presenting a jumping pattern generation method that takes advantage of this property via the utilization of the base resonance frequency, which is of special importance. To begin with, the resonance frequency is determined through a system identification procedure on our actual robot. Consequentially, the vertical component of the CoM is generated via a periodic function in which the resonance frequency is employed. The horizontal component of the CoM is obtained using the ZMP criterion to guarantee the dynamic balance. Having obtained the necessary elements of the CoM trajectory within an analytical manner, joint motions are computed with the help of translational and angular momenta constraints. In order to validate the method, two legged jumping experiments are conducted on our actual compliant robot. In conclusion, we observed repetitive, continuous, and dynamically equilibrated jumping cycles with feasible landing phases.

关键词： Joints Trajectory Resonant frequency Legged locomotion Potential energy Acceleration

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The Anatomy of a Fall: Automated Real-time Analysis of Raw Force Sensor Data from Bipedal Walking Robots and Humans

The Anatomy of a Fall: Automated Real-time Analysis of Raw F...

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IEEE/RSJ International Conference on Intelligent robotics and Systems

作者： Petar Kormushev Barkan Ugurlu Luca Colasanto Nikolaos G. Tsagarakis Darwin G. Caldwell Department of Advanced Robotics Istituto Italiano di Tecnologia via Morego 30 16163 Genova Italy. Control Systems Laboratory Toyota Technological Institute 468-8511 Nagoya Japan.

ISBN: (纸本)9781467317375

An automated approach is proposed which can analyze ground reaction force data from bipedal walking robots and humans. The input of the automated analysis is the raw data from force sensors mounted in the feet of a robot. The output is detailed information, such as detected single support, double support, and swing phases, their durations, timings of events like heel strikes, properties of the phase transitions and of the robot itself. The proposed approach is generic, parameter-free, model-free, robust, computationally efficient, and applicable for real-time use during walking. It can detect early indications of instability that could lead to a fall of the robot. Three real-world experiments are presented: with a compliant bipedal robot, with a stiff humanoid robot, and with a human subject.

关键词： Walking phase transitions robot itself Phase transtormations walking real-time analysis force sensors falls Fall Anatomy Robot human subject ground reaction force time series analysis Human Humanoid robots automatic analysis immatures

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ESICM LIVES 2016: part one Milan, Italy. 1-5 October 2016 Abstracts

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INTENSIVE CARE MEDICINE EXPERIMENTAL 2016年第SUPPL 1期4卷 1-1页

作者： [Anonymous] Academic Medical Center University of Amsterdam Amsterdam Netherlands UMCU Utrecht Netherlands Autonomous University of Barcelona Barcelona Spain Hospital São Francisco Xavier Lisbon Portugal CHU Henri Mondor Medical ICU Créteil France Université de Paris Est-Créteil Groupe de Recherche Clinique CARMAS Créteil France Hôpital Tenon APHP Medical and Surgical ICU Paris France Université Paris Diderot Sorbonne Paris Cité UMR 1153 Paris France Interdepartmental Division of Critical Care University of Toronto Toronto Canada University Health Network University of Toronto Critical Care Medicine Toronto Canada University of Milan - Bicocca Health Sciences School of Medicine and Surgery Monza Italy St Michael’s Hospital University of Toronto Anesthesia Toronto Canada CHU d’Angers Medical ICU Angers France Interdepartmental Division of Critical Care University of Toronto Saint Michael’s Hospital and Keenan Research Centre Toronto Canada University of Birmingham Institute for Inflammation and Ageing Birmingham UK University of Sheffield Mathematics and Statistics Help Centre Sheffield UK Heart of England NHS Foundation Trust Intensive Care Medicine Birmingham UK Department of Surgery University Hospitals Birmingham NHS Trust Birmingham UK University Hospitals Birmingham NHS Trust Anaesthesia and Critical Care Medicine Birmingham UK Queen’s University of Belfast Wellcome-Wolfson Institute for Experimental Medicine Belfast UK Royal Victoria Hospital Intensive Care Medicine Belfast UK University of Warwick Warwick Clinical Trials Unit Coventry UK University of Bari ‘Aldo Moro’ Bari Italy Hospital Israelita Albert Einstein São Paulo Brazil Mayo Clinic Jacksonville USA Mahidol University Faculty of Tropical Medicine Bangkok Thailand University Medical Center Utrecht Intensive Care Utrecht Netherlands University Medical Center Utrecht Julius Center for Health Sciences and Primary Care Utrecht Netherlands Academic Medical Center University of Amste

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Overhead Power Line Detection from UAV Video Images

Overhead Power Line Detection from UAV Video Images

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International Conference on Mechatronics and Machine Vision in Practice

作者： Tang Wen YANG Hang YIN Qiu Qi RUAN Jian Da HAN Jun Tong QI Qing YONG Zi Tong WANG Zeng Qi SUN Beijing Key Laboratory of Advanced Information Science and Network Technology Institute of Information Science Beijing Jiaotong University Beijing 100044 China Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing 100044 China State Key Laboratory of Robotics Shenyang Institute of Automation CAS Shenyang 110016 China Department of Computer Science and Technology Tsinghua University Beijing 100084 China

ISBN: (纸本)9781467316439

Currently, unmanned aerial vehicles (UAVs) are applied to routine inspection tasks of electric distribution networks. As an important information source, machine vision attracts much attention in the area of the UAV's autonomous control. To this end, real-time algorithms are studied-in this paper to detect the power lines in the UAV video images. First, video images are converted into binary images through an adaptive thresholding approach. Then, Hough Transform is used to detect line candidates in the binary images. Finally, a fuzzy C- means (FCM) clustering algorithm is used to discriminate the power lines from the detected line candidates. The properties of power lines are used to remove the spurious lines, and the length and slope of the detected lines are used as features to establish the clustering data set. Experimental results show that the algorithms proposed are effective and able to tolerate noises from complicated terrain background and various illuminations.

关键词： Hough transforms autonomous aerial vehicles computer vision fuzzy set theory pattern clustering power overhead lines power system control video signal processing

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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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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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A Convex Optimization Approach for Online Human Walking Motion Adaptation

A Convex Optimization Approach for Online Human Walking Moti...

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Workshop on Digital Media and Digital Content Management (DMDCM)

作者： Qing Tang Rong Xiong Jian Chu Changjiu Zhou State Key Laboratory of Industrial Control Technology University of Zhejiang Hangzhou China ARICC(Advanced Robotics and Intelligent Control Centre) Singapore polytechnics Singapore

For human-like locomotion, applying human motion capture data (HMCD) to the humanoid robot is a nature way. However, adapting HMCD online is a non-trivial problem. Not only because achieving both kinematic similarity and dynamic stability is difficult but also because such an optimization problem is intractable. This paper proposes an online adaptation approach using convex optimization for humanoid's walking motion, where the optimal objectives of kinematic similarity and dynamic stability can be simultaneously considered and easily balanced. With an inverted pendulum model based dynamics formulation of zero moment point (ZMP) in the framework of Lie group, the optimal objectives and constraints on both kinematics and dynamics are formulized into a second order cone programming problem. The simulation experiments show that the proposed method is promising for adapting HMCD online as well as simultaneously achieving and balancing the kinematic similarity and dynamic stability.

关键词： Joints Kinematics Mathematical model Equations Humanoid robots Dynamics Optimization

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Virtual nano-hand: A stable pushing strategy in AFM based sensorless nanomanipulation

Virtual nano-hand: A stable pushing strategy in AFM based se...

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2011 IEEE International Conference on robotics and Biomimetics, ROBIO 2011

作者： Wang, Zhiyu Liu, Lianqing Hou, Jing Wang, Zhidong Yuan, Shuai Dong, Zaili State Key Laboratory of Robotics Shenyang Institute of Automation CAS Shenyang 110016 China Dept. of Advanced Robotics Chiba Institute of Technology Chiba Japan Faculty of Electrical and Control Engineering Liaoning Technical University Huludao 125105 China Graduate School of Chinese Academy of Sciences Beijing 100001 China College of Information Science and Engineering Northeastern University Shenyang 110004 China

ISBN: (纸本)9781457721373

One of the prerequisites for AFM based nanomanipulation is that the position of the target nanoparticle can be stable controlled within a known area, while this prerequisite is still hindered by the uncertainties including the initial position of the nanoparticle and of the AFM tip, together with the uncertain forces from the substrate and so on. In this paper, a Stochastic Pushing (SP) model and a Stable Pushing Strategy are proposed respectively, which can push the nanoparticle into a bounded target area within a given probability. Simulations and experiment based on the proposed SP model demonstrate the validity of the Stable Pushing Strategy. © 2011 IEEE.

关键词： Nanoparticles

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Feature referenced tip localization enhanced by probability motion model for AFM based nanomanipulations

Feature referenced tip localization enhanced by probability ...

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2011 IEEE International Conference on robotics and Biomimetics, ROBIO 2011

作者： Yuan, Shuai Liu, Lianqing Wang, Zhidong Xi, Ning Wang, Yuechao Dong, Zaili Wang, Zhiyu Wang, Zhibo State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang Liaoning Province 110016 China College of Information and Control Engineering Shenyang Jianzhu University Shenyang 110168 China Graduate School of Chinese Academy of Sciences Beijing 100001 China Department of Electrical and Computer Engineering Michigan State University East Lansing MI 48823 United States Department of Advanced Robotics Chiba Institute of Technology Chiba Japan

ISBN: (纸本)9781457721373

The spatial uncertainties of tip positioning due to the nonlinearity of the PZT scanner and thermal drift hinder the further application of the AFM based nanomanipulation. This paper brings forward feature referenced tip localization enhanced by probability motion model to reduce the spatial uncertainties. An improved motion model is probabilistically built by incorporating the PI model, the creep model and the thermal drift model. For calibrating the accurate model parameters, the statistical experiments are designed and performed. Then the tip position is optimally estimated by combining with a local scan based feature sensing method. The simulation and corresponding experiments are performed to illustrate the validity and feasibility of the calibrated parameters and the algorithm. © 2011 IEEE.

关键词： Micromanipulators

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Reinforcement learning of impedance control in stochastic force fields

Reinforcement learning of impedance control in stochastic fo...

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IEEE International Conference on Development and Learning, ICDL

作者： Freek Stulp Jonas Buchli Alice Ellmer Michael Mistry Evangelos Theodorou Stefan Schaal Computational Learning and Motor Control Laboratory University of Southern California Los Angeles CA USA Department of Advanced Robotics Italian Institute of Technology Genova Italy Disney Research Pittsburgh PA USA

Variable impedance control is essential for ensuring robust and safe physical interaction with the environment. As demonstrated in numerous force field experiments, humans combine two strategies to adapt their impedance to external perturbations: 1) if perturbations are unpredictable, subjects increase their impedance through co-contraction; 2) if perturbations are predictable, subjects learn a feed-forward command to counter the known perturbation. In this paper, we apply the force field paradigm to a simulated 7-DOF robot, by exerting stochastic forces on the robot's end-effector. The robot `subject' uses our model-free reinforcement learning algorithm PI2 to simultaneously learn the end-effector trajectories and variable impedance schedules. We demonstrate how the robot learns the same two-fold strategy to perturbation rejection as humans do, resulting in qualitatively similar behavior. Our results provide a biologically plausible approach to learning appropriate impedances purely from experience, without requiring a model of either body or environment dynamics.

关键词： Computational modeling Noise measurement

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