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International Conference on Information and Automation (ICIA)

作者： Yan Zhou Huiwen Guo Guoyuan Liang Xinyu Wu Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Dept. Mechanical and Automation Engineering The Chinese University of Hong Kong

In this paper, we present an automatic method to remove shadows in light field images. Taking into account the internal structure of the light field data, depth map of the captured scene is extracted to calculate the surface normal. Using nonlocal matching by combining chromaticity, normal and spatial location information in an anisotropic window, the shadow confidence of each pixel is established. For effectively utilizing the prior knowledge, MRF (Markov random fields) is introduced to obtain the shadow label of each pixel iteratively. Once obtained the shadow labels of pixels, inpainting with an energy minimize framework is used to remove shadows. The experimental results on real data demonstrate good performance of this algorithm.

关键词： Image color analysis Cameras Computational modeling Computer vision Minimization Mathematical model Lighting

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Embedding shape memory alloy actuators in miniature articulating polymer structures using in-mold assembly

Embedding shape memory alloy actuators in miniature articula...

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40th Annual North American Manufacturing Research Conference, NAMRC40

作者： Ananthanarayanan, Arvind Ho, Mingyen Ehrlich, Leicester Desai, Jaydev P. Gupta, Satyandra K. Biomimetics Robotics Lab. Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge MA United States Robotics Automation and Medical Systems Lab. Department of Mechanical Engineering University of Maryland College Park MD United States Advanced Manufacturing Lab. Department of Mechanical Engineering University of Maryland College Park MD United States Department of Mechanical Engineering Institute of Systems Research University of Maryland College Park MD United States

ISBN: (纸本)9781622762477

Fabrication and assembly of highly articulating structures has been a challenge for manufacturing engineers for several years. This challenge is further enhanced when the structures are in the miniature size scales. As part of our previous work, we have developed a process called in-mold assembly to tackle this challenging manufacturing and assembly task. However, attaching the actuators to such miniature structures remains a challenging task and can be expensive in terms of costs. In this work, we will demonstrate a single step in-mold assembly method to fabricate miniature multi degree of freedom (DOF) mechanical devices with embedded SMA actuators. We have developed design optimization methods to shield the SMA actuators from the thermal cycles seen during the injection molding process. This is done to minimize thermal impact on the SMA actuators during embedding in in-mold assembled articulating joints. We have also developed experimental methods to calibrate the force and position of the embedded SMA actuators with respect to temperature. Our experimental results indicate that the SMA behavior is very repeatable with respect to temperature. Hence temperature can be used as a sensory feedback for position and force control. Using the approaches developed as part of this work, we have successfully fabricated an in-mold assembled multi DOF miniature robot with embedded SMA actuators.

关键词： Fabrication

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A multiple feature fusion model for image segmentation based on hierarchical visual pathway

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Advances in Information Sciences and Service Sciences 2012年第8期4卷 274-282页

作者： Hu, Dekun Li, Jianping Yang, Simon X. Gregori, Stefano School of Computer Science and Engineering University of Electronic Science and Technology of China China Advanced Robotics and Intelligent Systems Lab School of Engineering University of Guelph Canada

To segment an object from its background in an image for advanced vision processing, this paper presents a novel bio-inspired general framework for image segmentation in complex nature scenes, which is a hierarchical system that mimics the organization of layered early visual area in primate visual cortex. The proposed methodology consists of two typical stages: the first stage is a parallel modular structure including three segmenting operators based on color feature, form feature and texture feature, each of which solves the segmentation problem independently for the same input. Then, a fusion operation, multiple feature fusion segmentation (MFFS), integrates these three feature segmentations together through the backpropagation neuron network (BPNN) in the last stage, which simulates the operation of area following the lateral geniculate nucleus in primary visual cortex. The proposed approach is applied to several segmentation experiments of many single objects in clustering conditions, the result shows that the approach is capable of competing with state-of-the-art systems.

关键词： Neural networks

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An object recognition model with top-down prediction and context

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International Journal of Advancements in Computing technology 2012年第12期4卷 283-290页

作者： Hu, Dekun Li, Jianping Yang, Simon X. University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu 611731 China University of Guelph School of Engineering Advanced Robotics and Intelligent Systems Lab Guelph ON Canada

Based on the trickle-up and trickle-down process in primate visual pathway, an ideal model for object recognition is researched. Firstly, the information of the low spatial frequency are extracted and passed to high visual cortex, the bank of the objects in memory is activated by this input, so the prediction of the possible candidate objects is pulled out. Then, the information of high spatial frequency, which keeps the invariability and selectivity of an object, is gained through the trickle-up process. With the top-down prediction, the context and the detailed information from trickle-up pathway, the object is recognized by Bayes inference. The performance of the proposed model is proved by several experiments.

关键词： Object recognition

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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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Image segmentation based on trickle-up and trickle-down visual pathway in primate

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Journal of Computational Information systems 2012年第9期8卷 3757-3765页

作者： Hu, Dekun Yang, Simon X. Gregori, Stefano Li, Jianping Fu, Bo University of Electronic Science and Technology of China School of Computer Science and Engineering Jianshe Road Chengdu 610054 China University of Guelph School of Engineering Advanced Robotics and Intelligent Systems Lab. Guelph Ont. N1G 2W1 Canada

To locate the object accurately in a scene for further vision processing, a novel bio-inspired model for image segmentation is proposed, which closely follows the computation of trickle-up and trickle-down processing in primate visual pathways. The trickle-up stage is a parallel modular structure consisting of three segmenting operators implementing the function as the parvocellular, magnocellular and koniocelluar pathways. The computation model of trickle-down path, predicts incoming stimuli based on the prior knowledge of the scenes. The proposed approach is applied to several segmentation experiments of clustering conditions, the results demonstrate that the performance of the proposed approach surpasses those achieved by previous top-down or bottom-up schemes on figure-ground segmentation. © 2011 by Binary Information Press.

关键词： Image segmentation

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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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Design and Analysis of Group Escape Behavior for Distributed Autonomous Mobile Robots

Design and Analysis of Group Escape Behavior for Distributed...

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2011 IEEE International Conference on robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)

作者： Hongkyu Min Zhidong Wang Biomimetic Systems Lab. Department of Advanced RoboticsChiba Institute of TechnologyJapan

In designing distributed control architecture of multiple autonomous robot system, behaviors and cooperative control strategies using by insects, birds, fishes, animals and human being are usually considered as good models because they are with many very nice features such as simplicity, flexibility, robustness and fault tolerance, etc. Within them, schooling behavior of fish and flocking behavior of birds were well studied for controlling group motion of robots with local sensing ability and local interaction only. Several research groups have proposed and successfully demonstrated full distributed control architectures for various size robot swarm system via simulations or experiments. To many types of fish schools, we can observe another very interesting behavior, Group Escape Behavior which shows that all fish change their moving directions rapidly and cooperatively without global broadcasting communications while some fish sense a predator. In this study, we proposed a distributed algorithm to perform Group Escape Behavior without inter-robot communication by mimicking behaviors of fish schools. It provides an alternate method for robot teams to achieve some emergency tasks while inter-robot communication is restricted. In this paper, the proposed control mechanism for achieving group escape behavior is introduced, and the characteristics of escape motion mode transition in the swarm are discussed. Some simulation results and experimental results are provided for illustrating the validity of the proposed algorithm, and for analyzing performance of the group escape behavior implemented.

关键词： Educational institutions Robot sensing systems Robot kinematics Marine animals Collision avoidance

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Reports of the AAAI 2011 conference workshops

Reports of the AAAI 2011 conference workshops

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作者： Agmon, Noa Agrawal, Vikas Aha, David W. Aloimonos, Yiannis Buckley, Donagh Doshi, Prashant Geib, Christopher Grasso, Floriana Green, Nancy Johnston, Benjamin Kaliski, Burt Kiekintveld, Christopher Law, Edith Lieberman, Henry Mengshoel, Ole J. Metzler, Ted Modayil, Joseph Oard, Douglas W. Onder, Nilufer O'Sullivan, Barry Pastra, Katerina Precup, Doina Ramachandran, Sowmya Reed, Chris Sariel-Talay, Sanem Selker, Ted Shastri, Lokendra Singh, Satinder Smith, Stephen F. Srivastava, Siddharth Sukthankar, Gita Uthus, David C. Williams, Mary-Anne Department of Computer Science University of Texas at Austin United States Center for Knowledge Driven Information Systems Infosys Labs India Adaptive Systems Section Naval Research Laboratory Washington DC United States Department of Computer Science University of Maryland College Park United States EMC's Center of Excellence in Ireland Research Europe at EMC Ireland Department of Computer Science University of Georgia United States School of Informatics University of Edinburgh United Kingdom Department of Computer Science University of Liverpool United Kingdom Department of Computer Science University of North Carolina Greensboro United States Faculty of Engineering and Information Technology University of Technology Sydney Australia VeriSign Inc. United States Department of Computer Science University of Texas at El Paso United States Machine Learning Department Carnegie Mellon University United States Massachusetts Institute of Technology Media Laboratory United States Carnegie Mellon University's Silicon Valley Campus United States Darrell W. Hughes Program for Religion and Science Dialogue Oklahoma City University United States Department of Computing Science University of Alberta Canada College of Information Studies Institute for Advanced Computer Studies University of Maryland College Park United States Department of Computer Science Michigan Technological University United States Department of Computer Science University College Cork Ireland Cognitive Systems Research Insitute Athens Greece School of Computer Science McGill University Canada Stottler Henke Inc. United States School of Computing University of Dundee United Kingdom Department of Computer Engineering Istanbul Technical University Turkey Convergence Lab Infosys Technologies Ltd. United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor United States Robotics Institute Carnegie Mellon University United States University of Wisconsin

The AAAI-11 workshop program was held Sunday and Monday, August 7-18, 2011, at the Hyatt Regency San Francisco in San Francisco, California USA. The AAAI-11 workshop program included 15 workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were Activity Context Representation: Techniques and Languages;Analyzing Microtext;Applied Adversarial Reasoning and Risk Modeling;Artificial Intelligence and Smarter Living: The Conquest of Complexity;Artifiicial Intelligence for Data Center Management and Cloud Computing;Automated Action Planning for Autonomous Mobile Robots;Computational Models of Natural Argument;Generalized Planning;Human Computation;Human-Robot Interaction in Elder Care;Interactive Decision Theory and Game Theory, 2010;Language-Action Tools for Cognitive Artificial Agents: Integrating Vision, Action, and Language;Lifelong Learning from Sensorimotor Experience;Plan, Activity, and Intent Recognition;and Scalable Integration of Analytics and Visualization. This article presents short summaries of those events. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Human robot interaction

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Control of legged robots with optimal distribution of contact forces

Control of legged robots with optimal distribution of contac...

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2011 11th IEEE-RAS International Conference on Humanoid Robots, HUMANOIDS 2011

作者： Righetti, Ludovic Buchli, Jonas Mistry, Michael Schaal, Stefan Computational Learning and Motor Control Lab. University of Southern California Los Angeles CA 90089 United States Max Planck Institute for Intelligent Systems Tübingen Germany Dept. of Advanced Robotics Italian Institute of Technology Genoa Italy Disney Research Pittsburgh Pittsburgh PA 15213 United States

ISBN: (纸本)9781612848679

The development of agile and safe humanoid robots require controllers that guarantee both high tracking performance and compliance with the environment. More specifically, the control of contact interaction is of crucial importance for robots that will actively interact with their environment. Model-based controllers such as inverse dynamics or operational space control are very appealing as they offer both high tracking performance and compliance. However, while widely used for fully actuated systems such as manipulators, they are not yet standard controllers for legged robots such as humanoids. Indeed such robots are fundamentally different from manipulators as they are underactuated due to their floating-base and subject to switching contact constraints. In this paper we present an inverse dynamics controller for legged robots that use torque redundancy to create an optimal distribution of contact constraints. The resulting controller is able to minimize, given a desired motion, any quadratic cost of the contact constraints at each instant of time. In particular we show how this can be used to minimize tangential forces during locomotion, therefore significantly improving the locomotion of legged robots on difficult terrains. In addition to the theoretical result, we present simulations of a humanoid and a quadruped robot, as well as experiments on a real quadruped robot that demonstrate the advantages of the controller. © 2011 IEEE.

关键词： Controllers

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