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Weighted-permutation entropy: A complexity measure for time series incorporating amplitude information

Physical Review E 2013年第2期87卷 022911-022911页

作者： Bilal Fadlallah Badong Chen Andreas Keil José Príncipe Computational NeuroEngineering Laboratory Department of Electrical and Computer Engineering University of Florida Gainesville Florida 32611 USA Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an 710049 China NIMH Center for the Study of Emotion and Attention Department of Psychology University of Florida Gainesville Florida 32611 USA

Permutation entropy (PE) has been recently suggested as a novel measure to characterize the complexity of nonlinear time series. In this paper, we propose a simple method to address some of PE's limitations, mainly its inability to differentiate between distinct patterns of a certain motif and the sensitivity of patterns close to the noise floor. The method relies on the fact that patterns may be too disparate in amplitudes and variances and proceeds by assigning weights for each extracted vector when computing the relative frequencies associated with every motif. Simulations were conducted over synthetic and real data for a weighting scheme inspired by the variance of each pattern. Results show better robustness and stability in the presence of higher levels of noise, in addition to a distinctive ability to extract complexity information from data with spiky features or having abrupt changes in magnitude.

关键词： Time series

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The Iceberg Observation Problem: Using Multiple Agents to Monitor and Observe Ablating Target Sources

The Iceberg Observation Problem: Using Multiple Agents to Mo...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Richard A. Coogle Ayanna M. Howard Electronic Systems Laboratory Georgia Tech Research Institute Atlanta GA USA Department of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

ISBN: (纸本)9781479906505

Ships that operate in polar regions continue to face the threat of floating ice sheets and icebergs generated from an ice ablation process. Systems have been implemented to track these threats, with varying degrees of success. In this paper, a definition is proposed for this tracking problem that re-casts it within a class of robotic, multiagent target observation problems. The focus in this new definition is on minimizing the time an initial contact for newly generated targets is obtained, as opposed to obtaining a contact long after a target has been generated. Focusing on the initial contact time provides for the ability to enact early countermeasures. A model is provided for the target sources, as well as metrics for computing costs associated with the model for reallocating robotic agents during an observation task. The effectiveness of the solution compared with an existing observation technique is demonstrated using simulation.

关键词： Field robotics multiagent robotics remote sensing icebergs remote sensing Targets early contact ABLATION tracking problem polar regions Agents

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A manually-actuated MRI compatible robotic manipulator for image-guided interventions

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Physica Medica 2014年 30卷 e55-e56页

作者： Eftychios G. Christoforou Christoforos Keroglou Andrew Webb Nikolaos V. Tsekos Ioannis Seimenis KIOS Research Center for Intelligent Systems and Networks University of Cyprus Cyprus Department of Electrical and Computer Engineering University of Cyprus Cyprus C.J. Gorter Center for High Field MRI Leiden University Medical Center Leiden Netherlands Medical Robotics Laboratory University of Houston Houston TX USA Med. Physics Lab Medical School Democritus University of Thrace Alexandroupolis Greece

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Improvement of Power Efficiency in Flapping-Wing MAVs through a Semi-Passive Motion Control Approach

Improvement of Power Efficiency in Flapping-Wing MAVs throug...

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International Conference on Unmanned Aircraft Systems

作者： Hosein Mahjoubi Katie Byl Robotics Laboratory Department of Electrical and Computer Engineering University of California at Santa Barbara Santa Barbara CA 93106 USA

ISBN: (纸本)9781479908158

Among unmanned aerial systems (UASs), flapping-wing micro aerial vehicles (MAVs) are perhaps the newest field of research. Inspired by small size and agile flight of insects and birds, these vehicles offer a great potential for applications such as reconnaissance, surveillance, search and rescue, mapping, etc. However, practicality of these systems still depends on how we overcome various challenges ranging from control methodology to morphological construction and power supply. Further inspiration from solutions in nature is one way of approaching such problems. Through modeling synchronous muscles in insects, we have previously shown that manipulation of mechanical impedance properties at wing joints can be a very efficient method for controlling lift and thrust production in flapping-wing MAVs. In our current work, we will investigate the power requirements of this control approach through simulated flight experiments. The results indicate that these requirements are considerably lower compared to when conventional control strategies - methods that often rely on changing stroke properties such as frequency or magnitude of the flapping motion - are employed. With less power demands, we believe our proposed control strategy is able to significantly improve flight time in future flapping-wing MAVs.

关键词： Flapping-Wing MAVs Power Efficiency Aerial robotics Hummingbird Flight Tunable Impedance Variable Stiffness Actuator Bio-inspired MAVs Maneuverability

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The AAAI-13 conference workshops

The AAAI-13 conference workshops

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作者： Agrawal, Vikas Archibald, Christopher Bhatt, Mehul Bui, Hung Cook, Diane J. Cortés, Juan Geib, Christopher Gogate, Vibhav Guesgen, Hans W. Jannach, Dietmar Johanson, Michael Kersting, Kristian Konidaris, George Kotthoff, Lars Michalowski, Martin Natarajan, Sriraam O'Sullivan, Barry Pickett, Marc Podobnik, Vedran Poole, David Shastri, Lokendra Shehu, Amarda Sukthankar, Gita IBM Research India Mississippi State University United States University of Bremen Germany Laboratory for Natural Language Understanding Sunnyvale CA United States Washington State University United States University of Toulouse France Drexel University United States Department of Computer Science University of Texas Dallas United States Massey University New Zealand TU Dortmund Germany University of Alberta in Edmonton Canada Fraunhofer IAIS and the University of Bonn Germany MIT United States INSIGHT Centre for Data Analytics University College Cork Ireland Adventium Labs Minneapolis MN United States Indiana University Bloomington IN United States Naval Research Laboratory Washington DC United States Telecommunication Department of the Faculty of Electrical Engineering and Computing University of Zagreb Croatia Department of Computer Science University of British Columbia Vancouver Canada Infosys India George Mason University United States University of Central Florida United States

The AAAI-13 Workshop Program, a part of the 27th AAAI Conference on Artificial Intelligence, was held Sunday and Monday, July 14-15, 2013, at the Hyatt Regency Bellevue Hotel in Bellevue, Washington, USA. The program included 12 workshops covering a wide range of topics in artificial intelligence, including Activity Context-Aware System Architectures (WS-13-05);Artificial Intelligence and robotics Methods in Computational Biology (WS-13-06);Combining Constraint Solving with Mining and Learning (WS-13-07);computer Poker and Imperfect Information (WS-13-08);Expanding the Boundaries of Health Informatics Using Artificial Intelligence (WS-13-09);Intelligent Robotic Systems (WS-13-10);Intelligent Techniques for Web Personalization and Recommendation (WS- 13-11);Learning Rich Representations from Low-Level Sensors (WS-13-12);Plan, Activity, and Intent Recognition (WS-13-13);Space, Time, and Ambient Intelligence (WS-13-14);Trading Agent Design and Analysis (WS-13-15);and Statistical Relational Artificial Intelligence (WS-13-16). Copyright © 2013, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Data mining

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Tunable impedance: A semi-passive approach to practical motion control of insect-inspired MAVs

Tunable impedance: A semi-passive approach to practical moti...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Hosein Mahjoubi Katie Byl Robotics Laboratory Department of Electrical and Computer Engineering University of California Santa Barbara Santa Barbara CA USA

research on insect-inspired flapping-wing micro-aerial vehicles (FWMAV) has grown steadily in the past decade, aiming to address unique challenges in morphological construction, force production, and control strategy. In particular, effective methods for motion control still remain an open problem. This paper analyzes the mechanical impedance properties of the joint and their role in rotation of the wing and force production. The results suggest that in addition to previously observed relationship between set point and drag [1], the average lift force is also related to the stiffness of the joint. Furthermore, as long as changes in impedance properties are small, net lift and drag production are almost independent. These relationships are the basis of `tunable impedance' technique, a new approach to force/motion control in FWMAVs. A simple controller designed based on this method is used to simulate various flight maneuvers. The simulated MAV demonstrates exceptional performance, even in presence of measurement noise. This technique requires a fixed stroke profile for both wings, thus allowing to use a single stroke actuator - in a real MAV - with a bandwidth as low as the frequency of flapping. Impedance actuators also prove to have low bandwidth requirements.

关键词： Force Drag Aerodynamics Impedance Joints Production Vehicles

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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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A novel swarm intelligence algorithm and its application in solving wireless sensor networks coverage problems

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Journal of Networks 2012年第12期7卷 2037-2043页

作者： Mo, Yuanbin Liu, Jizhong Wang, Baolei Jonathan Wu, Q.M. College of Mathematics and Computer Science Guangxi University for Nationalities Nanning 530006 China School of Mechanical and Electrical Engineering Institute of Robotics of Nanchang University Nanchang University Nanchang 330031 China Department of Electrical and Computer Engineering Computer Vision and Sensing Systems Laboratory University of Windsor Windsor N9B3P4 Canada

Wireless sensor networks (WSNs) have attracted a great deal of research due to their wide-range of potential applications. Sensor deployment and coverage problems are their important issues. This article briefly introduces the principle of swarm intelligence (SI). A novel SI algorithm based on information sharing of Particle Swarm Optimization (PSO) and diversity maintenance mechanism of Artificial Immune System (AIS) is designed and the model of coverage problems is given. Its applications in solving different deterministic and random coverage problems are given. The algorithm used in obtaining maximum coverage probability with given number of sensor nodes and minimum number of sensor nodes with required coverage probability of WSNs deterministic coverage, and determining the selected sensor nodes with coverage probability and connectivity requirement of WSNs random coverage, are analyzed in detail. The simulation results show the algorithm is practical. The applications of SI on K- coverage and connectivity problems in the future are also projected in the article. © 2012 ACADEMY PUBLISHER.

关键词： Swarm intelligence

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Stochastic search strategies in 2D using agents with limited perception

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IFAC Proceedings Volumes 2012年第22期45卷 650-654页

作者： David Puljiz Maja Varga Stjepan Bogdan University of Zagreb Faculty of Electrical Engineering and Computing Department of Control and Computer Engineering LARICS–Laboratory for Robotics and Intelligent Control Systems Zagreb Croatia Laboratory of Intelligent Systems Ecole Polytechnique Federale de Lausanne 1015 Lausanne Switzerland

Animals use stochastic search strategies to find a potential nest or to localize the food source. Animals do not rely much on the environmental cues or their sensory inputs. Since their cognitive capabilities are rather limited, search strategies are usually simplistic and stochastic. And yet such simple search strategies result in high efficiency in finding potential targets in limited time intervals. Similar problems with perception of the environment and limited computational power and sensory inputs are observed on the mobile robots used in search scenarios. In spite of their limitations, such robotic systems usually perform deterministic search strategies which heavily depend on environmental cues and sensory input. The research question we are addressing in this paper is which stochastic search strategy has the best performance in terms of area coverage and can stochastic search strategies outperform deterministic search strategies.

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A framework for autonomous self-righting of a generic robot on sloped planar surfaces

A framework for autonomous self-righting of a generic robot ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Chad C. Kessens Daniel C. Smith Philip R. Osteen United States Army Research Laboratory MD USA University of Maryland College Park MD USA US Army Research Laboratory USA Department of Computer Science and Electrical Engineering West Virginia University Morgantown WV USA

Increasingly, robots are being applied to challenges in dynamic, unstructured environments including urban search and rescue (USAR), planetary exploration, and military missions. During the execution of these missions, the robot may unintentionally tip over, rendering it unable to move normally. The ability to self-right and recover in such situations is crucial to mission completion and safe robot recovery. However, to date, nearly all self-righting solutions have been point solutions, each designed for a specific platform. As a first step toward a generic solution, this paper presents a framework for analyzing the self-righting capabilities of any generic robot on sloped planar surfaces. Based on the planar assumption, interactions with the ground can be defined entirely in terms of the robot's convex hull. Motion of arms, legs, or other appendages may change the convex hull shape and/or center of mass position, affecting the robot's orientation. Our framework for solving this problem can be summarized as follows: first, for each stable conformation, we analyze the position of the center of mass relative to the vertical projection of the convex hull face in contact with the ground. From this, we develop a conformation space map, defining stable state sets as nodes and the conformations where discontinuous state changes occur as transitions. Finally, we convert this map into a directed graph, and assign costs to the transitions according to changes in potential energy between states. Based upon the ability to traverse this directed graph to the goal state, one can analyze a robot's ability to self-right. To illustrate each step in our framework, we use a simple two-dimensional robot with a one degree of freedom arm, and then show a case study of iRobot's 510 Packbot®. Ultimately, we project that this framework will be useful both for designing robots with the ability to self-right and for planning joint movements to achieve efficient, autonomous self-righting behav

关键词： Joints Potential energy Educational robots Face Stability analysis Service robots

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