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Low speed control of an autonomous vehicle using a hybrid fractional order controller

Low speed control of an autonomous vehicle using a hybrid fr...

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International Conference on Control, Instrumentation, and Automation

作者： S. Hassan HosseinNia Inés Tejado Blas M. Vinagre Vicente Milanés Jorge Villagrá Dept. of Electrical Electronic and Automation Engineering University of Extremadura Badajoz Spain AUTOPIA Program Centre for Automation and Robotics (CAR UPM-CSIC) Madrid Spain

Highly non-linear vehicle dynamics plays an important role in autonomous driving systems, especially in congested traffic situations at very low speeds. Due to this fact, accurate controllers are needed in order to ensure safety during navigation. In this paper, based on a previous fractional order speed control, an improved fractional order control is presented to control a commercial Citroën C3 prototype - which has automatic driving capabilities - at low speeds, which considers a hybrid model of the vehicle. Specifically, two different fractional order PI α controllers are designed to act over the throttle and brake pedals, respectively. Concerning to the uncertain dynamics of the system during the brake action parameters are tuned to design a robust controller. In addition, the system is modeled as hybrid fractional order differential inclusions. Experimental and simulation results, obtained in a real circuit, are given to demonstrate the effectiveness of the proposed strategies for cruise control at low speeds.

关键词： Vehicles Vehicle dynamics Robustness Acceleration Control systems Integrated circuit modeling Global Positioning System

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A Machine Learning Approach to Falling Detection and Avoidance for Biped Robots

A Machine Learning Approach to Falling Detection and Avoidan...

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SICE Annual Conference

作者： Jeong-Jung Kim Yeoun-Jae Kim Ju-Jang Lee Department of Electrical Engineering KAIST Robotics Program KAIST

ISBN: (纸本)9784907764388

A falling avoidance of biped robots is an important research topic to use the robot in a human life environment. In this paper, we propose a machine learning approach to falling detection and avoidacne for biped robots. Support Vector Machine (S VM) is used as the machine learning algorithm and it detects the falling state of the robot based on acceleration value of torso and center of pressure value of the robot. When the falling is detected, the reaction module produces gait for extending areas of supporting polygon of the robot. The main contribution of the paper is falling detection of the biped robot based on the sensor data and machine learning algorithm without explicit dynamic parameters of the robot and predefined threshold value.

关键词： Falling Avoidance Biped Robot Machine Learning

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Report on the AAAI 2010 robot exhibition

Report on the AAAI 2010 robot exhibition

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作者： Anderson, Monica D. Chernova, Sonia Dodds, Zachary Thomaz, Andrea L. Touretzky, David S. Computer Science Department University of Alabama United States Robotics Engineering Program Worcester Polytechnic Institute United States Department of Computer Science Harvey Mudd College United States School of Interactive Computing Georgia Institute of Technology United States Center for the Neural Basis of Cognition Carnegie Mellon University United States

The 19th robotics program at the annual AAAI conference was held in Atlanta, Georgia, in July 2010. In this article we give a summary of three components of the exhibition: the Small-Scale Manipulation Challenge: Robotic Chess;the Learning by Demonstration Challenge;and the Education Track. In each section we detail the challenge task. We also describe the participating teams, highlight the research questions they tackled, and briefly describe the systems they demonstrated. Copyright © 2011, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Exhibitions

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Probabilistic voxel mapping using stereo matching confidence

Probabilistic voxel mapping using stereo matching confidence

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International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)

作者： Sijong Kim Seunguk Ahn Myung Jin Chung Department of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon South Korea Robotics Program Korea Advanced Institute of Science and Technology Daejeon South Korea

ISBN: (纸本)9781457707223

There has been meaningful research into the development of 3D world modeling techniques that are important requisite for intelligent vehicle navigation. In this paper we describe a 3D probabilistic voxel mapping process using stereo matching confidence. Proposed 3D probabilistic voxel map is more reliable representation than general voxel map that just contains the occupancy information. To get the matching confidence value, we evaluate stereo matching costs and its propagation. We test the proposed method in large-scale outdoor environment.

关键词： Three dimensional displays Stereo vision Probabilistic logic Solid modeling Uncertainty Computational modeling Intelligent vehicles

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Vision-based direction determination of a mobile robot in indoor environment

Vision-based direction determination of a mobile robot in in...

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Inaugural IEEE-IES Digital EcoSystems and Technologies Conference, DEST

作者： Yeoun Jae Kim Minho Park Dong-Hyun Lee Joon-Yong Lee Ju-Jang Lee Robotics Program Korea Advanced Institute of Science and Technology Daejeon South Korea Department of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon South Korea

In this paper, a vision based direction determination of a mobile robot in indoor environment is defined to detect the robot direction normal to the surrounding wall in indoor environment. For this purpose, we propose the direction determination algorithm using the Hough transformation and horizontal line search with vision information. Our proposal shows that this scheme can be used in any indoor environment where there are horizontal line structures (corner, any notice on the wall). In order to verify the proposed algorithm, we perform the experiments by the mobile robot utilizing a webcam as a vision sensor. The experimental results show that this method is verified for navigation of a mobile robot in indoor environment since its longitudinal pathway direction can be determined automatically.

关键词： Mobile robots Cameras Robot vision systems Image edge detection Indoor environments

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Fast SLAM using polar scan matching and particle weight based occupancy grid map for mobile robot

Fast SLAM using polar scan matching and particle weight base...

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International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)

作者： Hyun Chul Roh Chang Hun Sung Min Tae Kang Myung Jin Chung Robotics Program Korea Advanced Institute of Science and Technology Daejeon South Korea Department of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon South Korea

ISBN: (纸本)9781457707223

Simultaneous Localization and Mapping is the one of essential techniques for mobile robot navigation. In this paper, we propose a fast SLAM method that uses particle weight based occupancy grid map, and also works well with in indoor environment. In addition, in the prediction step, our method that uses the polar scan matching and wheel odometry information improves the accuracy of prediction pose. In order to evaluate our proposed method, we use a simulation and the 2-wheeled mobile robot that is able to move in large-scale indoor environment. The experimental results show that our method is suitable for fast and accurate mapping and localization algorithm for mobile robot.

关键词： Simultaneous localization and mapping Mobile robots Robot kinematics Indoor environments Particle filters Service robots

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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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Planar marker-based localization of a robotic fish in a public aquarium

Planar marker-based localization of a robotic fish in a publ...

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International Conference on Control, Automation and Systems ( ICCAS)

作者： Donghwa Lee Donghoon Kim Hyun Myung Department of Civil and Environmental Engineering KAIST Daejeon South Korea Robotics Program KAIST Daejeon South Korea Korea Advanced Institute of Science and Technology Daejeon Daejeon KR

A robotic fish localization system in a public aquarium is presented in this paper. The system is built on the marker-based camera pose estimation method. A webcam, an ARM processor, and multiple planar markers are employed for real-time camera pose estimation. The camera is attached at the bottom of the head of the robotic fish. Since the camera and the fish body are connected rigidly, the pose estimation method gives the absolute 6 degree of freedom (DOF) location of the robotic fish. The experiments above ground and underwater validate the proposed localization system. Through the tests in a public aquarium with the integrated robotic fish system, the autonomous swimming was successfully demonstrated.

关键词： Cameras Robot vision systems Estimation Machine vision Transmission line matrix methods

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Dynamic Brain-Machine Interface: a novel paradigm for bidirectional interaction between brains and dynamical systems

Dynamic Brain-Machine Interface: a novel paradigm for bidire...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society

作者： Francois D. Szymanski Marianna Semprini Ferdinando A. Mussa-Ivaldi Luciano Fadiga Stefano Panzeri Alessandro Vato Robotics Brain and Cognitive Sciences Dept. Istituto Italiano di Tecnologia Physical Medicine & Rehabilitation Mechanical and Biomedical Engineering at Northwestern University and Sensory Motor Performance Program Rehabilitation Institute of Chicago Section of Human Physiology University of Ferrara

ISBN: (纸本)9781424441211

Brain-Machine Interfaces (BMIs) are systems which mediate communication between brains and artificial devices. Their long term goal is to restore motor functions, and this ultimately demands the development of a new generation of bidirectional brain-machine interfaces establishing a two-way brain-world communication channel, by both decoding motor commands from neural activity and providing feedback to the brain by electrical stimulation. Taking inspiration from how the spinal cord of vertebrates mediates communication between the brain and the limbs, here we present a model of a bidirectional brain-machine interface that interacts with a dynamical system by generating a control policy in the form of a force field. In our model, bidirectional communication takes place via two elements: (a) a motor interface decoding activities recorded from a motor cortical area, and (b) a sensory interface encoding the state of the controlled device into electrical stimuli delivered to a somatosensory area. We propose a specific mathematical model of the sensory and motor interfaces guiding a point mass moving in a viscous medium, and we demonstrate its performance by testing it on realistically simulated neural responses.

关键词： Cerebrum DYNAMICAL SYSTEMS Bidirectional viscous medium Diencephalon control strategy Communication Research Motor Sensation Mathematical Model DECODING field theory (physics)

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Study of classification of failure states for Small Unmanned Ground Vehicle by response surface methodology

Study of classification of failure states for Small Unmanned...

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International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)

作者： Young Kook Kim Dooyeol Koh Sang Hoon Lee Soohyun Kim Robotics Program Korea Advanced Institute of Science and Technology Daejeon South Korea Division of Mechanical Engineering School of Mechanical AeroSpace and Systems Engineering Korea Advanced Institute of Science and Technology Daejeon South Korea 5th Research and Development Institute-2 Agency for Defense Development Daejeon South Korea

ISBN: (纸本)9781457707223

SUGV(Small Unmanned Ground Vehicle) used for human rescue and military operation is need to overcome the environment that tough terrains such as a hole or high obstacles. As far as the survival of the vehicle and the preserving ability of the information in the vehicle are concerned, it is essential to have an approach for recognizing unattainable driving states. However, despite the fact that the study of obstacle avoidance and stabilization of SUGV is focused on, it is hardly found out ways of classifying unattainable driving states with the sensors mounted on the SUGV unit. In order to overcome the problem, this paper proposes a practical way for classifying SUGV Failure State(SFS) with the meaningful parameters by statistical method, response surface methodology(RSM).

关键词： Response surface methodology Equations Vehicles Mathematical model Robot sensing systems

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