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检索条件"机构=Intelligent Robotics and Systems Lab"

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Micromanipulation using artificial bacterial flagella

Micromanipulation using artificial bacterial flagella

引用

2009 IEEE/RSJ International Conference on intelligent Robots and systems

作者： Li Zhang Jake J. Abbott Lixin Dong Bradley E. Kratochvil Haixin Zhang Kathrin E. Peyer Bradley J. Nelson Multi Scale Robotics Lab Institute of Robotics and intelligent Systems ETH Zurich Zurich Switzerland Department of Mechanical Engineering University of Utah Salt Lake UT USA Department of Electrical and Computer Engineering Michigan State University East Lansing MI USA

Artificial bacterial flagella (ABF) are swimming microrobots that mimic the swimming motion of bacteria. The helical swimmer consists of an InGaAs/GaAs/Cr helical nanobelt tail fabricated by a self-scrolling technique with dimensions similar to a natural flagellum, and a thin soft-magnetic metal ¿head¿ consisting of a Cr/Ni/Au multi-layer. The swimming locomotion of ABF is precisely controlled in 3-D by external rotating magnetic fields. Microsphere manipulation is performed by ABF, and experimental results show that both the position and the orientation of microspheres can be precisely controlled. The propelling force of ABF is in the pico-Newton range. We also describe a swarm-like behavior in which three ABF swim in a pack, indicating the potential to handle several micro objects in parallel. Self-propelled devices such as these are candidates for wireless 6-DOF micro and nanomanipulation tools for handling cellular and sub-cellular objects.

关键词： Microorganisms Chromium Indium gallium arsenide Gallium arsenide Tail Gold Magnetic fields Propulsion Force control Nanoscale devices

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Model-based Heart rate prediction during Lokomat walking

Model-based Heart rate prediction during Lokomat walking

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Alexander C. Koenig Luca Somaini Michael Pulfer Thomas Holenstein Ximena Omlin Martin Wieser Robert Riener Spinal Cord Injury Center University Hospital Balgrist Switzerland Department of Mechanical Engineering and Process Engineering Sensory Motor Systems Lab Institute of Robotics and Intelligent Systems ETH Zurich Zurich Switzerland Spinal Cord Injury Center University of Zurich Switzerland

We implemented a model for prediction of heart rate during Lokomat walking. Using this model, we can predict potential overstressing of the patient and adapt the physical load accordingly. Current models for treadmill based heart rate control neglect the fact that the interaction torques between Lokomat and human can have a significant effect on heart rate. Tests with five healthy subjects lead to a model of sixth order with walking speed and power expenditure as inputs and heart rate prediction as output. Recordings with five different subjects were used for model validation. Future work includes model identification and predictive heart rate control with spinal cord injured and stroke patients.

关键词： Predictive models Heart rate Legged locomotion Humans Cardiology Velocity control Spinal cord injury Patient monitoring Heart rate measurement Automatic control

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Fuzzy machine vision based porosity detection

Fuzzy machine vision based porosity detection

引用

Conference of the North American Fuzzy Information Processing Society - NAFIPS

作者： Pejman Mehran Kudret Demirli Gary Bone Brian Surgenor Intelligent Fuzzy Systems Laboratory Department of Mechanical and Industrial Engineering Concordia University Montreal QUE Canada Robotics and Manufacturing Automation Lab Department Mechanical Engineering McMaster University Hamilton ONT Canada Intelligent Automation Laboratory Department of Mechanical and Materialś Engineering Queen's University Kingston ONT Canada

In this paper an objective fuzzy approach for fast and accurate porosity vision based inspection is presented. An automated methodology of detection of pores, which are formed in aluminum alloys during production of water-pumps for car engines with the die casting method, is described. The proposed method is based on the correlation of the core of pore candidates with twelve developed matrices resulted in five novel features. The fuzzy decision making on porosity detection adopted and presented in this paper, adds great value to the whole production system, by increasing the confidence of the inspectors in the machine performing real-time verification. The fuzzy porosity detection was carried out on a database of 105 gray level images. The proposed model properly identifies 93.36% of the pores in the entire database.

关键词： Machine vision Image databases Inspection Aluminum alloys Engines Die casting Fuzzy systems Decision making Production systems Real time systems

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Uncertainty minimization in multi-sensor localization systems using model selection theory

Uncertainty minimization in multi-sensor localization system...

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作者： Sukumar, Sreenivas R. Bozdogan, Hamparsum Page, David L. Koschan, Andreas F. Abidi, Mongi A. Imaging Robotics and Intelligent Systems Lab University of Tennessee Knoxville United States

ISBN: (纸本)9781424421756

Belief propagation methods are the state-of-the-art with multi-sensor state localization problems. However, when localization applications have to deal with multi-modality sensors whose functionality depends on the environment of operation, we understand the need for an inference framework to identify confident and reliable sensors. Such a framework helps eliminate failed/non-functional sensors from the fusion process minimizing uncertainty while propagating belief. We derive a framework inspired from model selection theory and demonstrate results on real world multi-sensor robot state localization and multi-camera target tracking applications. © 2008 IEEE.

关键词： Target tracking

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Probabilistic contextual situation analysis

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作者： Ramel, Guy Siegwart, Roland Autonomous Systems Lab. Ecole Polytechnique Fédérale de Lausanne Autonomous Systems Lab. Institute of Robotics and Intelligent Systems ETH Zürich

ISBN: (纸本)9783540790068

Mobile robots are gradually appearing in our daily environment. To navigate autonomously in real-world environments and interact with objects and humans, robots face various major technological challenges. Among the required key competencies of such robots is their ability to perceive the environment and reason about it, to plan appropriate actions. However, sensory information perceived from real-world situations is error prone and incomplete and thus often results in ambiguous interpretations. We propose a new approach for object recognition that incorporates visual and range information with spatial arrangement between objects (context information). It is based on using Bayesian networks to fuse and infer information from different *** the proposed framework, we first extract potential objects from the scene image using simple features- characteristics like colour or the relation between height and width. This basic information is easy to extract but often results in ambiguous situations between similar objects. To resolve ambiguities among the detected objects, the relative spatial arrangement (context information) of the objects is used in a second step. Consider, for example, a cola can and a red trash can that are both cylindrical, have similar ratios between width and height and have very similar colours. Depending on their distances from the robot, they may be hard to distinguish. However, if we further consider their spatial arrangement with other objects, e.g. a table, they might be clearly differentiable, the cola can typically standing on the table and the trash can on the floor. This contextual information is therefore a very efficient way to increase drastically the reliability of object recognition and scene interpretation. Moreover, range information from a laser scanner and speech recognition offer complementary information to improve reliability further. Thus, an approach using laser range data to recognize places (such as corridors, crossings

关键词： Object recognition

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Uncertainty minimization in multi-sensor localization systems using model selection theory

Uncertainty minimization in multi-sensor localization system...

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International Conference on Pattern Recognition

作者： Sreenivas R. Sukumar Hamparsum Bozdogan David L. Page Andreas F. Koschan Mongi A. Abidi Imaging Robotics and Intelligent Systems Lab University of Tennessee Knoxville USA

ISBN: (纸本)9781424421749

Belief propagation methods are the state-of-the-art with multisensor state localization problems. However, when localization applications have to deal with multimodality sensors whose functionality depends on the environment of operation, we understand the need for an inference framework to identify confident and reliable sensors. Such a framework helps eliminate failed/non-functional sensors from the fusion process minimizing uncertainty while propagating belief. We derive a framework inspired from model selection theory and demonstrate results on real world multisensor robot state localization and multicamera target tracking applications.

关键词： Uncertainty Sensor fusion Acoustic sensors Robot sensing systems State estimation intelligent robots intelligent sensors Belief propagation Multimodal sensors Global Positioning System

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Sensor planning for PTZ cameras using the probability of camera overload

Sensor planning for PTZ cameras using the probability of cam...

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International Conference on Pattern Recognition

作者： Yi Yao Chung-Hao Chen Besma Abidi David Page Andreas Koschan Mongi Abidi Imaging Robotics and Intelligent Systems (IRIS) Lab University of Tennessee Knoxville USA

Most existing sensor planning algorithms find it difficult to tackle the discrepancy between a PTZ camera¿s limited instant field of view (FOV) and panoramic achievable FOV. In this paper, we introduce the probability of camera overload to resolve this discrepancy and present a sensor planning algorithm for PTZ cameras under the same framework as static cameras. The resulting camera placement achieves the optimal balance between coverage and handoff success rate. Furthermore, our algorithm is able to incorporate the target¿s dynamics into sensor planning. As a result, the system¿s handoff success rate can be maintained in environments with various target densities. Experimental results and comparisons with a reference algorithm proposed by Erdem and Sclaroff verify the effectiveness of our algorithm via a significantly improved handoff success rate.

关键词： Sensor systems intelligent sensors Robot vision systems Surveillance Image sensors Smart cameras intelligent robots Probability Robot sensing systems intelligent systems

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Spectral range selection for face recognition under various illuminations

Spectral range selection for face recognition under various ...

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IEEE International Conference on Image Processing

作者： H. Chang Y. Yao A. Koschan B. Abidi M. Abidi Imaging Robotics and Intelligent Systems (IRIS) Lab University of Tennessee Knoxville TN USA

Multispectral imaging and fusion of multispectral images in visible spectrum have been employed and proved to enhance the recognition performance over conventional broad-band images, especially when the illumination changes. In this paper, we further our study toward the recognition performance of a smaller number of spectral band images. Given the illumination, we propose an automated method that specifies the optimal spectral ranges to achieve an improved recognition performance compared with conventional broad-band images. Our discovery can be practically used for a new customized sensor design associated with given illuminations for an improved face recognition performance over conventional broad-band images.

关键词： Face recognition Lighting Image recognition Multispectral imaging Narrowband Image sensors Probes Optical imaging intelligent robots Infrared imaging

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A fuzzy inference controller with Accelerate/Brake Module for mobile robots

A fuzzy inference controller with Accelerate/Brake Module fo...

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IEEE International Conference on Automation and Logistics

作者： Suo Tan Simon X. Yang Advanced Robotics and Intelligent Systems (ARIS) Lab School of Engineering University of Guelph Guelph ONT Canada

ISBN: (纸本)9781424425020

In this paper, a fuzzy-inference controller with Accelerate/Brake (A/B) Module is proposed for navigation control of a mobile robot in dynamic environments. This approach includes target seeking, obstacle avoidance, and optimal path planning. By utilizing the fuzzy controller, a mobile robot could approach a target in a dynamic environment autonomously. The A/B Module of the proposed system is to enable the mobile robot to make human-like decisions as it moves toward a target. Under the control of the proposed fuzzy inference model, the robot can perform well both in avoiding static and moving obstacles like human beings along a reasonable short path.

关键词： Mobile robots Robots Robot sensing systems Navigation Acceleration Wheels Heuristic algorithms

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Learning structurally discriminant features in 3D faces

Learning structurally discriminant features in 3D faces

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IEEE International Conference on Image Processing

作者： Sreenivas R. Sukumar Hamparsum Bozdogan David L. Page Andreas F. Koschan Mongi A. Abidi Imaging Robotics and Intelligent Systems Lab Department of Electrical and Computer Engineering University of Tennessee Knoxville USA Department of Statistics University of Tennessee Knoxville USA

In this paper, we derive a data mining framework to analyze 3D features on human faces. The framework leverages kernel density estimators, genetic algorithm and an information complexity criterion to identify discriminant feature-clusters of lower dimensionality. We apply this framework on human face anthropometry data of 32 features collected from each of the 300 3D face mesh models. The feature-subsets that we infer as the output establishes domain knowledge for the challenging problem of 3D face recognition with dense 3D gallery models and sparse or low resolution probes.

关键词： Probes Face recognition Data mining Humans Facial features Feature extraction Principal component analysis Linear discriminant analysis Input variables intelligent robots

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