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Conference on intelligent robots and computer vision XXX - algorithms and techniques

作者： Theisen, Bernard L. Kosinski, Andrew US Army TARDEC Warren MI 48397 USA

ISBN: (纸本)9780819494351

The intelligent Ground Vehicle Competition (IGVC) is one of four, unmanned systems, student competitions that were founded by the Association for Unmanned Vehicle Systems International (AUVSI). The IGVC is a multidisciplinary exercise in product realization that challenges college engineering student teams to integrate advanced control theory, machine vision, vehicular electronics and mobile platform fundamentals to design and build an unmanned system. Teams from around the world focus on developing a suite of dual-use technologies to equip ground vehicles of the future with intelligent driving capabilities. Over the past 20 years, the competition has challenged undergraduate, graduate and Ph.D. students with real world applications in intelligent transportation systems, the military and manufacturing automation. To date, teams from over 80 universities and colleges have participated. This paper describes some of the applications of the technologies required by this competition and discusses the educational benefits. The primary goal of the IGVC is to advance engineering education in intelligent vehicles and related technologies. The employment and professional networking opportunities created for students and industrial sponsors through a series of technical events over the four-day competition are highlighted. Finally, an assessment of the competition based on participation is presented.

关键词： intelligent robots autonomous systems ground vehicles vehicle control engineering education IGVC

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active Visual Planning for Mobile Robot Teams Using Hierarchical POMDPs

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IEEE TRANSACTIONS ON ROBOTICS 2013年第4期29卷 975-985页

作者： Zhang, Shiqi Sridharan, Mohan Washington, Christian Texas Tech Univ Dept Comp Sci Lubbock TX 79409 USA Louisiana State Univ Baton Rouge LA 70814 USA

Key challenges to widespread deployment of mobile robots include collaboration and the ability to tailor sensing and information processing to the task at hand. Partially observable Markov decision processes (POMDPs), which are an instance of probabilistic sequential decision-making, can be used to address these challenges in domains characterized by partial observability and nondeterministic action outcomes. However, such formulations tend to be computationally intractable for domains that have large complex state spaces and require robots to respond to dynamic changes. This paper presents a hierarchical decomposition of POMDPs that incorporates adaptive observation functions, constrained convolutional policies, and automatic belief propagation, enabling robots to retain capabilities for different tasks, direct sensing to relevant locations, and determine the sequence of sensing and processing algorithms best suited to any given task. A communication layer is added to the POMDP hierarchy for belief sharing and collaboration in a team of robots. All algorithms are evaluated in simulation and on physical robots, localizing target objects in dynamic indoor domains.

关键词： Bayesian methods hierarchical systems intelligent robots Markov processes multirobot systems planning robot vision systems

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Remotely controlling of mobile robots using gesture captured by the Kinect and recognized by machine learning method

Remotely controlling of mobile robots using gesture captured...

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Conference on intelligent robots and computer vision XXX - algorithms and techniques

作者： Hsu, Roy Chaoming Jian, Jhih-Wei Lin, Chih-Chuan Lai, Chien-Hung Liu, Cheng-Ting Dept Elect Engn 300 Syuefu Rd Chiayi 60004 Taiwan Dept Comp Sci Informat Engn Chiayi 60004 Taiwan

ISBN: (纸本)9780819494351

The main purpose of this paper is to use machine learning method and Kinect and its body sensation technology to design a simple, convenient, yet effective robot remote control system. In this study, a Kinect sensor is used to capture the human body skeleton with depth information, and a gesture training and identification method is designed using the back propagation neural network to remotely command a mobile robot for certain actions via the Bluetooth. The experimental results show that the designed mobile robots remote control system can achieve, on an average, more than 96% of accurate identification of 7 types of gestures and can effectively control a real e-puck robot for the designed commands.

关键词： Robot remote control Kinect sensor gesture recognition back propagation neural network

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Panoramic Stereo Sphere vision

Panoramic Stereo Sphere Vision

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Conference on intelligent robots and computer vision XXX - algorithms and techniques

作者： Feng, Weijia Zhang, Baofeng Roning, Juha Zong, Xiaoning Yi, Tian Tianjin Normal Univ Coll Comp & Informat Engn Tianjin Peoples R China Tianjin Univ Technol Tianjin Key Lab Control Theory & Appl Complied In Tianjin Peoples R China Univ Oulu Dept Comp Sci & Engn SF-90100 Oulu Finland

ISBN: (纸本)9780819494351

Conventional stereo vision systems have a small field of view (FOV) which limits their usefulness for certain applications. While panorama vision is able to "see" in all directions of the observation space, scene depth information is missed because of the mapping from 3D reference coordinates to 2D panoramic image. In this paper, we present an innovative vision system which builds by a special combined fish-eye lenses module, and is capable of producing 3D coordinate information from the whole global observation space and acquiring no blind area 360 degrees x360 degrees panoramic image simultaneously just using single vision equipment with one time static shooting. It is called Panoramic Stereo Sphere vision (PSSV). We proposed the geometric model, mathematic model and parameters calibration method in this paper. Specifically, video surveillance, robotic autonomous navigation, virtual reality, driving assistance, multiple maneuvering target tracking, automatic mapping of environments and attitude estimation are some of the applications which will benefit from PSSV.

关键词： Panoramic Stereo Sphere vision fish-eye lens parameters calibration

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LABRADOR: A Learning Autonomous Behavior-based Robot for Adaptive Detection and Object Retrieval

LABRADOR: A Learning Autonomous Behavior-based Robot for Ada...

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Conference on intelligent robots and computer vision XXX - algorithms and techniques

作者： Yamauchi, Brian Moseley, Mark Brookshire, Jonathan iRobot Corp 8 Crosby Dr Bedford MA 01730 USA MIT Comp Sci & Artificial Intelligence Lab Cambridge MA 02139 USA

ISBN: (纸本)9780819494351

As part of the TARDEC-funded CANINE (Cooperative Autonomous Navigation in a Networked Environment) Program, iRobot developed LABRADOR (Learning Autonomous Behavior-based Robot for Adaptive Detection and Object Retrieval). LABRADOR was based on the rugged, man-portable, iRobot PackBot unmanned ground vehicle (UGV) equipped with an explosives ordnance disposal (EOD) manipulator arm and a custom gripper. For LABRADOR, we developed a vision-based object learning and recognition system that combined a TLD (track-learn-detect) filter based on object shape features with a color-histogram-based object detector. Our vision system was able to learn in real-time to recognize objects presented to the robot. We also implemented a waypoint navigation system based on fused GPS, IMU (inertial measurement unit), and odometry data. We used this navigation capability to implement autonomous behaviors capable of searching a specified area using a variety of robust coverage strategies - including outward spiral, random bounce, random waypoint, and perimeter following behaviors. While the full system was not integrated in time to compete in the CANINE competition event, we developed useful perception, navigation, and behavior capabilities that may be applied to future autonomous robot systems.

关键词： CANINE mobile robotics computer vision machine learning navigation human-robot interaction

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Optimizing feature selection strategy for adaptive object identification in a noisy environment

Optimizing feature selection strategy for adaptive object id...

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Conference on intelligent robots and computer vision XXX - algorithms and techniques

作者： Pandya, Sagar Lu, Thomas Chao, Tien-Hsin Univ Southern Calif Los Angeles CA 90089 USA CALTECH Jet Prop Lab Pasadena CA 91125 USA

ISBN: (纸本)9780819494351

We present the development of a multi-stage automatic target recognition (MS-ATR) system for computer vision in robotics. This paper discusses our work in optimizing the feature selection strategies of the MS-ATR system. Past implementations have utilized Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filtering as an initial feature selection method, and principal component analysis (PCA) as a feature extraction strategy before the classification stage. Recent work has been done in the implementation of a modified saliency algorithm as a feature selection method. Saliency is typically implemented as a "bottom-up" search process using visual sensory information such as color, intensity, and orientation to detect salient points in the imagery. It is a general saliency mapping algorithm that receives no input from the user on what is considered salient. We discuss here a modified saliency algorithm that accepts the guidance of target features in locating regions of interest (ROI). By introducing target related input parameters, saliency becomes more focused and task oriented. It is used as an initial stage for the fast ROI detection method. The ROIs are passed to the later stages for feature extraction and target identification process.

关键词： optimization target detection saliency vision

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GPU based real-time trinocular stereovision

GPU based real-time trinocular stereovision

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intelligent robots and computer vision XXX: algorithms and techniques

作者： Yao, Yuanbin Linton, R.J. Padir, Taskin Robotics Engineering Program Electrical and Computer Engineering Department Worcester Polytechnic Institute 100 Institute Road Worcester MA 01609 United States

ISBN: (纸本)9780819494351

Most stereovision applications are binocular which uses information from a 2-camera array to perform stereo matching and compute the depth image. Trinocular stereovision with a 3-camera array has been proved to provide higher accuracy in stereo matching which could benefit applications like distance finding, object recognition, and detection. This paper presents a real-time stereovision algorithm implemented on a GPGPU (General-purpose graphics processing unit) using a trinocular stereovision camera array. Algorithm employs a winner-take-all method applied to perform fusion of disparities in different directions following various image processing techniques to obtain the depth information. The goal of the algorithm is to achieve real-time processing speed with the help of a GPGPU involving the use of Open Source computer vision Library (OpenCV) in C++ and NVidia CUDA GPGPU Solution. The results are compared in accuracy and speed to verify the improvement. © 2013 SPIE-IS&T.

关键词： Graphics processing unit

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Training industrial robots with gesture recognition techniques

Training industrial robots with gesture recognition techniqu...

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Conference on intelligent robots and computer vision XXX - algorithms and techniques

作者： Piane, Jennifer Raicu, Daniela Furst, Jacob Yaskawa Amer Inc Waukegan IL 60085 USA Depaul Univ Chicago IL 60604 USA

ISBN: (纸本)9780819494351

In this paper we propose to use gesture recognition approaches to track a human hand in 3D space and, without the use of special clothing or markers, be able to accurately generate code for training an industrial robot to perform the same motion. The proposed hand tracking component includes three methods: a color-thresholding model, naive Bayes analysis and Support Vector Machine (SVM) to detect the human hand. Next, it performs stereo matching on the region where the hand was detected to find relative 3D coordinates. The list of coordinates returned is expectedly noisy due to the way the human hand can alter its apparent shape while moving, the inconsistencies in human motion and detection failures in the cluttered environment. Therefore, the system analyzes the list of coordinates to determine a path for the robot to move, by smoothing the data to reduce noise and looking for significant points used to determine the path the robot will ultimately take. The proposed system was applied to pairs of videos recording the motion of a human hand in a, real. environment to move the end-affector of a SCARA robot along the same path as the hand of the person in the video. The correctness of the robot motion was determined by observers indicating that motion of the robot appeared to match the motion of the video.

关键词： Naive Bayes Support Vector Machine Stereo matching Gesture recognition Industrial Robot

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Proceedings of SPIE-IS and T Electronic Imaging - intelligent robots and computer vision XXX: algorithms and techniques

Proceedings of SPIE-IS and T Electronic Imaging - Intelligen...

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intelligent robots and computer vision XXX: algorithms and techniques

ISBN: (纸本)9780819494351

The proceedings contain 25 papers. The topics discussed include: control issues and recent solutions for voltage controlled piezoelectric elements utilizing artificial neural networks;the 20th annual intelligent ground vehicle competition: building a generation of robotists;panoramic stereo sphere vision;loop closure detection using local Zernike moment patterns;stabilization and control of quad-rotor helicopter using a smartphone device;optimizing feature selection strategy for adaptive object identification in noisy environment;remotely controlling of mobile robots using gesture captured by the kinect and recognized by machine learning method;natural image understanding using algorithm selection and high-level feedback;natural image understanding using algorithm selection and high-level feedback;finger tracking for hand-held device interface using profile-matching stereo vision;and a restrained-torque-based motion instructor: forearm flexion/extension-driving exoskeleton.

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Finger Tracking for Hand-held Device Interface Using Profile-matching Stereo vision

Finger Tracking for Hand-held Device Interface Using Profile...

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Conference on intelligent robots and computer vision XXX - algorithms and techniques

作者： Chang, Yung-Ping Lee, Dah-Jye Moore, Jason Desai, Alok Tippetts, Beau Brigham Young Univ Dept Elect & Comp Engn Provo UT 84602 USA

ISBN: (纸本)9780819494351

Hundreds of millions of people use hand-held devices frequently and control them by touching the screen with their fingers. If this method of operation is being used by people who are driving, the probability of deaths and accidents occurring substantially increases. With a non-contact control interface, people do not need to touch the screen. As a result, people will not need to pay as much attention to their phones and thus drive more safely than they would otherwise. This interface can be achieved with real-time stereovision. A novel Intensity Profile Shape-Matching Algorithm is able to obtain 3-D information from a pair of stereo images in real time. While this algorithm does have a trade-off between accuracy and processing speed, the result of this algorithm proves the accuracy is sufficient for the practical use of recognizing human poses and finger movement tracking. By choosing an interval of disparity, an object at a certain distance range can be segmented. In other words, we detect the object by its distance to the cameras. The advantage of this profile shape-matching algorithm is that detection of correspondences relies on the shape of profile and not on intensity values, which are subjected to lighting variations. Based on the resulting 3-D information, the movement of fingers in space from a specific distance can be determined. Finger location and movement can then be analyzed for non-contact control of hand-held devices.

关键词： stereo vision hand-held device interface non-contact control finger tracking

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