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检索条件"任意字段=Intelligent Robots and Computer Vision XXIII: Algorithms, Techniques, and Active Vision"

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The 14^TH Annual intelligent Ground Vehicle Competition:: intelligent teams creating intelligent ground robots

The 14<SUP>TH</SUP> Annual Intelligent Ground Vehicle Compet...

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Conference on intelligent robots and computer vision XXIV - algorithms, techniques, and active vision

作者： Theisen, Bernard L. Nguyen, Dmitri US Army Tank Automot Res Dev & Engn Ctr 6501 E Eleven Mile Rd Warren MI 48397 USA

ISBN: (纸本)0819464821

The intelligent Ground Vehicle Competition (IGVC) is one of three, unmanned systems, student competitions that were founded by the Association for Unmanned Vehicle Systems International (AUVSI) in the 1990s. 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 14 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 50 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 three-day competition are highlighted. Finally, an assessment of the competition based on participant feedback is presented.

关键词： intelligent roboss autonomous systems ground vehicles engineering education IGVC

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A 2D CMAC neural net algorithm for a positioning system of automated agriculture vehicle

A 2D CMAC neural net algorithm for a positioning system of a...

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Conference on intelligent robots and computer vision XXIV - algorithms, techniques, and active vision

作者： Zhang, Fangming Ying, Yibin Zhejiang Univ Coll Biosyst Engn & Food Sci 268 Kaixuan Rd Hangzhou 310029 Zhejiang Peoples R China Kansas State Univ Dept Agr & Biol Engn Manhattan KS 66502 USA

ISBN: (纸本)0819464821

In a machine vision-based guidance system, a camera must be corrected precisely to calculate the position of vehicle, however, it is not easy to obtain the intrinsic and extrinsic parameters of the camera, while neural nets have the advantage to set up a mapping relationship for a nonlinear system. We intended to use the CMAC neural net to construct two map relationships: image coordinates and offsets of the vehicle, and image coordinates and the heading angle of the vehicle. The net inputs were the coordinates of top and bottom points in the detected guidance line in the image coordinate system. The outputs were offsets and heading angles. The verified results show that the RMS of inferred offset is 10.5 mm, and the STD is 11.3 mm;the RMS of inferred heading is 1.1 degrees, and the STD is 0.99 degrees.

关键词： CMAC neural net machine vision positioning system guidance

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Obstacle avoidance using predictive vision based on a dynamic 3D world model

Obstacle avoidance using predictive vision based on a dynami...

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Conference on intelligent robots and computer vision XXIV - algorithms, techniques, and active vision

作者： Benjamin, D. Paul Lyons, Damian Achtemichuk, Tom Pace Univ Dept Comp Sci 1 Pace Pl New York NY 10038 USA Fordham Univ Dept Comp & Informat Sci Bronx NY 10458 USA

ISBN: (纸本)0819464821

We have designed and implemented a fast predictive vision system for a mobile robot based on the principles of active vision. This vision system is part of a larger project to design a comprehensive cognitive architecture for mobile robotics. The vision system represents the robot's environment with a dynamic 3D world model based on a 3D gaming platform (Ogre3D). This world model contains a virtual copy of the robot and its environment, and outputs graphics showing what the virtual robot "sees" in the virtual world;this is what the real robot expects to see in the real world. The vision system compares this output in real time with the visual data. Any large discrepancies are flagged and sent to the robot's cognitive system, which constructs a plan for focusing on the discrepancies and resolving them, e.g. by updating the position of an object or by recognizing a new object. An object is recognized only once;thereafter its observed data are monitored for consistency with the predictions, greatly reducing the cost of scene understanding. We describe the implementation of this vision system and how the robot uses it to locate and avoid obstacles.

关键词： mobile robotics active vision cognitive architecture predictive vision

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A simple, inexpensive, and effective implementation of a vision-guided autonomous robot

A simple, inexpensive, and effective implementation of a vis...

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Conference on intelligent robots and computer vision XXIV - algorithms, techniques, and active vision

作者： Tippetts, Beau Lillywhite, Kirt Fowers, Spencer Dennis, Aaron Lee, Dah-Jye Archibald, James Brigham Young Univ Dept Elect & Comp Engn Robot Vis Lab Provo UT 84602 USA

ISBN: (纸本)0819464821

This paper discusses a simple, inexpensive, and effective implementation of a vision-guided autonomous robot. This implementation is a second year entrance for Brigham Young University students to the intelligent Ground Vehicle Competition. The objective of the robot was to navigate a course constructed of white boundary lines and orange obstacles for the autonomous competition. A used electric wheelchair was used as the robot base. The wheelchair was purchased from a local thrift store for $28. The base was modified to include Kegresse tracks using a friction drum system. This modification allowed the robot to perform better on a variety of terrains, resolving issues with last year's design. In order to control the wheelchair and retain the robust motor controls already on the wheelchair the wheelchair joystick was simply removed and replaced with a printed circuit board that emulated joystick operation and was capable of receiving commands through a serial port connection. Three different algorithms were implemented and compared: a purely reactive approach, a potential fields approach, and a machine learning approach. Each of the algorithms used color segmentation methods to interpret data from a digital camera in order to identify the features of the course. This paper will be useful to those interested in implementing an inexpensive vision-based autonomous robot.

关键词： IGVC autonomous robot design machine learning potential fields friction drum Kegresse

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A design approach for small vision-based autonomous vehicles

A design approach for small vision-based autonomous vehicles

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Conference on intelligent robots and computer vision XXIV - algorithms, techniques, and active vision

作者： Edwards, Barrett B. Fife, Wade S. Archibald, James K. Lee, Dah-Jye Wilde, Doran K. Brigham Young Univ Dept Elect & Comp Engn 435 Clyde Bldg Provo UT 84602 USA

ISBN: (纸本)0819464821

This paper describes the design of a small autonomous vehicle based on the Helios computing platform, a custom FPGA-based board capable of supporting on-board vision. Target applications for the Helios computing platform are those that require lightweight equipment and low power consumption. To demonstrate the capabilities of FPGAs in real-time control of autonomous vehicles, a 16 inch long R/C monster truck was outfitted with a Helios board. The platform provided by such a small vehicle is ideal for testing and development. The proof of concept application for this autonomous vehicle was a timed race through an environment with obstacles. Given the size restrictions of the vehicle and its operating environment, the only feasible on-board sensor is a small CMOS camera. The single video feed is therefore the only source of information from the surrounding enviromnent. The image is then segmented and processed by custom logic in the FPGA that also controls direction and speed of the vehicle based on visual input.

关键词： autonomous vehicle real-time vision FPGA centered design

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Autonomous robot navigation using vision and sensor based algorithm

Autonomous robot navigation using vision and sensor based al...

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Conference on intelligent robots and computer vision XXIV - algorithms, techniques, and active vision

作者： Bhandari, Susmita Mathis, Allison Mohiuddin, Kashif Pietrocola, David Restrepo, Maria Ahlgren, David J. Trinity Coll Dept Engn 300 Summit St Hartford CT 06106 USA

ISBN: (纸本)0819464821

ALVIN-VII is an autonomous vehicle designed to compete in the AUVSI intelligent Ground Vehicle Competition (IGVC). The competition consists of two events, the Autonomous Challenge and Navigation Challenge. Using tri-processor control architecture the information from sonar sensors, cameras, GPS and compass is effectively integrated to map out the path of the robot. In the Autonomous Challenge, the real time data from two Firewire web cameras and an array of four sonar sensors are plotted on a custom-defined polar grid to identify the position of the robot with respect to the obstacles in its path. Depending on the position of the obstacles in the grid, a state number is determined and a command of action is retrieved from the state table. The image processing algorithm comprises a series of steps involving plane extraction, morphological analysis, edge extraction and interpolation, all of which are statistically based allowing optimum operation at varying ambient conditions. In the Navigation Challenge, data from GPS and sonar sensors are integrated on a polar grid with flexible distance thresholds and a state table approach is used to drive the vehicle to the next waypoint while avoiding obstacles. Both algorithms are developed and implemented using National Instruments (NI) hardware and LabVIEW software. The task of collecting and processing information in real time can be time consuming and hence not reactive enough for moving robots. Using three controllers, the image processing is done separately for each camera while a third controller integrates the data received through an Ethernet connection.

关键词： autonomous robot image processing navigation sensors algorithm

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A method for sensor processing, sensor integration, and navigation in mobile autonomous ground vehicles

A method for sensor processing, sensor integration, and navi...

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Conference on intelligent robots and computer vision XXIV - algorithms, techniques, and active vision

作者： Riggins, Robert N. Mutter, Bruce V. Baker, Scott Farmer, Jesse Huntley, Joy Kessler, Joe Lewis, Lenny Bleufield State Coll CART Inc Ctr Appl Res & Technol POB 2182 Bluefield WV 24701 USA

ISBN: (纸本)0819464821

This paper presents an algorithm for solving three challenges of autonomous navigation: sensor signal processing, sensor integration, and path-finding. The algorithm organizes these challenges into three steps. The first step involves converting the raw data from each sensor to a form suitable for real-time processing. Emphasis in the first step is on image processing. In the second step, the processed data from all sensors is integrated into a single map. Using this map as input, during the third step the algorithm calculates a goal and finds a suitable path from robot to the goal. The method presented in this paper completes these steps in this order and the steps repeat indefinitely. The robotic platform designed for testing the algorithm is a six-wheel mid-wheel drive system using differential steering. The robot, called Anassa II, has an electric wheelchair base and a custom-built top and it is designed to participate in the intelligent Ground Vehicle Competition (IGVC). The sensors consist of a laser scanner, a video camera, a Differential Global Positioning System (DGPS) receiver, a digital compass, and two wheel encoders. Since many intelligent vehicles have similar sensors, the approach presented here is general enough for many types of autonomous mobile robots.

关键词： sensor integration autonomous ground robotic vehicles vision path-planning

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Moving target detection through omniorientational vision fixed on AGV

Moving target detection through omniorientational vision fix...

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Conference on intelligent robots and computer vision XXIV - algorithms, techniques, and active vision

作者： Yang Shu-Ying Cao Zuo-Liang He Pei-Lian Tianjin Univ Tianjin 300072 Peoples R China Tianjin Univ Technol Tianjin 300191 Peoples R China

ISBN: (纸本)0819464821

Extremely wide view of the onmi-vision performs highly advanced for the vehicle navigation and target detection. However moving targets detection through omni-vision fixed on AGV (Automatic Guided Vehicle) involves more complex environments, where both the targets and the vehicle are in the moving condition. The moving targets will be detected in a moving background. After analyzing the character on orriniorientational vision and image, we propose to use the estimation in optical flow fields, Gabor filter over optical flow fields for detecting moving objects. Because polar angle theta and polar radius R of polar coordinates are being changed as the targets moving, we improved optical flow approach which can be calculated based on the polar coordinates at the oniniorientational center. We constructed Gabor filter which has 24 orientations every 15', and filter optical flow fields at 24 orientations. By the contrast of the Gabor filter images at the same orientation and the same AGV position between the situation which there aren't any moving targets in the environment and the situation which there are some moving targets in the same environment, the moving targets' optical flow fields could be recognized. Experiment results show that the proposed approach is feasible and effective.

关键词： optical flow omniorientational vision Gabor filters

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Embodying a cognitive model in a mobile robot

Embodying a cognitive model in a mobile robot

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Conference on intelligent robots and computer vision XXIV - algorithms, techniques, and active vision

作者： Benjamin, D. Paul Lyons, Damian Lonsdale, Deryle Pace Univ Dept Comp Sci 1 Pace Plaza New York NY 10038 USA Fordham Univ Dept Comp & Informat Sci Bronx NY 10458 USA Brigham Young Univ Dept Linguist & English Language Provo UT 84602 USA

ISBN: (纸本)0819464821

The ADAPT project is a collaboration of researchers in robotics, linguistics and artificial intelligence at three universities to create a cognitive architecture specifically designed to be embodied in a mobile robot. There are major respects in which existing cognitive architectures are inadequate for robot cognition. In particular, they lack support for true concurrency and for active perception. ADAPT addresses these deficiencies by modeling the world as a network of concurrent schemas, and modeling perception as problem solving. Schemas are represented using the RS (Robot Schemas) language, and are activated by spreading activation. RS provides a powerful language for distributed control of concurrent processes. Also, The formal semantics of RS provides the basis for the semantics of ADAPT's use of natural language. We have implemented the RS language in Soar, a mature cognitive architecture originally developed at CMU and used at a number of universities and companies. Soar's subgoaling and learning capabilities enable ADAPT to manage the complexity of its environment and to learn new schemas from experience. We describe the issues faced in developing an embodied cognitive architecture, and our implementation choices.

关键词： mobile robotics cognitive architecture predictive vision learning robot schemas ADAPT

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Lane identification and path planning for autonomous mobile robots

Lane identification and path planning for autonomous mobile ...

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Conference on intelligent robots and computer vision XXIV - algorithms, techniques, and active vision

作者： McKeon, Robert T. Paulik, Mark Krishnan, Mohan Univ Detroit Mercy Dept Elect & Comp Engn Detroit MI 48221 USA

ISBN: (纸本)0819464821

This work has been performed in conjunction with the University of Detroit Mercy's (UDM) ECE Department autonomous vehicle entry in the 2006 intelligent Ground Vehicle Competition (***. The IGVC challenges engineering students to design autonomous vehicles and compete in a variety of unmanned mobility competitions. The course to be traversed in the competition consists of a lane demarcated by painted lines on grass with the possibility of one of the two lines being deliberately left out over segments of the course. The course also consists of other challenging artifacts such as sandpits, ramps, potholes, and colored tarps that alter the color composition of scenes, and obstacles set up using orange and white construction barrels. This paper describes a composite lane edge detection approach that uses three algorithms to implement noise filters enabling increased removal of noise prior to the application of image thresholding. The first algorithm uses a row-adaptive statistical filter to establish an intensity floor followed by a global threshold based on a reverse cumulative intensity histogram and a priori knowledge about lane thickness and separation. The second method first improves the contrast of the image by implementing an arithmetic combination of the blue plane (RGB format) and a modified saturation plane (HSI format). A global threshold is then applied based on the mean of the intensity image and a user-defined offset. The third method applies the horizontal component of the Sobel mask to a modified gray scale of the image, followed by a thresholding method similar to the one used in the second method. The Hough transform is applied to each of the resulting binary images to select the most probable line candidates. Finally, a heuristics-based confidence interval is determined, and the results sent on to a separate fuzzy polar-based navigation algorithm, which fuses the image data with that produced by a laser scanner (for obstacle detection).

关键词： image processing image filtering thresholding path planning autonomous navigation

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