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Seth Hutchinson

Other affiliations: Purdue University, Yale University, École Normale Supérieure  ...read more
Bio: Seth Hutchinson is an academic researcher from Georgia Institute of Technology. The author has contributed to research in topics: Motion planning & Robot. The author has an hindex of 40, co-authored 253 publications receiving 15229 citations. Previous affiliations of Seth Hutchinson include Purdue University & Yale University.


Papers
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Journal ArticleDOI
01 Oct 1996
TL;DR: This article provides a tutorial introduction to visual servo control of robotic manipulators by reviewing the prerequisite topics from robotics and computer vision, including a brief review of coordinate transformations, velocity representation, and a description of the geometric aspects of the image formation process.
Abstract: This article provides a tutorial introduction to visual servo control of robotic manipulators. Since the topic spans many disciplines our goal is limited to providing a basic conceptual framework. We begin by reviewing the prerequisite topics from robotics and computer vision, including a brief review of coordinate transformations, velocity representation, and a description of the geometric aspects of the image formation process. We then present a taxonomy of visual servo control systems. The two major classes of systems, position-based and image-based systems, are then discussed in detail. Since any visual servo system must be capable of tracking image features in a sequence of images, we also include an overview of feature-based and correlation-based methods for tracking. We conclude the tutorial with a number of observations on the current directions of the research field of visual servo control.

3,619 citations

Book
01 Jan 2006
TL;DR: In this paper, the Jacobian is used to describe the relationship between rigid motions and homogeneous transformations, and a linear algebraic approach is proposed for vision-based control of dynamical systems.
Abstract: Preface. 1. Introduction. 2. Rigid Motions and Homogeneous Transformations. 3. Forward and Inverse Kinematics. 4. Velocity Kinematics-The Jacobian. 5. Path and Trajectory Planning. 6. Independent Joint Control. 7. Dynamics. 8. Multivariable Control. 9. Force Control. 10. Geometric Nonlinear Control. 11. Computer Vision. 12. Vision-Based Control. Appendix A: Trigonometry. Appendix B: Linear Algebra. Appendix C: Dynamical Systems. Appendix D: Lyapunov Stability. Index.

3,100 citations

Journal ArticleDOI
30 Nov 2006
TL;DR: This paper is the first of a two-part series on the topic of visual servo control using computer vision data in the servo loop to control the motion of a robot using basic techniques that are by now well established in the field.
Abstract: This paper is the first of a two-part series on the topic of visual servo control using computer vision data in the servo loop to control the motion of a robot. In this paper, we describe the basic techniques that are by now well established in the field. We first give a general overview of the formulation of the visual servo control problem. We then describe the two archetypal visual servo control schemes: image-based and position-based visual servo control. Finally, we discuss performance and stability issues that pertain to these two schemes, motivating the second article in the series, in which we consider advanced techniques

2,026 citations

Journal ArticleDOI
TL;DR: This tutorial has only considered velocity controllers, which is convenient for most of classical robot arms and geometrical features coming from a classical perspective camera is considered.
Abstract: This article is the second of a two-part tutorial on visual servo control. In this tutorial, we have only considered velocity controllers. It is convenient for most of classical robot arms. However, the dynamics of the robot must of course be taken into account for high speed task, or when we deal with mobile nonholonomic or underactuated robots. As for the sensor, geometrical features coming from a classical perspective camera is considered. Features related to the image motion or coming from other vision sensors necessitate to revisit the modeling issues to select adequate visual features. Finally, fusing visual features with data coming from other sensors at the level of the control scheme will allow to address new research topics

894 citations

Journal ArticleDOI
01 Aug 2001
TL;DR: A partitioned approach to visual servo control is introduced that decouple the x-axis rotational and translational components of the control from the remaining degrees of freedom and incorporates a potential function that repels feature points from the boundary of the image plane.
Abstract: In image-based visual servo control, where control is effected with respect to the image, there is no direct control over the Cartesian velocities of the robot end effector. As a result, the robot executes trajectories that are desirable in the image, but which can be indirect and seemingly contorted in Cartesian space. We introduce a partitioned approach to visual servo control that overcomes this problem. In particular, we decouple the x-axis rotational and translational components of the control from the remaining degrees of freedom. Then, to guarantee that all features remain in the image throughout the entire trajectory, we incorporate a potential function that repels feature points from the boundary of the image plane. We illustrate our control scheme with a variety of results.

482 citations


Cited by
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MonographDOI
01 Jan 2006
TL;DR: This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms, into planning under differential constraints that arise when automating the motions of virtually any mechanical system.
Abstract: Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

6,340 citations

Journal ArticleDOI
TL;DR: A new approach toward target representation and localization, the central component in visual tracking of nonrigid objects, is proposed, which employs a metric derived from the Bhattacharyya coefficient as similarity measure, and uses the mean shift procedure to perform the optimization.
Abstract: A new approach toward target representation and localization, the central component in visual tracking of nonrigid objects, is proposed. The feature histogram-based target representations are regularized by spatial masking with an isotropic kernel. The masking induces spatially-smooth similarity functions suitable for gradient-based optimization, hence, the target localization problem can be formulated using the basin of attraction of the local maxima. We employ a metric derived from the Bhattacharyya coefficient as similarity measure, and use the mean shift procedure to perform the optimization. In the presented tracking examples, the new method successfully coped with camera motion, partial occlusions, clutter, and target scale variations. Integration with motion filters and data association techniques is also discussed. We describe only a few of the potential applications: exploitation of background information, Kalman tracking using motion models, and face tracking.

4,996 citations

Journal ArticleDOI
01 Aug 1996
TL;DR: Experimental results show that path planning can be done in a fraction of a second on a contemporary workstation (/spl ap/150 MIPS), after learning for relatively short periods of time (a few dozen seconds).
Abstract: A new motion planning method for robots in static workspaces is presented. This method proceeds in two phases: a learning phase and a query phase. In the learning phase, a probabilistic roadmap is constructed and stored as a graph whose nodes correspond to collision-free configurations and whose edges correspond to feasible paths between these configurations. These paths are computed using a simple and fast local planner. In the query phase, any given start and goal configurations of the robot are connected to two nodes of the roadmap; the roadmap is then searched for a path joining these two nodes. The method is general and easy to implement. It can be applied to virtually any type of holonomic robot. It requires selecting certain parameters (e.g., the duration of the learning phase) whose values depend on the scene, that is the robot and its workspace. But these values turn out to be relatively easy to choose, Increased efficiency can also be achieved by tailoring some components of the method (e.g., the local planner) to the considered robots. In this paper the method is applied to planar articulated robots with many degrees of freedom. Experimental results show that path planning can be done in a fraction of a second on a contemporary workstation (/spl ap/150 MIPS), after learning for relatively short periods of time (a few dozen seconds).

4,977 citations

01 Jan 2004
TL;DR: Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance and describes numerous important application areas such as image based rendering and digital libraries.
Abstract: From the Publisher: The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

3,627 citations

Journal ArticleDOI
01 Oct 1996
TL;DR: This article provides a tutorial introduction to visual servo control of robotic manipulators by reviewing the prerequisite topics from robotics and computer vision, including a brief review of coordinate transformations, velocity representation, and a description of the geometric aspects of the image formation process.
Abstract: This article provides a tutorial introduction to visual servo control of robotic manipulators. Since the topic spans many disciplines our goal is limited to providing a basic conceptual framework. We begin by reviewing the prerequisite topics from robotics and computer vision, including a brief review of coordinate transformations, velocity representation, and a description of the geometric aspects of the image formation process. We then present a taxonomy of visual servo control systems. The two major classes of systems, position-based and image-based systems, are then discussed in detail. Since any visual servo system must be capable of tracking image features in a sequence of images, we also include an overview of feature-based and correlation-based methods for tracking. We conclude the tutorial with a number of observations on the current directions of the research field of visual servo control.

3,619 citations