Rajeev Sharma

Bio: Rajeev Sharma is an academic researcher from Pennsylvania State University. The author has contributed to research in topics: Gesture & Gesture recognition. The author has an hindex of 34, co-authored 107 publications receiving 5446 citations. Previous affiliations of Rajeev Sharma include University of Illinois at Urbana–Champaign.

Articulated models from video

Abstract: Past research on model-based tracking of articulated targets has neglected to address the problems of model-acquisition and initialization. However, for model-based approaches to ever become practical and autonomous, these important issues need to be addressed Towards this goal, this paper-presents a model-acquisition framework for acquiring articulated models directly from monocular video. Both structure, shape, and appearance of articulated models are estimated In addition, the initialization problem is solved by estimating pose information for at least one frame of a sequence, allowing subsequent model-based tracking. The presented work is based on basic assumptions and hence not restricted towards specific types of targets. It has in particular the ability to process human as well as non-human targets and makes no assumptions with respect to the structure of the kinematic tree or complexity. This work hence presents a set of systematic solutions to the problems of model-acquisition and initialization that bridge the gap between state of the art model-based tracking approaches and practical applications.

Robot motion planning in a changing, partially predictable environment

Abstract: Presents a framework for analyzing and determining robot motion plans for situations in which the robot is affected by an environment that probabilistically changes over time. In general, motion planning under uncertainty has received substantial interest, and in particular a changing-environment has been recognized as an important aspect of motion planing under uncertainty. The authors model the environment as a finite-state Markov process, and the robot executes a motion strategy that is conditioned on its current position and the state of the environment. Optimality of a robot strategy is evaluated in terms of a performance functional that depends on the environment, robot actions, and a precise encoding of relevant preferences. By using a simple, yet powerful computation technique that is based on dynamic programming, the authors can numerically compute optimal robot strategies for a wide class of problems, surpassing previous results in this context that were obtained analytically. Several computed motion planning examples are presented. >

On the observability of robot motion under active camera control

Abstract: Defines a measure of "observability" of robot motion that can be used in evaluating a hand/eye set-up with respect to the ease of achieving vision-based control. This extends the analysis of "manipulability" of a robotic mechanism in Yoshikawa (1983) to incorporate the effect of visual features. The authors discuss how the observability measure can be applied for active camera placement and for robot trajectory planning to improve the visual servo control. The authors use the examples of a planar 2-DOF arm and a PUMA-type 3-DOF arm to show the variation of the observability and manipulability measure with respect to the relative position of the active camera. >

Early detection of independent motion from active control of normal image flow patterns

Abstract: An important initial step in interpreting a dynamic scene is to detect moving objects in the environment. This paper presents a novel solution to the problem of early motion detection by a moving observer. The solution requires the observer to be active in the acquisition of images thereby controlling the optical flow pattern due to egomotion. A theoretical analysis is done based on geometric considerations to establish conditions that are necessary and sufficient to guarantee motion detection at a point. The detection problem is posed in terms of locally computable image quantities (the normal image flow) which this makes it implementable in real time. The performance of the technique can be improved by imposing any applicable constraint; this is demonstrated for the detection of the motions of "compact" objects satisfying a size bound. The goal is to design a flexible and efficient early motion detection strategy that can be tailored to the needs of a particular navigation system.

A survey of augmented reality

Abstract: This paper surveys the field of augmented reality AR, in which 3D virtual objects are integrated into a 3D real environment in real time. It describes the medical, manufacturing, visualization, path planning, entertainment, and military applications that have been explored. This paper describes the characteristics of augmented reality systems, including a detailed discussion of the tradeoffs between optical and video blending approaches. Registration and sensing errors are two of the biggest problems in building effective augmented reality systems, so this paper summarizes current efforts to overcome these problems. Future directions and areas requiring further research are discussed. This survey provides a starting point for anyone interested in researching or using augmented reality.

Planning Algorithms: Introductory Material

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.

A tutorial on visual servo control

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.

Robot Modeling and Control

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.