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.

Vision-based motion planning of a pneumatic robot using a topology representing neural network

Abstract: We present a new approach to integrate sensors into robot motion planning by combining the concept of the perceptual control manifold (PCM) and the topology representing network (TRN) algorithm. Motion planning should incorporate sensing due to the presence of uncertainty. Therefore, the PCM extends the notion of robot configuration space to include sensor space. Exploiting the topology preserving features of the TRN algorithm, the neural network learns a representation of the PCM. The learnt representation of the manifold is then used as a basis for motion planning with various constraints. The feasibility of this approach is demonstrated by experiments with a pneumatically driven robot arm (SoftArm).

Multimodal interaction for distributed collaboration

Abstract: We demonstrate a same-time different-place collaboration system for managing crisis situations using geospatial information. Our system enables distributed spatial decision-making by providing a multimodal interface to team members. Decision makers in front of large screen displays and/or desktop computers, and emergency responders in the field with tablet PCs can engage in collaborative activities for situation assessment and emergency response.

Robust probabilistic estimation of uncertain appearance for model-based tracking

Abstract: We present a method for appearance management for model-based visual tracking. The approach provides a concise mechanism for mapping image information to a model surface by taking into consideration uncertainties from the mapping process and by propagating appearance information from visible high-confidence surface regions to hidden or low-confidence regions. The method allows acquiring and utilizing appearance information online from available data, eliminating the need for offline appearance information. The problem is modeled with a continuous Markov random field (MRF) with clique potentials defined as energy functionals over color distributions with the Earth mover distance replacing traditional distance measures between random variables. A general MAP-MRF solution usable for general distributions as well as an efficient real-time capable linear close form approximation are presented.

A tracking framework for collaborative human computer interaction

Abstract: The ability to track multiple people and their body parts (i.e., face and hands) in a complex environment is crucial for designing a collaborative natural human computer interaction (HCI). One of the challenging issues in tracking body parts of people is the data association uncertainty while assigning measurements to the proper tracks in the case of occlusion and close interaction of body parts of different people. This paper describes a framework for tracking body parts of people in 2D/3D using multiple hypothesis tracking (MHT) algorithm. A path coherence function has been incorporated along with MHT to reduce the negative effects of closely spaced measurements that produce unconvincing tracks and needless computations. The performance of the framework has been validated using experiments on real sequence of images.

Role of active vision in optimizing visual feedback for robot control

Abstract: A purposeful change of camera parameters or “active vision” can be used to improve the process of extracting visual information. Thus if a robot visual servo loop incorporates active vision, it can lead to a better performance while increasing the scope of the control tasks. Although significant advances have been made in this direction, much of the potential improvement is still unrealized. This chapter discusses the advantages of using active vision for visual servoing. It reviews some of the past research in active vision relevant to visual servoing, with the aim of improving: (1) the measurement of image parameters, (2) the process of interpreting the image parameters in terms of the corresponding world parameters, and (3) the control of a robot in terms of the visual information extracted.

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.