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.

/pdf/a-survey-of-augmented-reality-17irtaxt7z.pdf

A survey of augmented reality

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.

Planning Algorithms: Introductory Material

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.

/pdf/a-tutorial-on-visual-servo-control-5cy6rje63g.pdf

A tutorial on visual servo control

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.

/pdf/robot-modeling-and-control-2n3memrp6g.pdf

Robot Modeling and Control

https://www.cs.cmu.edu/~illah/PAPERS/socialroboticssurvey.pdf

A survey of socially interactive robots

The major natural disasters that occurred in the last few months have shown the importance and necessity for collaborative, international crisis management. Current geoinformation technologies are potentially powerful tools for mitigation, preparation, response, and recovery tasks in crisis situations; however, they fail to support group work and have typically been designed without scientific understanding of how groups (or groups of groups) work in crisis management to collect, process, and use geospatial information. The GeoCollaborative Crisis Management (GCCM) project investigates how groups utilize geospatial technologies in crisis situations and uses findings to design novel, multimodal (speech and gesture-based), collaborative interfaces for geospatial technologies.

Project highlight: GeoCollaborative crisis management

Interactive visualization and augmentation of mechanical assembly sequences

The goal of integrating sensors into robot motion planning has incited recent research efforts. The Perceptual Control Manifold serves this goal extending the notion of the robot configuration space to include sensor space. In this paper, we develop a framework for sensor-based motion planning of robotic manipulators using the Topology Representing Network algorithm to develop a learned representation of the Perceptual Control Manifold. The topology preserving features of the neural network lend themselves to yield, after learning, a diffusion-based path planning strategy for flexible obstacle avoidance. We demonstrate the capabilities of topology preserving maps using an industrial robot simulator and a pneumatically driven robot arm (SoftArm).

/pdf/vision-based-robot-motion-planning-using-a-topology-3upa038kh7.pdf

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

Learning using independent component analysis (ICA) has found a wide range of applications in the area of computer vision and pattern analysis, ranging from face recognition to speech separation. This paper presents a non-parametric approach to the ICA problem that is robust towards outlier effects. The algorithm, for the first time in the field of ICA, adopts an intuitive and direct approach, focusing on the very definition of independence itself; i.e. the joint probability density function (pdf) of independent sources is factorial over the marginal distributions. In the proposed algorithm, kernel density estimation is employed to approximate the underlying distributions. There are two major advantages of our algorithm. First, existing algorithms focus on learning the independent components by attempting to fulfill necessary conditions (but not sufficient) for independence. For example, the Jade algorithm attempts to approximate independence by minimizing higher order statistics, which are not robust to outliers. Comparatively, our technique is inherently robust towards outlier effects. Second, since the learning employs kernel density estimation, it is naturally free from the assumptions of source distributions (unlike the Infomax algorithm). Experimental results show that the algorithm is able to perform separation of sources in the presence of outliers, whereas existing algorithms like Jade and Infomax break down under such conditions. The results have also shown that the proposed non-parametric approach is generally source distribution independent. In addition, it is able to separate non-Gaussian zero-kurtotic signals unlike the traditional ICA algorithms like Jade and Infomax.

A non-parametric approach for independent component analysis using kernel density estimation

The success of an autonomous assembly task relies on a motion planning system to generate a plan to accomplish the task, and on a sensor-based robot control system to ensure successful execution of the plan. The decoupling of sensor-based robot control from motion planning may yield undesirable motion plans that do not utilize the sensing effectively or consider sensor constraints. For example, a feasible collision-free path may be hard to traverse in practice because of inadequate sensor feedback in certain regions under uncertainty and sensor limitations. In this paper we consider an approach for motion planning that incorporates visual servoing constraints into the computation of the motion plans. The approach extends the notion of configuration space to include the corresponding sensor values. We propose a hierarchical representation of the high dimensional planning space involved, and a multistrategic heuristics search. This results in a practical motion planning scheme that is proven to be resolution-complete. Performance results are described for several robot manipulators with up to 6-DOF and under various sensing constraints.

https://ieeexplore.ieee.org/iel3/4861/13422/00615414.pdf

Rajeev Sharma

Papers

Project highlight: GeoCollaborative crisis management

Interactive visualization and augmentation of mechanical assembly sequences

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

A non-parametric approach for independent component analysis using kernel density estimation

Practical motion planning with visual constraints