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

Level set methods have been widely used in image processing and computer vision. In conventional level set formulations, the level set function typically develops irregularities during its evolution, which may cause numerical errors and eventually destroy the stability of the evolution. Therefore, a numerical remedy, called reinitialization, is typically applied to periodically replace the degraded level set function with a signed distance function. However, the practice of reinitialization not only raises serious problems as when and how it should be performed, but also affects numerical accuracy in an undesirable way. This paper proposes a new variational level set formulation in which the regularity of the level set function is intrinsically maintained during the level set evolution. The level set evolution is derived as the gradient flow that minimizes an energy functional with a distance regularization term and an external energy that drives the motion of the zero level set toward desired locations. The distance regularization term is defined with a potential function such that the derived level set evolution has a unique forward-and-backward (FAB) diffusion effect, which is able to maintain a desired shape of the level set function, particularly a signed distance profile near the zero level set. This yields a new type of level set evolution called distance regularized level set evolution (DRLSE). The distance regularization effect eliminates the need for reinitialization and thereby avoids its induced numerical errors. In contrast to complicated implementations of conventional level set formulations, a simpler and more efficient finite difference scheme can be used to implement the DRLSE formulation. DRLSE also allows the use of more general and efficient initialization of the level set function. In its numerical implementation, relatively large time steps can be used in the finite difference scheme to reduce the number of iterations, while ensuring sufficient numerical accuracy. To demonstrate the effectiveness of the DRLSE formulation, we apply it to an edge-based active contour model for image segmentation, and provide a simple narrowband implementation to greatly reduce computational cost.

Distance Regularized Level Set Evolution and Its Application to Image Segmentation

This tutorial/survey paper: (1) provides a concise point of departure for researchers and practitioners alike wishing to assess the current state of the art in the control and monitoring of civil engineering structures; and (2) provides a link between structural control and other fields of control theory, pointing out both differences and similarities, and points out where future research and application efforts are likely to prove fruitful. The paper consists of the following sections: section 1 is an introduction; section 2 deals with passive energy dissipation; section 3 deals with active control; section 4 deals with hybrid and semiactive control systems; section 5 discusses sensors for structural control; section 6 deals with smart material systems; section 7 deals with health monitoring and damage detection; and section 8 deals with research needs. An extensive list of references is provided in the references section.

Structural control: past, present, and future

Most of the signal processing that we will study in this course involves local operations on a signal, namely transforming the signal by applying linear combinations of values in the neighborhood of each sample point. You are familiar with such operations from Calculus, namely, taking derivatives and you are also familiar with this from optics namely blurring a signal. We will be looking at sampled signals only. Let's start with a few basic examples. Local difference Suppose we have a 1D image and we take the local difference of intensities, DI(x) = 1 2 (I(x + 1) − I(x − 1)) which give a discrete approximation to a partial derivative. (We compute this for each x in the image.) What is the effect of such a transformation? One key idea is that such a derivative would be useful for marking positions where the intensity changes. Such a change is called an edge. It is important to detect edges in images because they often mark locations at which object properties change. These can include changes in illumination along a surface due to a shadow boundary, or a material (pigment) change, or a change in depth as when one object ends and another begins. The computational problem of finding intensity edges in images is called edge detection. We could look for positions at which DI(x) has a large negative or positive value. Large positive values indicate an edge that goes from low to high intensity, and large negative values indicate an edge that goes from high to low intensity. Example Suppose the image consists of a single (slightly sloped) edge:

http://ieeexplore.ieee.org/iel5/5/31307/01456462.pdf

Linear systems

Sobri&#233;t&#233; fonci&#232;re et soutien &#224; la vie &#233;conomique&#160;: entre ces deux n&#233;cessit&#233;s fondamentales, et en apparence antinomiques, l&#8217;entrep&#244;t n&#8217;est pas oblig&#233; de choisir. Producteur massif d&#8217;&#233;nergie d&#233;carbon&#233;e et peu co&#251;teuse, et bras arm&#233; de l&#8217;industrie, l&#8217;entrep&#244;t est par essence un b&#226;timent &#224; double fonction et &#224; double niveau. Depuis plusieurs ann&#233;es, et bien avant l&#8217;obligation de 30% de photovolta&#239;que inscrite dans la loi &#233;nergie-climat, l&#8217;entrep&#244;t est syst&#233;matiquement compos&#233;, en plus du niveau logistique, d&#8217;un second niveau de production d&#8217;&#233;lectricit&#233;, gr&#226;ce &#224; la solarisation de sa toiture et de ses ombri&#232;res de parking. La profession de l&#8217;immobilier logistique, repr&#233;sent&#233;e par l&#8217;association Afilog, a pris l&#8217;engagement d&#8217;aller au-del&#224; des 30% obligatoires de photovolta&#239;que en toiture pour arriver &#224; un taux de 50%. Ainsi, l&#8217;entrep&#244;t non seulement r&#233;duit son empreinte fonci&#232;re, mais il contribue &#224; l&#8217;effort national de production d&#8217;&#233;nergie, tout en repr&#233;sentant moins de 1% de l&#8217;artificialisation. 

L’entrepôt, un bâtiment à (au moins) deux étages

This technical communique aims at correcting an erroneous statement (Lemma 2.4) in an earlier paper by the same authors concerning a sufficient condition of uniform observability for a Linear Time-Varying (LTV) system. In this earlier paper, the proofs of two other lemmas, about body-pose estimation from range measurements, relied on this erroneous statement. For the sake of conciseness, only a new proof of one of these lemmas is presented, the proof of the second lemma being a simpler version of it.

Correction to: "Position estimation from direction or range measurements".

This paper addresses the problems of estimating the position of an object moving in $n(\geq\ 2)$ -dimensional Euclidean space using possibly biased velocity measurements and possibly biased range measurements of one or multiple source points. The proposed solutions exploit the Continuous Riccati Equation (CRE) to calculate observer gains yielding global uniform exponential stability of zero estimation errors. These results are obtained under persistent excitation (p.e.) conditions whose satisfaction i) depends on the number and relative positioning of source points and on the object motion properties, and ii) ensures uniform observability and good conditioning of the CRE solutions. In particular, these conditions can be satisfied in the case of a single source point and a moving object. Simulation results illustrate the performance of the proposed observers in this latter case.

Claude Samson

Papers

L’entrepôt, un bâtiment à (au moins) deux étages

Correction to: "Position estimation from direction or range measurements".

Riccati Observers for Position Estimation Using (Pseudo) Range and Biased Velocity Information