Motion generation for groups of robots: a centralized, geometric approach

We develop a method for generating smooth trajectories for a set of mobile robots. We show that, given two end configurations of the set of robots, by tuning one parameter, the user can choose an interpolating trajectory from a continuum of curves varying from that corresponding to maintaining a rigid formation to motion of the robots toward each other. The idea behind this method is to change the original constant kinetic energy metric in the configuration space and can be summarized into three steps. First, the energy of the motion as a rigid structure is decoupled from the energy of motion along directions that violate the rigid constraints. Second, the metric is shaped by assigning different weights to each term, and, third, geodesic flow is constructed for the modified metric. The optimal motions generated on the manifolds of rigid body displacements in 3-D space (SE(3)) or in plane (SE(2)) and the uniform rectilinear motion of each robot corresponding to a totally uncorrelated approach are particular cases of our general treatment

An SVD-Based Projection Method for Interpolation on \u3ci\u3eSE\u3c/i\u3e(3)

This paper develops a method for generating smooth trajectories for a moving rigid body with specified boundary conditions. Our method involves two key steps: 1) the generation of optimal trajectories in GA+(n), a subgroup of the affine group in Rn and 2) the projection of the trajectories onto SE(3), the Lie group of rigid body displacements. The overall procedure is invariant with respect to both the local coordinates on the manifold and the choice of the inertial frame. The benefits of the method are threefold. First, it is possible to apply any of the variety of well-known efficient techniques to generate optimal curves on GA+(n). Second, the method yields approximations to optimal solutions for general choices of Riemannian metrics on SE(3). Third, from a computational point of view, the method we propose is less expensive than traditional methods

Abstraction and Control for Groups of Robots

This paper addresses the general problem of controlling a large number of robots required to move as a group. We propose an abstraction based on the definition of a map from the configuration space Q of the robots to a lower dimensional manifold A, whose dimension is independent of the number of robots. In this paper, we focus on planar fully actuated robots. We require that the manifold has a product structure A = G x S, where G is a Lie group, which captures the position and orientation of the ensemble in the chosen world coordinate frame, and S is a shape manifold, which is an intrinsic characterization of the team describing the “shape” as the area spanned by the robots. We design decoupled controllers for the group and shape variables. We derive controllers for individual robots that guarantee the desired behavior on A. These controllers can be realized by feedback that depends only on the current state of the robot and the state of the manifold A. This has the practical advantage of reducing the communication and sensing that is required and limiting the complexity of individual robot controllers, even for large numbers of robots

MicroRNA-383 located in frequently deleted chromosomal locus 8p22 regulates CD44 in prostate cancer.

A major genomic alteration in prostate cancer (PCa) is frequent loss of chromosome (chr) 8p with a common region of loss of heterozygosity (LOH) at chr8p22 locus. Genomic studies implicate this locus in the initiation of clinically significant PCa and with progression to metastatic disease. However, the genes within this region have not been fully characterized to date. Here we demonstrate for the first time that a microRNA component of this region-miR-383-is frequently downregulated in prostate cancer, has a critical role in determining tumor-initiating potential and is involved in prostate cancer metastasis via direct regulation of CD44, a ubiquitous marker of PCa tumor-initiating cells (TICs)/stem cells. Expression analyses of miR-383 in PCa clinical tissues established that low miR-383 expression is associated with poor prognosis. Functional data suggest that miR-383 regulates PCa tumor-initiating/stem-like cells via CD44 regulation. Ectopic expression of miR-383 inhibited tumor-initiating capacity of CD44+ PCa cells. Also, 'anti-metastatic' effects of ectopic miR-383 expression were observed in a PCa experimental metastasis model. In view of our results, we propose that frequent loss of miR-383 at chr8p22 region leads to tumor initiation and prostate cancer metastasis. Thus, we have identified a novel finding that associates a long observed genomic alteration to PCa stemness and metastasis. Our data suggest that restoration of miR-383 expression may be an effective therapeutic modality against PCa. Importantly, we identified miR-383 as a novel PCa tissue diagnostic biomarker with a potential that outperforms that of serum PSA

Euclidean metrics for motion generation on \u3cem\u3eSE(3)\u3c/em\u3e

Previous approaches to trajectory generation for rigid bodies have been either based on the so-called invariant screw motions or on ad hoc decompositions into rotations and translations. This paper formulates the trajectory generation problem in the framework of Lie groups and Riemannian geometry. The goal is to determine optimal curves joining given points with appropriate boundary conditions on the Euclidean group. Since this results in a two-point boundary value problem that has to be solved iteratively, a computationally efficient, analytical method that generates near-optimal trajectories is derived. The method consists of two steps. The first step involves generating the optimal trajectory in an ambient space, while the second step is used to project this trajectory onto the Euclidean group. The paper describes the method, its applications and its performance in terms of optimality and efficiency

MicroRNA history : discovery, recent applications and next frontiers

We thank the Department of Scientific Publications at The University of Texas MD Anderson Cancer Center for English language editing of the manuscript.Since 1993, when the first small non-coding RNA was identified, our knowledge about microRNAs has grown exponentially. In this review, we focus on the main progress in this field and discuss the most important findings under a historical perspective. In addition, we examine microRNAs as markers ofdisease diagnosis and prognosis, and as new therapeutic targets.M.I.A is supported by a PhD fellowship (SFRH/BD/47031/2008) from FCT (Fundação para a Ciência e Tecnologia) from Portugal. G.A.C. is supported as a Fellow at The University of Texas MD Anderson Research Trust, as a Fellow of The University of Texas System Regents Research Scholar, and by the CLL Global Research Foundation. Work in Dr. Calin’s laboratory was supported in part by NIH, by DoD, by 2009 Seena Magowitz – Pancreatic Cancer Action Network – AACR Pilot Grant and by the U.S./European Alliance for the Therapy of CLL

MicroRNAs and metastases--the neuroblastoma link

[Excerpt] MicroRNAs (miRNAs) are small noncoding RNAs of approximately 22 nucleotides in length that regulate gene expression post-transcriptionally. These small RNAs are fundamental regulators of several cellular processes, such as differentiation, development, apoptosis, proliferation, cell cycle regulation and metabolism, through the binding to 3' untranslated regions, coding sequence or 5' untranslated regions of target messenger RNAs (mRNAs), preventing their translation or causing their degradation.1 A modest change in only one miRNA will affect multiple mRNA targets; consequently, the deregulation of miRNAs has important consequences to the cellular homeostatic stability, and aberrant miRNAs expression patterns have been described in several types of cancer.2 Recently, miRNAs have been implicated in the metastatic process of several tumors such as human breast and colorectal cancers3 and, as reported this issue of Cancer Biology & Therapy by Guo et al. in neuroblastoma.4 These are extracranial solid tumors, arising from neural crest cells, that are most common in infants and children; metastasis, the main cause of death, is present at the time of diagnosis in approximately 60% of patients. (5) [...

On Controlling Aircraft Formations

We describe a framework for controlling a group of unmanned aerial vehicles (UAVs) flying in close formation. We first present a nonlinear dynamical model which includes the induced rolling moment by the lead aircraft on the wing of the following aircraft. Then, we outline two methods for trajectory generation of the leading aircraft, based on interpolation techniques on the Euclidean group, SE(3). Two formation controllers that allow each aircraft to maintain its position and orientation with respect to neighboring UAVs are derived using input-output feedback linearization. Numerical simulations illustrate the application of these ideas and demonstrate the validity of the proposed framework