David Álvarez

Molecular epidemiology, antimicrobial susceptibilities and resistance mechanisms of Streptococcus pyogenes isolates resistant to erythromycin and tetracycline in Spain (1994–2006)

BackgroundGroup A Streptococcus (GAS) causes human diseases ranging in severity from uncomplicated pharyngitis to life-threatening necrotizing fasciitis and shows high rates of macrolide resistance in several countries. Our goal is to identify antimicrobial resistance in Spanish GAS isolates collected between 1994 and 2006 and to determine the molecular epidemiology (emm/T typing and PFGE) and resistance mechanisms of those resistant to erythromycin and tetracycline.ResultsTwo hundred ninety-five out of 898 isolates (32.8%) were erythromycin resistant, with the predominance of emm 4T4, emm 75T25, and emm 28T28, accounting the 67.1% of the 21 emm/T types. Spread of emm 4T4, emm 75T25 and emm 28T28 resistant clones caused high rates of macrolide resistance. The distribution of the phenotypes was M (76.9%), cMLSB (20.3%), iMLSB (2.7%) with the involvement of the erythromycin resistance genes mef(A) (89.5%), msr(D) (81.7%), erm(B) (37.3%) and erm(A) (35.9%).Sixty-one isolates were tetracycline resistant, with the main representation of the emm 77T28 among 20 emm/T types. To note, the combination of tet(M) and tet(O) tetracycline resistance genes were similar to tet(M) alone reaching values close to 40%. Resistance to both antibiotics was detected in 19 isolates of 7 emm/T types, being emm 11T11 and the cMLSB phenotype the most frequent ones. erm(B) and tet(M) were present in almost all the strains, while erm(A), mef(A), msr(D) and tet(O) appeared in less than half of them.ConclusionsSpanish GAS were highly resistant to macrolides meanwhile showed minor resistance rate to tetracycline. A remarkable correlation between antimicrobial resistance and emm/T type was noticed. Clonal spread of emm 4T4, emm 75T25 and emm 28T28 was the main responsable for macrolide resistance where as that emm 77T28 clones were it to tetraclycline resistance. A wide variety of macrolide resistance genes were responsible for three macrolide resistance phenotypes.

Kinesthetic teaching via Fast Marching Square

This paper presents a novel robotic learning technique based on Fast Marching Square (FM2). This method, which we have called FM Learning, is based on incorporating previous experience to the path planning system of the robot by taking into account paths taught to the robot via kinesthetic teaching, this is, guiding manually the robot through the desired path. The method proposed ensures that the path planning is always a globally asymptotically stable system at the target point, considering the motion as a nonlinear autonomous dynamical system. The few parameters the algorithm has can be tuned to get different behaviours of the learning system. The method has been evaluated through a set of simulations and also tested in the mobile manipulator Manfred V2.

3D robot formations planning with Fast Marching Square

This research presents a novel approach for 3D robot formation motion planning. The methodology presented is based on the standard Fast Marching Square (FM2) path planning method and its application to robot formations motion planning. For the formation coordination, a leader-followers scheme is used, which means that the reference pose for the follower robots is defined by geometric equations that place the goal pose of each follower as a function of the leaders pose. The use of the Frenet-Serret frame in order to control the orientation of the formation is introduced. Thanks to the combination of these methods, the configuration of the formation is able to adapt its shape depending on the environment conditions. This adaptation is based on the velocities map calculated in the first step of the FM2 algorithm. Also, robot priorities within the formations are introduced. This is an important contribution since it provides different behaviours to the formation members in special situations. Using this information, simulations show that the method is able to achieve good performance in difficult environments.

3D Robot Formations Path Planning with Fast Marching Square

This work presents a path planning algorithm for 3D robot formations based on the standard Fast Marching Square (FM2) path planning method. This method is enlarged in order to apply it to robot formations motion planning. The algorithm is based on a leader-followers scheme, which means that the reference pose for the follower robots is defined by geometric equations that place the goal pose of each follower as a function of the leader’s pose. Besides, the Frenet-Serret frame is used to control the orientation of the formation. The algorithm presented allows the formation to adapt its shape so that the obstacles are avoided. Additionally, an approach to model mobile obstacles in a 3D environment is described. This model modifies the information used by the FM2 algorithm in favour of the robots to be able to avoid obstacles. The shape deformation scheme allows to easily change the behaviour of the formation. Finally, simulations are performed in different scenarios and a quantitative analysis of the results has been carried out. The tests show that the proposed shape deformation method, in combination with the FM2 path planner, is robust enough to manage autonomous movements through an indoor 3D environment.

Nonholonomic Motion Planning Using the Fast Marching Square Method

This research presents two novel approaches to nonholonomic motion planning. The methodologies presented are based on the standard fast marching square path planning method and its application to car-like robots. Under the first method, the environment is considered as a three-dimensional C-space, with the first two dimensions given by the position of the robot and the third dimension by its orientation. This means that we operate over the configuration space instead of the bi-dimensional environment map. Moreover, the trajectory is computed along the C-space taking into account the dimensions of the vehicle, and thus guaranteeing the absence of collisions. The second method uses the standard fast marching square, and takes advantage of the vector field of the velocities computed during the first step of the method in order to adapt the motion plan to the control inputs that a car-like robot is able to execute. Both methods ensure the smoothness and safety of the calculated paths in addition to providing the control actions to perform the trajectory.

Precision grasp planning with Gifu Hand III based on fast marching square

This paper presents a novel methodology for planning the movements of a robotic hand when a precision grasp is going to be performed. The approach used is based on the standard Fast Marching Square (FM2) path planning method and its application to robot formations motion planning. In this case, the hand is considered to be a kinematic chain in which a mobile robot is located at every joint position. The robot formation is therefore deformable among the positions allowed by the mechanical limits of the joints. To perform a given precision grasp, the task is divided into two phases. First the hand approaches the object. FM2 is used to calculate a fast and smooth path towards the object to be grasped. While the hand is covering it, the formation updates its shape according to the map of velocities calculated in FM2. The second phase consists on performing the precision grasp. Every finger is modelled as a robot formation and a path is calculated for each fingertip so that they reach the grasping points on the object. The position of the joints of the fingers is computed using an inverse kinematics algorithm. Simulations show the usefulness of this approach thanks to a good performance of the approaching and the grasping tasks.

Adaptive evolving strategy for dextrous robotic manipulation

A robot task can be represented as a set of trajectories conformed by a sequence of poses. In this way it is possible to teach a mobile robot to accomplish a manipulation task, and also to reproduce it. Nevertheless robot navigation may normally introduce inaccuracies in localization due to natural events as wheel-slides, causing a mismatch between the end-effector and the objects or tools the robot is supposed to interact with. We propose an algorithm for adapting manipulation trajectories for different locations. The adaptation is achieved by optimizing in position, orientation and energy consumption. The approach is built over the basis of Evolution Strategies, and only uses forward kinematics permitting to avoid all the inconveniences that inverse kinematics imply, as well as convergence problems in singular kinematic configurations. Manipulation paths generated with this algorithm can achieve optimal performance, sometimes even improving original path smoothness. Experimental results are presented to verify the algorithm.

Path Planning for Mars Rovers Using the Fast Marching Method

This paper presents the application of the Fast Marching Method, with or without an external vectorial field, to the path planning problem of robots in difficult outdoors environments. The resulting trajectory has to take into account the obstacles, the slope of the terrain (gradient of the height), the roughness (spherical variance) and the type of terrain (presence of sand) that can lead to slidings. When the robot is in sandy terrain with a certain slope, there is a landslide (usually small) that can be modelled as a lateral current or vectorial field in the direction of the negative gradient. Besides, the method calculates a weight matrix W that represents difficulty for the robot to move in certain terrain and is built based on the information extracted from the surface characteristics. Then, the Fast Marching Method is applied with matrix W being a velocities map. Finally, the algorithm has been modified to incorporate the effect of an external vectorial field.

Improving Sampling-Based Path Planning Methods with Fast Marching

Sampling-based path planning algorithms are well-known because they are able to find a path in a very short period of time, even in high-dimensional spaces. However, they are non-smooth, random paths far away from the optimum. In this paper we introduce a novel improving technique based on the Fast Marching Method which improves in a deterministic, non-iterative way the initial path provided by a sampling-based methods. Simulation results show that the computation time of the proposed method is low and that path length and smoothness are improved.

Fast Marching-based globally stable motion learning

In this paper, a novel motion learning method is introduced: Fast Marching Learning (FML). While other learning methods are focused on optimising probabilistic functions or fitting dynamical systems, the proposed method consists on the modification of the Fast Marching Square (FM