Dolores Blanco

Laser treatments on skin enhancing and controlling transdermal delivery of 5-fluorouracil.

Laser ablation of stratum corneum (SC) enhances transdermal delivery of hydrophilic drugs. The influence of the infrared (IR) (λ = 1,064 nm), visible (λ = 532 nm), and ultraviolet (UV) (λ = 355 nm) radiations of a Nd:YAG laser on transdermal delivery of 5‐Fluorouracil (5‐Fu) across skin was studied in vitro.

Active human-mobile manipulator cooperation through intention recognition

A human-mobile manipulator cooperation module is designed to support a target task consisting of the transportation of a rigid object between a mobile manipulator and a master human worker. Our approach introduces an intention recognition capability in the robot, based on the search for spectral patterns in the force signal measured at the arm gripper. The mobile manipulator takes advantage of this capability by generating its own motion plans in order to collaborate in the execution of the task. This has been designated as active cooperation.

Voronoi diagram and fast marching applied to path planning

This paper presents a new path planning method which operates in two steps. In the first step the safest areas in the environment are extracted by means of a Voronoi diagram. In the second step fast marching method is applied to the Voronoi extracted areas in order to obtain the shortest path. In this way the trajectory obtained is the shortest between the safe possible ones. This two step method combines speed and reliability, because the map dimensions is reduced to a unidimensional map and this map represents the safest areas in the environment for moving the robot

FM 2: A REAL-TIME SENSOR-BASED FEEDBACK CONTROLLER FOR MOBILE ROBOTS

The Fast Marching based algorithm proposed here solves the problem of finding Feedback Control Laws for mobile robots, including nonholonomic vehicles. It integrates in a single Real Time Controller the global motion planning tasks and the collision avoidance capabilities required to efficiently move a mobile robot in a dynamic environment. The solution proposed is fast enough to be used in real-time and to operate with a laser scanner system at the sensor rate frequency. The method combines map-based and sensor-based planning operations to provide a smooth and reliable motion plan. The method works in two steps: In the first, it uses a Fast Marching technique to propagate a wave from the walls and obstacles to determine a potential of slowness for the robot. In the second step, this slowness map is used as refractive index, to calculate the potential of the propagation of a wave from the robot pose to the goal with time as the last axis. The generated trajectory corresponds to the path of the light ray through a medium with non-homogeneous refraction index. The robot trajectory is calculated on the vector field associated to the potential surface. The computational efficiency of the method allows the planner to operate at high rate sensor frequencies. For small and medium scale environments, the proposed method avoids the need for a collision avoidance algorithms plus a global motion planner. Since the method works over a smooth vector field, it allows the simple introduction of nonholonomic constraints. This way, the method can be used directly to develop a control scheme for nonholonomic vehicles, for example for car like

Exploration of a cluttered environment using Voronoi Transform and Fast Marching

Abstract The Extended Voronoi Transform and the Fast Marching Method combination provide potential maps for robot navigation in previously unexplored dynamic environments. The Extended Voronoi Transform of a binary image of the environment gives a grey scale that is darker near the obstacles and walls and lighter far from them. The Logarithm of the Extended Voronoi Transform imitates the repulsive electric potential from walls and obstacles. The method proposed, called Voronoi Fast Marching method, uses a Fast Marching technique on the Extended Voronoi Transform of the environment’s image, provided by sensors, to determine a motion plan. The computational efficiency of the method lets the planner operate at high rate sensor frequencies. This avoids the need for collision avoidance algorithms. The robot is directed towards the most unexplored and free zones of the environment so as to be able to explore all the workspace. This method is very fast and reliable and the trajectories are similar to the human trajectories: smooth and not very close to obstacles and walls. In this article we propose its application to the task of exploring unknown environments.

Sensor-based global planning for mobile robot navigation

The proposed algorithm integrates in a single planner the global motion planning and local obstacle avoidance capabilities. It efficiently guides the robot in a dynamic environment. This eliminates some of the traditional problems of planned architectures (model-plan-act scheme) while obtaining many of the qualities of behavior-based architectures. The computational efficiency of the method allows the planner to operate at high-rate sensor frequencies. This avoids the need for using both a collision-avoidance algorithm and a global motion planner for navigation in a cluttered environment. The method combines map-based and sensor-based planning operations to provide a smooth and reliable motion plan. Operating on a simple grid-based world model, the method uses a fast marching technique to determine a motion plan on a Voronoi extended transform extracted from the environment model. In addition to this real-time response ability, the method produces smooth and safe robot trajectories.

Symbolic Place Recognition in Voronoi-Based Maps by Using Hidden Markov Models

This article presents a new algorithm to recognize natural distinctive places such as corridors, halls, narrowings, corridors with doors opening on the left side, etc., from indoor environments using Hidden Markov Models (HMM). HMM give a stochastic solution which can be used to make decisions on localization, navigation and path-planning. The environment is modeled as a topo-geometric map which combines topological and geometric information. This map is obtained from a Voronoi diagram using measurements of a laser telemeter. The characteristics of topo-geometric map (nodes, number of edges adjacent to nodes, slope of edges, etc.) are used to learn and to recognize the different places typical of indoor environments. This map can be used in order to resolve several problems in robotics such as localization, navigation and path-planning. Our method of place recognition is a fast and effective way for a robot to recognize typical places of indoor environments from a topo-geometric map.

Exploration and Mapping Using the VFM Motion Planner

Efficient mapping of unknown environments is a fundamental function for mobile robot intelligence. To do so requires good exploration strategies and solving the simultaneous localization and mapping problem. The approach presented in this paper is an integration of our solutions into the problems of exploration and map building with a single robot. The exploration algorithm is based on the Voronoi fast marching (VFM) method to determine a motion plan toward the most unexplored and free zones of the environment. One consistent global map of the workspace is created using the simultaneous localization and modeling (SLAM) algorithm based on a nonlinear evolutive filter called the evolutive localization filter. The combination of the extended Voronoi transform and the fast marching method in the VFM method provides potential maps for robot navigation in previously unexplored dynamic environments. The logarithm of the extended Voronoi transform imitates the repulsive electric potential from walls and obstacles. The method uses a fast marching technique to determine a motion plan. A new strategy such that the robot determines the zones that it must explore in an autonomous way is described. As the robot carries out the exploration, it constructs a consistent map of the environment using the SLAM algorithm.

Localization by Voronoi diagrams correlation

Sensor-based localization is one of the fundamental problems in mobile robots. We present a technique for online robot localization in an a priori known indoor environment. Our approach uses the local Voronoi diagram, generated from a laser range scan, to match it against the global Voronoi diagram of the robots workspace. The result from this process is used to estimate the robots position in the map or to correct the robots odometry. Experiments with real data are presented to validate this algorithm.

Differential evolution solution to the SLAM problem

A new solution to the Simultaneous Localization and Modelling problem is presented in this paper. The algorithm is based on the stochastic search for solutions in the state space to the global localization problem by means of a differential evolution algorithm. This non linear evolutive filter, called Evolutive Localization Filter (ELF), searches stochastically along the state space for the best robot pose estimate. The set of pose solutions (the population) focuses on the most likely areas according to the perception and up to date motion information. The population evolves using the log-likelihood of each candidate pose according to the observation and the motion errors derived from the comparison between observed and predicted data obtained from the probabilistic perception and motion model. The proposed SLAM algorithm operates in two steps: in the first step the ELF filter is used at local level to re-localize the robot based on the robot odometry, the laser scan at a given position and a local map where only a low number of the last scans have been integrated. In the second step, the aligned laser measures and the corrected robot poses are used to detect whether the robot is revisiting a previously crossed area (i.e., a cycle in the robot trajectory exists). Once a cycle is detected, the Evolutive Localization Filter is used again to estimate the accumulated residual drift in the detected loop and then to re-estimate the robot poses in order to integrate the sensor measures in the global map of the environment. The algorithm has been tested in different environments to demonstrate the effectiveness, robustness and computational efficiency of the proposed approach.