This paper presents a rehabilitation wearable exoskeleton for wrist joint with two degrees of freedom (DOF), flexion-extension and adduction-abduction (radial and ulnar deviation), actuated with Shape Memory Alloy (SMA) based actuators. Thanks to this type of actuators, the proposed device presents a very light weight and noiseless operation, in comparison with similar devices. The preliminary results obtained over real tests with the wrist exoskeleton are presented. This prototype demonstrates that SMA actuator technology is a viable alternative when investigating possible improvement of rehabilitation robotic devices in terms of weight, size and cost.

A new evolutionary-based algorithm is proposed to solve the robot manipulator optimal path generation problem. The following scenario is considered: given a learnt joint path describing a robot manipulator simple task in the Cartesian space, an optimal path is calculated when a different initial joint configuration is considered. The optimization problem is formulated as the minimization of both the end-effector pose error and the total joint displacement so as to ensure convergence towards the learnt path and a smooth joint motion. To solve the optimization problem an algorithm based on an evolutionary method called differential evolution (DE) is used. DE is a stochastic direct search optimization method based on the evolution of a candidate solution population in an iterative process of mutation, recombination, and selection. Since the algorithm does not require the use of the Jacobian matrix during the kinematic inversion, singularities problems are overcome. Results on the optimal path generation of a six degrees of freedom robot manipulator are also presented.

In this paper, a new methodology to build compact local maps in real time for outdoor robot navigation is presented. The environment information is obtained from a 3-D scanner laser. The navigation model, which is called traversable region model, is based on a Voronoi diagram technique, but adapted to large outdoor environments. The model obtained with this methodology allows a definition of safe trajectories that depend on the robot's capabilities and the terrain properties, and it will represent, in a topogeometric way, the environment as local and global maps. The application presented is validated in real outdoor environments with the robot called GOLIAT.

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

This paper presents a new path planning method based in the inverse of the logarithm of the distance transform and in the fast marching method. The distance transform of an image gives a grey scale that is darker near the obstacles and walls and more clear far from them and it is calculated via Voronoi diagram. The logarithm of the inverse of the distance transform imitates the repulsive electric potential from walls and obstacles. 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 robotic service field, safety navigation in human environments is one of the most important goals. To navigate without collision, some kind of environment representation is needed. This paper presents a new methodology to build, in real-time, compact local maps for navigation in complex indoor environments. The map is called Traversable Regions Model (TRM) and is based on Voronoi diagram techniques. The new approach is the application of Voronoi techniques in 3D environments and the previous study of tr avers ability in indoor environments. The model built allows to define safe trajectories depending on the robot's capabilities and the terrain properties, because a previous traversability study is performed with the 3D data. The methodology presented is validated in real indoor environments with the mobile manipulator MANFRED, developed in our lab.

Simultaneous localization and mapping (SLAM) is one of the fundamental problems in mobile robots. In this paper we present a new SLAM algorithm in an unknown indoor environment. A local Voronoi diagram (LVD) is built from a laser scanner measurements, in order to estimate the robot position using its topological and geometrical characteristics. The proposed algorithm introduces a backward step, which revises all previous poses. This step is fundamental to obtain good results in large and cyclic indoor environments. Genetic algorithms are used in order to improve the estimated approach.

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 robot's workspace. The result from this process is used to estimate the robot's position in the map or to correct the robot's odometry. Experiments with real data are presented to validate this algorithm.

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.

To navigate in complex environments, an autonomous mobile robot needs to reach a compromise between the need for reacting to unexpected events and the need for having efficient and optimized trajectories. Sensor based path planning can be used to achieve this goal. We present a new sensor based method which consist of building up local Voronoi diagrams using measurements from a scanning laser. The space is divided into regular cells, and the Euclidean distance is calculated between each cell and the objects. The cells which are equidistant to several objects belong to the Voronoi diagram. Experimental results obtained by running this algorithm are also presented. These results show that our method can be used in sensor based path planning.