Pedicle screw insertion: robotic assistance versus conventional C-arm fluoroscopy.

Bone-mounted robotic guidance for pedicle screw placement has been recently introduced, aiming at increasing accuracy. The aim of this prospective study was to compare this novel approach with the conventional fluoroscopy assisted freehand technique (not the two- or three-dimensional fluoroscopy-based navigation). Two groups were compared: 11 patients, constituting the robotical group, were instrumented with 64 pedicle screws; 23 other patients, constituting the fluoroscopic group, were also instrumented with 64 pedicle screws. Screw position was assessed by two independent observers on postoperative CT-scans using the Rampersaud A to D classification. No neurological complications were noted. Grade A (totally within pedicle margins) accounted for 79% of the screws in the robotically assisted and for 83% of the screws in the fluoroscopic group respectively (p = 0.8). Grade C and D screws, considered as misplacements, accounted for 4.7% of all robotically inserted screws and 7.8% of the fluoroscopically inserted screws (p = 0.71). The current study did not allow to state that robotically assisted screw placement supersedes the conventional fluoroscopy assisted technique, although the literature is more optimistic about the former.

Utilització del robot mòbil com a eina d'aprenentatge en ensenyaments tècnics

En aquest Projecte de Millora de la Qualitat Docent es descriu el disseny, la construcció i la utilització d’un robot mòbil com a eina docent en titulacions d’Enginyeria. El robot mòbil té com a element de control un PC portàtil convencional per tal de facilitar el procés d’aprenentatge de l’alumnat estigui centrat en l’objectiu de les pràctiques i no en el funcionament i control del robot. A més a més, el robot disposa d’un elevat nombre de sensors i actuadors per tal d’oferir un elevat grau d’interdisciplinaritat.

Estudi de comportaments socials d'aixams robòtics amb aplicació a la neteja d'espais no estructurats

La intel·ligència d’eixams és una branca de la intel·ligència artificial que està agafant molta força en els últims temps, especialment en el camp de la robòtica. En aquest projecte estudiarem el comportament social sorgit de les interaccions entre un nombre determinat de robots autònoms en el camp de la neteja de grans superfícies. Un cop triat un escenari i un robot que s’ajustin als requeriments del projecte, realitzarem una sèrie de simulacions a partir de diferents polítiques de cerca que ens permetran avaluar el comportament dels robots per unes condicions inicials de distribució dels robots i zones a netejar. A partir dels resultats obtinguts serem capaços de determinar quina configuració genera millors resultats.

Neues aus Diagnostik und Therapie der spinalen Erkrankungen [Novel aspects of diagnostics and therapy of spinal cord diseases].

BACKGROUND: Both non-traumatic and traumatic spinal cord injuries have in common that a relatively minor structural lesion can cause profound sensorimotor and autonomous dysfunction. Besides treating the cause of the spinal cord injury the main goal is to restore lost function as far as possible. AIM: This article provides an overview of current innovative diagnostic (imaging) and therapeutic approaches (neurorehabilitation and neuroregeneration) aiming for recovery of function after non-traumatic and traumatic spinal cord injuries. MATERIAL AND METHODS: An analysis of the current scientific literature regarding imaging, rehabilitation and rehabilitation strategies in spinal cord disease was carried out. RESULTS: Novel magnetic resonance imaging (MRI) based techniques (e.g. diffusion-weighted MRI and functional MRI) allow visualization of structural reorganization and specific neural activity in the spinal cord. Robotics-driven rehabilitative measures provide training of sensorimotor function in a targeted fashion, which can even be continued in the homecare setting. From a preclinical point of view, defined stem cell transplantation approaches allow for the first time robust structural repair of the injured spinal cord. CONCLUSION: Besides well-established neurological and functional scores, MRI techniques offer the unique opportunity to provide robust and reliable "biomarkers" for restorative therapeutic interventions. Function-oriented robotics-based rehabilitative interventions alone or in combination with stem cell based therapies represent promising approaches to achieve substantial functional recovery, which go beyond current rehabilitative treatment efforts.

Experimentació amb eina d'àudio i vídeo a la carta i arxiu multidocumental en entorn web

L'objectiu d'aquest pràcticum és treballar amb una eina d'edició i catalogació remota de vídeos accessible via internet. L'eina ha estat desenvolupada per l'empresa Vision Robotics. La memòria reflecteix les experiències viscudes a través del treball amb aquesta eina i analitza les possibilitats de millora, les potencialitats de l'eina a més d'afegir unes reflexions finals de l'eina en general.

An hybrid methodology for RL-based behavior coordination in a target following mission with an AUV

Proposes a behavior-based scheme for high-level control of autonomous underwater vehicles (AUVs). Two main characteristics can be highlighted in the control scheme. Behavior coordination is done through a hybrid methodology, which takes in advantages of the robustness and modularity in competitive approaches, as well as optimized trajectories

Hybrid coordination of reinforcement learning-based behaviors for AUV control

This paper proposes a hybrid coordination method for behavior-based control architectures. The hybrid method takes advantages of the robustness and modularity in competitive approaches as well as optimized trajectories in cooperative ones. This paper shows the feasibility of applying this hybrid method with a 3D-navigation to an autonomous underwater vehicle (AUV). The behaviors are learnt online by means of reinforcement learning. A continuous Q-learning implemented with a feed-forward neural network is employed. Realistic simulations were carried out. The results obtained show the good performance of the hybrid method on behavior coordination as well as the convergence of the behaviors

Efficient learning of reactive robot behaviors with a Neural-Q_learning approach

The purpose of this paper is to propose a Neural-Q_learning approach designed for online learning of simple and reactive robot behaviors. In this approach, the Q_function is generalized by a multi-layer neural network allowing the use of continuous states and actions. The algorithm uses a database of the most recent learning samples to accelerate and guarantee the convergence. Each Neural-Q_learning function represents an independent, reactive and adaptive behavior which maps sensorial states to robot control actions. A group of these behaviors constitutes a reactive control scheme designed to fulfill simple missions. The paper centers on the description of the Neural-Q_learning based behaviors showing their performance with an underwater robot in a target following task. Real experiments demonstrate the convergence and stability of the learning system, pointing out its suitability for online robot learning. Advantages and limitations are discussed

Semi-online neural-Q_leaming for real-time robot learning

Reinforcement learning (RL) is a very suitable technique for robot learning, as it can learn in unknown environments and in real-time computation. The main difficulties in adapting classic RL algorithms to robotic systems are the generalization problem and the correct observation of the Markovian state. This paper attempts to solve the generalization problem by proposing the semi-online neural-Q_learning algorithm (SONQL). The algorithm uses the classic Q_learning technique with two modifications. First, a neural network (NN) approximates the Q_function allowing the use of continuous states and actions. Second, a database of the most representative learning samples accelerates and stabilizes the convergence. The term semi-online is referred to the fact that the algorithm uses the current but also past learning samples. However, the algorithm is able to learn in real-time while the robot is interacting with the environment. The paper shows simulated results with the "mountain-car" benchmark and, also, real results with an underwater robot in a target following behavior

Towards Direct Policy Search Reinforcement Learning for Robot Control

This paper proposes a high-level reinforcement learning (RL) control system for solving the action selection problem of an autonomous robot. Although the dominant approach, when using RL, has been to apply value function based algorithms, the system here detailed is characterized by the use of direct policy search methods. Rather than approximating a value function, these methodologies approximate a policy using an independent function approximator with its own parameters, trying to maximize the future expected reward. The policy based algorithm presented in this paper is used for learning the internal state/action mapping of a behavior. In this preliminary work, we demonstrate its feasibility with simulated experiments using the underwater robot GARBI in a target reaching task

A system to evaluate the accuracy of a visual mosaicking methodology

When underwater vehicles navigate close to the ocean floor, computer vision techniques can be applied to obtain motion estimates. A complete system to create visual mosaics of the seabed is described in this paper. Unfortunately, the accuracy of the constructed mosaic is difficult to evaluate. The use of a laboratory setup to obtain an accurate error measurement is proposed. The system consists on a robot arm carrying a downward looking camera. A pattern formed by a white background and a matrix of black dots uniformly distributed along the surveyed scene is used to find the exact image registration parameters. When the robot executes a trajectory (simulating the motion of a submersible), an image sequence is acquired by the camera. The estimated motion computed from the encoders of the robot is refined by detecting, to subpixel accuracy, the black dots of the image sequence, and computing the 2D projective transform which relates two consecutive images. The pattern is then substituted by a poster of the sea floor and the trajectory is executed again, acquiring the image sequence used to test the accuracy of the mosaicking system

Correction of shading effects in vision-based UUV localization

This paper describes the improvements achieved in our mosaicking system to assist unmanned underwater vehicle navigation. A major advance has been attained in the processing of images of the ocean floor when light absorption effects are evident. Due to the absorption of natural light, underwater vehicles often require artificial light sources attached to them to provide the adequate illumination for processing underwater images. Unfortunately, these flashlights tend to illuminate the scene in a nonuniform fashion. In this paper a technique to correct non-uniform lighting is proposed. The acquired frames are compensated through a point-by-point division of the image by an estimation of the illumination field. Then, the gray-levels of the obtained image remapped to enhance image contrast. Experiments with real images are presented

Towards a real-time vision-based navigation system for a small-class UUV

This paper deals with the problem of navigation for an unmanned underwater vehicle (UUV) through image mosaicking. It represents a first step towards a real-time vision-based navigation system for a small-class low-cost UUV. We propose a navigation system composed by: (i) an image mosaicking module which provides velocity estimates; and (ii) an extended Kalman filter based on the hydrodynamic equation of motion, previously identified for this particular UUV. The obtained system is able to estimate the position and velocity of the robot. Moreover, it is able to deal with visual occlusions that usually appear when the sea bottom does not have enough visual features to solve the correspondence problem in a certain area of the trajectory

Probabilistic sonar scan matching for an AUV

This paper proposes MSISpIC, a probabilistic sonar scan matching algorithm for the localization of an autonomous underwater vehicle (AUV). The technique uses range scans gathered with a Mechanical Scanning Imaging Sonar (MSIS), the robot displacement estimated through dead-reckoning using a Doppler velocity log (DVL) and a motion reference unit (MRU). The proposed method is an extension of the pIC algorithm. An extended Kalman filter (EKF) is used to estimate the robot-path during the scan in order to reference all the range and bearing measurements as well as their uncertainty to a scan fixed frame before registering. The major contribution consists of experimentally proving that probabilistic sonar scan matching techniques have the potential to improve the DVL-based navigation. The algorithm has been tested on an AUV guided along a 600 m path within an abandoned marina underwater environment with satisfactory results

Proposal of a parallel architecture for a motion detection algorithm

This paper proposes a parallel architecture for estimation of the motion of an underwater robot. It is well known that image processing requires a huge amount of computation, mainly at low-level processing where the algorithms are dealing with a great number of data. In a motion estimation algorithm, correspondences between two images have to be solved at the low level. In the underwater imaging, normalised correlation can be a solution in the presence of non-uniform illumination. Due to its regular processing scheme, parallel implementation of the correspondence problem can be an adequate approach to reduce the computation time. Taking into consideration the complexity of the normalised correlation criteria, a new approach using parallel organisation of every processor from the architecture is proposed