Is early manual preference in infants defined by intermanual performance asymmetry in reaching?

In the present study we evaluated the relationship between manual preference and intermanual performance asymmetry in reaching of 5-month-old infants. Manual preference was assessed through frequency of reaches toward toys presented at midline, left or right in egocentric coordinates. Intermanual performance asymmetry was evaluated through kinematic analysis. Results showed that performance was predominantly symmetric between hands. Lateral toy positions induced predominance of ipsilateral reaching, while the midline position led to equivalent distribution between right and left handed reaches. No significant correlation between manual preference and intermanual performance asymmetry was observed. These results converge against the notion that manual preference derives from a genetically determined advantage of movement control favoring the right hand. (C) 2012 Elsevier Inc. All rights reserved.

Generierung von Konstruktionsmerkmalen zur Entwicklung eines bio-inspirierten Kommissionier- Greifers für die Intralogistik mittels Clusteranalyse

Kurzbeschreibung: In der Automatisierung von intralogistischen Kommissioniervorgängen ist ein großes Zukunftspotential erkennbar. Elementarer Bestandteil des Automatisierungsprozesses ist der Einsatz von Industrierobotern, die mit einem geeigneten Endeffektor, dem Greifer, ausgestattet werden müssen. Die Robotik ist in der Lage schneller, präziser und ausdauernder als die menschlichen Kommissionierer zu arbeiten und trägt damit entscheidend zur Effizienzsteigerung bei. Eine wesentliche Herausforderung für diesen Entwicklungsschritt zur Substitution der manuellen Kommissionierung ist die Konstruktion und Bereitstellung eines geeigneten Greifsystems. Am Lehrstuhl für Maschinenelemente und Technische Logistik der Helmut-Schmidt-Universität wurde mit der Erfahrung aus einem vorangegangenen Forschungsprojekt die Methode der Clusteranalyse erstmalig zur Untersuchung von Greifobjekten zur Entwicklung eines bionischen Universalgreifers für die Kommissionierung von Drogerieartikeln verwendet. Diese Abhandlung beschreibt einen Beitrag zur Entwicklung dieses Greifers am Beispiel handelsüblicher Drogerieartikel, die aktuell manuell kommissioniert werden. Diese werden hinsichtlich der für das Greifen relevanten Objektmerkmale geclustert und die daraus resultierenden Erkenntnisse in Form von Konstruktionsmerkmalen abgeleitet. Nach einer Analyse und Festlegung der greifrelevanten Merkmale der Greifobjekte wird eine Objektdatenbasis erstellt. Mit Hilfe geeigneter Methoden wird die gewonnene Datenbasis aufbereitet und reduziert. Im Anschluss werden die Greifobjekte bzw. deren Merkmalsausprägungen einer hierarchischen Clusteranalyse unterzogen. Hierbei werden die Grenzen der gebildeten Cluster mittels der zugehörigen Greifobjekte festgelegt und analysiert. Abschließend werden bestimmte greiferspezifische Merkmale auf die Anwendbarkeit in den Clustern überprüft und bewertet. Diese Betrachtungen ermöglichen es, dass spezielle Anforderungen an den Greifer, die direkt aus den Eigenschaften der Greifobjekte herrühren, zuverlässig erkannt und konstruktiv berücksichtigt werden können.

Scene Mapping and Understanding by Robotic Vision

The first mechanical Automaton concept was found in a Chinese text written in the 3rd century BC, while Computer Vision was born in the late 1960s. Therefore, visual perception applied to machines (i.e. the Machine Vision) is a young and exciting alliance. When robots came in, the new field of Robotic Vision was born, and these terms began to be erroneously interchanged. In short, we can say that Machine Vision is an engineering domain, which concern the industrial use of Vision. The Robotic Vision, instead, is a research field that tries to incorporate robotics aspects in computer vision algorithms. Visual Servoing, for example, is one of the problems that cannot be solved by computer vision only. Accordingly, a large part of this work deals with boosting popular Computer Vision techniques by exploiting robotics: e.g. the use of kinematics to localize a vision sensor, mounted as the robot end-effector. The remainder of this work is dedicated to the counterparty, i.e. the use of computer vision to solve real robotic problems like grasping objects or navigate avoiding obstacles. Will be presented a brief survey about mapping data structures most widely used in robotics along with SkiMap, a novel sparse data structure created both for robotic mapping and as a general purpose 3D spatial index. Thus, several approaches to implement Object Detection and Manipulation, by exploiting the aforementioned mapping strategies, will be proposed, along with a completely new Machine Teaching facility in order to simply the training procedure of modern Deep Learning networks.

Development of gripping devices based on electroadhesive forces

In a context of technological innovation, the aim of this thesis is to develop a technology that has gained interest in both scientific and industrial realms. This technology serves as a viable alternative to outdated and energy-consuming industrial systems. Electro-adhesive devices (EADs) leverage electrostatic forces for grasping objects or adhering to surfaces. The advantage of employing electrostatics lies in its adaptability to various materials without compromising the structure or chemistry of the object or surface. These benefits have led the industry to explore this technology as a replacement for costly vacuum systems and suction cups currently used for handling most products. Furthermore, the broad applicability of this technology extends to extreme environments, such as space with ultra-high vacuum conditions. Unfortunately, research in this area has yet to yield practical results for industrially effective gripper prototyping. This is primarily due to the inherent complexity of electro-adhesive technology, which operates on basic capacitive principles that does not find satisfying physical descriptions. This thesis aims to address these challenges through a series of studies, starting with the manufacturing process and testing of an EAD that has become the standard in our laboratory. It then delves into material and electrode geometry studies to enhance system performance, ultimately presenting potential industrial applications of the technology. All the presented results are encouraging, as they have yielded shear force values three times higher than those previously reported in the literature. The various applications have demonstrated the significant effectiveness of EADs as brakes or, more broadly, in exerting shear forces. This opens up the possibility of utilizing cutting-edge technologies to push the boundaries of technology to the fullest.

Contact force transitions in regrasp tasks of planar objects

This paper presents a simple and fast solution to the problem of finding the time variations of the forces that keep the object equilibrium when a finger is removed from a three contact point grasp or a finger is added to a two contact point grasp, assuming the existence of an external perturbation force (that can be the object weight itself). The procedure returns force set points for the control system of a manipulator device in a regrasping action. The approach was implemented and a numerical example is included in the paper to illustrate how it works.

Generating Compliant Motion of Objects with an Articulated Hand

The flexibility of the robot is the key to its success as a viable aid to production. Flexibility of a robot can be explained in two directions. The first is to increase the physical generality of the robot such that it can be easily reconfigured to handle a wide variety of tasks. The second direction is to increase the ability of the robot to interact with its environment such that tasks can still be successfully completed in the presence of uncertainties. The use of articulated hands are capable of adapting to a wide variety of grasp shapes, hence reducing the need for special tooling. The availability of low mass, high bandwidth points close to the manipulated object also offers significant improvements I the control of fine motions. This thesis provides a framework for using articulated hands to perform local manipulation of objects. N particular, it addresses the issues in effecting compliant motions of objects in Cartesian space. The Stanford/JPL hand is used as an example to illustrate a number of concepts. The examples provide a unified methodology for controlling articulated hands grasping with point contacts. We also present a high-level hand programming system based on the methodologies developed in this thesis. Compliant motion of grasped objects and dexterous manipulations can be easily described in the LISP-based hand programming language.

Passive and Active Grasping with a Prehensile Robot End-Effector

This report presents a design of a new type of robot end-effector with inherent mechanical grasping capabilities. Concentrating on designing an end-effector to grasp a simple class of objects, cylindrical, allowed a design with only one degree of actuation. The key features of this design are high bandwidth response to forces, passive grasping capabilities, ease of control, and ability to wrap around objects with simple geometries providing form closure. A prototype of this mechanism was built to evaluate these features.

Analysis and Implementation of Robust Grasping Behaviors

This thesis addresses the problem of developing automatic grasping capabilities for robotic hands. Using a 2-jointed and a 4-jointed nmodel of the hand, we establish the geometric conditions necessary for achieving form closure grasps of cylindrical objects. We then define and show how to construct the grasping pre-image for quasi-static (friction dominated) and zero-G (inertia dominated) motions for sensorless and sensor-driven grasps with and without arm motions. While the approach does not rely on detailed modeling, it is computationally inexpensive, reliable, and easy to implement. Example behaviors were successfully implemented on the Salisbury hand and on a planar 2-fingered, 4 degree-of-freedom hand.

Structure learning of graphical models for task-oriented robot grasping

In the collective imaginaries a robot is a human like machine as any androids in science fiction. However the type of robots that you will encounter most frequently are machinery that do work that is too dangerous, boring or onerous. Most of the robots in the world are of this type. They can be found in auto, medical, manufacturing and space industries. Therefore a robot is a system that contains sensors, control systems, manipulators, power supplies and software all working together to perform a task. The development and use of such a system is an active area of research and one of the main problems is the development of interaction skills with the surrounding environment, which include the ability to grasp objects. To perform this task the robot needs to sense the environment and acquire the object informations, physical attributes that may influence a grasp. Humans can solve this grasping problem easily due to their past experiences, that is why many researchers are approaching it from a machine learning perspective finding grasp of an object using information of already known objects. But humans can select the best grasp amongst a vast repertoire not only considering the physical attributes of the object to grasp but even to obtain a certain effect. This is why in our case the study in the area of robot manipulation is focused on grasping and integrating symbolic tasks with data gained through sensors. The learning model is based on Bayesian Network to encode the statistical dependencies between the data collected by the sensors and the symbolic task. This data representation has several advantages. It allows to take into account the uncertainty of the real world, allowing to deal with sensor noise, encodes notion of causality and provides an unified network for learning. Since the network is actually implemented and based on the human expert knowledge, it is very interesting to implement an automated method to learn the structure as in the future more tasks and object features can be introduced and a complex network design based only on human expert knowledge can become unreliable. Since structure learning algorithms presents some weaknesses, the goal of this thesis is to analyze real data used in the network modeled by the human expert, implement a feasible structure learning approach and compare the results with the network designed by the expert in order to possibly enhance it.

3D Visual Data-Driven Spatiotemporal Deformations for Non-Rigid Object Grasping Using Robot Hands

Sensing techniques are important for solving problems of uncertainty inherent to intelligent grasping tasks. The main goal here is to present a visual sensing system based on range imaging technology for robot manipulation of non-rigid objects. Our proposal provides a suitable visual perception system of complex grasping tasks to support a robot controller when other sensor systems, such as tactile and force, are not able to obtain useful data relevant to the grasping manipulation task. In particular, a new visual approach based on RGBD data was implemented to help a robot controller carry out intelligent manipulation tasks with flexible objects. The proposed method supervises the interaction between the grasped object and the robot hand in order to avoid poor contact between the fingertips and an object when there is neither force nor pressure data. This new approach is also used to measure changes to the shape of an object’s surfaces and so allows us to find deformations caused by inappropriate pressure being applied by the hand’s fingers. Test was carried out for grasping tasks involving several flexible household objects with a multi-fingered robot hand working in real time. Our approach generates pulses from the deformation detection method and sends an event message to the robot controller when surface deformation is detected. In comparison with other methods, the obtained results reveal that our visual pipeline does not use deformations models of objects and materials, as well as the approach works well both planar and 3D household objects in real time. In addition, our method does not depend on the pose of the robot hand because the location of the reference system is computed from a recognition process of a pattern located place at the robot forearm. The presented experiments demonstrate that the proposed method accomplishes a good monitoring of grasping task with several objects and different grasping configurations in indoor environments.

Robotic Sensing and Manipulation of Deformable Linear Objects with Learning-based methods

Nowadays robotic applications are widespread and most of the manipulation tasks are efficiently solved. However, Deformable-Objects (DOs) still represent a huge limitation for robots. The main difficulty in DOs manipulation is dealing with the shape and dynamics uncertainties, which prevents the use of model-based approaches (since they are excessively computationally complex) and makes sensory data difficult to interpret. This thesis reports the research activities aimed to address some applications in robotic manipulation and sensing of Deformable-Linear-Objects (DLOs), with particular focus to electric wires. In all the works, a significant effort was made in the study of an effective strategy for analyzing sensory signals with various machine learning algorithms. In the former part of the document, the main focus concerns the wire terminals, i.e. detection, grasping, and insertion. First, a pipeline that integrates vision and tactile sensing is developed, then further improvements are proposed for each module. A novel procedure is proposed to gather and label massive amounts of training images for object detection with minimal human intervention. Together with this strategy, we extend a generic object detector based on Convolutional-Neural-Networks for orientation prediction. The insertion task is also extended by developing a closed-loop control capable to guide the insertion of a longer and curved segment of wire through a hole, where the contact forces are estimated by means of a Recurrent-Neural-Network. In the latter part of the thesis, the interest shifts to the DLO shape. Robotic reshaping of a DLO is addressed by means of a sequence of pick-and-place primitives, while a decision making process driven by visual data learns the optimal grasping locations exploiting Deep Q-learning and finds the best releasing point. The success of the solution leverages on a reliable interpretation of the DLO shape. For this reason, further developments are made on the visual segmentation.

Trajectory planning of single and dual-arm robots for time-optimal handling of liquids and objects

This Thesis studies the optimal control problem of single-arm and dual-arm serial robots to achieve the time-optimal handling of liquids and objects. The first topic deals with the planning of time-optimal anti-sloshing trajectories of an industrial robot carrying a cylindrical container filled with a liquid, considering 1-dimensional and 2-dimensional planar motions. A technique for the estimation of the sloshing height is presented, together with its extension to 3-dimensional motions. An experimental validation campaign is provided and discussed to assess the thoroughness of such a technique. As far as anti-sloshing trajectories are concerned, 2-dimensional paths are considered and, for each one of them, three constrained optimizations with different values of the sloshing-height thresholds are solved. Experimental results are presented to compare optimized and non-optimized motions. The second part focuses on the time-optimal trajectory planning for dual-arm object handling, employing two collaborative robots (cobots) and adopting an admittance-control strategy. The chosen manipulation approach, known as cooperative grasping, is based on unilateral contact between the cobots and the object, and it may lead to slipping during motion if an internal prestress along the contact-normal direction is not prescribed. Thus, a virtual penetration is considered, aimed at generating the necessary internal prestress. The stability of cooperative grasping is ensured as long as the exerted forces on the object remain inside the static-friction cone. Constrained-optimization problems are solved for 3-dimensional paths: the virtual penetration is chosen among the control inputs of the problem and friction-cone conditions are treated as inequality constraints. Also in this case experiments are presented in order to prove evidence of the firm handling of the object, even for fast motions.

PARALLAXES OF SOUTHERN EXTREMELY COOL OBJECTS. I. TARGETS, PROPER MOTIONS, AND FIRST RESULTS

We present results from the PARallaxes of Southern Extremely Cool objects ( PARSEC) program, an observational program begun in 2007 April to determine parallaxes for 122 L and 28 T southern hemisphere dwarfs using the Wide Field Imager on the ESO 2.2 m telescope. The results presented here include parallaxes of 10 targets from observations over 18 months and a first version proper motion catalog. The proper motions were obtained by combining PARSEC observations astrometrically reduced with respect to the Second US Naval Observatory CCD Astrograph Catalog, and the Two Micron All Sky Survey Point Source Catalog. The resulting median proper motion precision is 5 mas yr(-1) for 195,700 sources. The 140 0.3 deg(2) fields sample the southern hemisphere in an unbiased fashion with the exception of the galactic plane due to the small number of targets in that region. The proper motion distributions are shown to be statistically well behaved. External comparisons are also fully consistent. We will continue to update this catalog until the end of the program, and we plan to improve it including also observations from the GSC2.3 database. We present preliminary parallaxes with a 4.2 mas median precision for 10 brown dwarfs, two of which are within 10 pc. These increase the present number of L dwarfs by 20% with published parallaxes. Of the 10 targets, seven have been previously discussed in the literature: two were thought to be binary, but the PARSEC observations show them to be single; one has been confirmed as a binary companion and another has been found to be part of a binary system, both of which will make good benchmark systems. These results confirm that the foreseen precision of PARSEC can be achieved and that the large field of view will allow us to identify wide binary systems. Observations for the PARSEC program will end in early 2011 providing three to four years of coverage for all targets. The main expected outputs are: more than a 100% increase in the number of L dwarfs with parallaxes, increment in the number of objects per spectral subclass up to L9-in conjunction with published results-to at least 10, and to put sensible limits on the general binary fraction of brown dwarfs. We aim to contribute significantly to the understanding of the faint end of the H-R diagram and of the L/T transition region.

Collaborative e-learning and Learning Objects

The Learning Object (OA) is any digital resource that can be reused to support learning with specific functions and objectives. The OA specifications are commonly offered in SCORM model without considering activities in groups. This deficiency was overcome by the solution presented in this paper. This work specified OA for e-learning activities in groups based on SCORM model. This solution allows the creation of dynamic objects which include content and software resources for the collaborative learning processes. That results in a generalization of the OA definition, and in a contribution with e-learning specifications.

Denisiuk-type reflection holography display with sillenite crystals for imaging and interferometry of small objects

A novel setup for imaging and interferometry through reflection holography with Bi12TiPO20(BTO) sillenite photorefractive crystals is proposed. A variation of the lensless Denisiuk arrangement was developed resulting in a compact, robust and simple interferometer. A red He-Ne laser was used as light source and the holographic recording occurred by diffusion with the grating vector parallel to the crystal [0 0 1]-axis. In order to enhance the holographic image quality and reduce noise a polarizing beam splitter (PBS) was positioned at the BTO input and the crystal was tilted around the [0 0 1]-axis. This enabled the orthogonally polarized transmission and diffracted beams to be separated by the PBS, providing the holographic image only. The possibility of performing deformation and strain analysis as well as vibration measurement of small objects was demonstrated. (C) 2007 Elsevier B.V. All rights reserved.