The Software Product Line approach undertakes the development of complete portfolios of software products as a single, coherent development task. Although there are well-documented examples of cost reduction, shorter development times, and quality improvement achieved by introducing the product line paradigm in industry, the approach is not always the best economic choice for building a family of related systems. To support decision makers, a number of economic models have been proposed. Apparently, existing proposals are rather heterogeneous in terms of their main characteristics and goals. Nevertheless, this paper shows how most models may be defined using a small common lexicon. Translating models to such common lexicon, we compare them in detail, identifying their strengths and weaknesses. As a result, the paper proposes an integrated model that improves the cost estimation accuracy of existing models.

Software Product Lines (SPLs) are families of related software systems that provide different combinations of features. Extensive research and application attest to the significant economical and technological benefits of employing SPL practices. Variability models represent the feature combinations and the properties of the software products of SPLs. Kconfig configuration files from open source projects have been proposed as large and realistic case studies for research on analysis of variability models. However, despite the extensive work on this subject, the seemingly direct approach of translating Kconfig to Binary Decision Diagrams (BDDs) — a representation that has shown significant advantages for complex analysis of variability models — has not been explored. To address this gap, we propose a simple translation scheme which we evaluate with five case studies. Our encouraging results indicate the feasibility of our approach and open up avenues for further research.

Variability models play a key role in software product line engineering as they are used to represent the common and variable features that products may include, and what constraints among the features must be satisfied to guarantee the validity of the products. Valuable analysis operations on variability models can be performed by black box reusing logic engines, such as SAT-solvers and binary decision diagram libraries. Unfortunately, such kind of reuse implies long computation times for operations that need calling the engines many times. To overcome this problem, we propose new algorithms that directly deal with the data structure of a binary decision diagram encoding a variability model. In particular, our algorithms are specifically designed to detect core & dead features, and the impact & exclusion sets of every feature.

A software product line is an engineering approach to efficient development of software product portfolios. Key to the success of the approach is to identify the common and variable features of the products and the interdependencies between them, which are usually modeled using feature models. Implicitly, such models also include valuable information that can be used by economic models to estimate the payoffs of a product line. Unfortunately, as product lines grow, analyzing large feature models manually becomes impracticable. This paper proposes an algorithm to compute the total number of products that a feature model represents and, for each feature, the number of products that implement it. The inference of both parameters is helpful to describe the standardization/parameterization balance of a product line, detect scope flaws, assess the product line incremental development, and improve the accuracy of economic models. The paper reports experimental evidence that our algorithm has better runtime performance than existing alternative approaches.

Manipulator robots working in changing environments need special path planners that provide good solutions in short times. In this work we describe a sample-based path planning algorithm that is suitable for such environments. Our approach is based on two points. Firstly, unlike most sampling techniques, we have defined a non-random local sampling which reduces the computational time greatly. Secondly, a set of interpolated walks is obtained through cubic splines which guarantee the smoothness and continuity of the walks. This path planning algorithm is part of a full intelligent manipulation system based on 3D vision

This paper shows how to extract information about the parts and their layout in a complex scene when a single range image is available. In the worst case, the complexity of the scene includes: no shape-restrictions, shades, occlusion, cluttering, contact, surfaces viewed in oblique angles and without textures. This work is a prerequisite before carrying out further robot interaction actions in the scene. The process is based on a novel 3D range data segmentation technique that avoids most restrictions imposed on other techniques. Making use of the 3D segmented parts, the method carries out an objects silhouette classification which allows us to perform a layout graph of the objects in the scene. A brief description of this method and experimental results are presented throughout the paper.

We study a 3D visual servoing system composed of a robot manipulator, a CCD camera mounted on the end effector of the robot and a specific hardware. The use of computer vision as feedback transducer strongly affects the closed loop dynamics of the overall system, so that a visual controller is required for achieving fast response and high control accuracy. Due to the long time delay to generate the control signal, it is necessary to carefully select the visual controller. The objective of this work is to present a well structured design of efficient controllers to keep a desired relative 3D position between the object and the camera. Also an experimental setup has been built and used to evaluate the performance of the position based dynamic look and move system. Several results relative to different types of controller are presented. These results demonstrate the possibility of getting competitive results in real time performance with respect to more closed solutions shown in other works, while maintaining the advantage of easy system adaptability.

Presents a modeling strategy (half modelling wave, HMW) for the situation when only partial information of the 3D object surface is available. The method is applicable to unstructured range data. It means that no previous knowledge of the sensor used or the rough position of the object with respect to the sensor is required to compute the model. The developed partial modeling method has been conceived as a tool to be applied in new solutions for conventional 3D computer vision problems. Main contributions concerning partial viewing and 3D occlusion are also presented.

Presents a solution to the problem of determining the orientation of arbitrarily located 3D parts. The proposed algorithm is based on a new data structure specifically designed to store spherical representation models of 3D objects. This data structure called modeling wave set (MWS), allows keeping redundant information about local and global features of an object under a unique framework. It involves a different approach to deal with the modeling process providing new methods for solving generic problems like object recognition or pose determination. The algorithm described in this work is based on the fact that a rotation of a solid can be seen as a change in the wave used to model it, because of the invariance to spatial transformation of the MWS structure. This property makes an algorithm simpler and with lower computational cost than any other method based on spherical representations. The algorithm has been tested over a wide set of polyhedral and free-from shapes. Success ratio and 3D orienting errors obtained with the proposed algorithm can be considered as acceptable.

A new method for controlling single-link flexible manipulators is proposed. The objective is to control the tip position of a flexible manipulator in the presence of changes in its payload. Particularly, a new adaptive control system has been designed. The proposed control system requires calculating only one parameter, the tip load, instead of all the transfer functions as in conventional adaptive control methods. The tip load identification algorithm is based on the equivalence existing between a manipulator with payload and a manipulator submitted to the action of external forces. Moreover, this equivalence provides a new technique for modeling these systems which simplifies the methods usually applied. This results in a simple control law that needs minimal computing effort and, thus, can be used for real-time control of flexible arms.