No abstract

Many aspects influence the economic sustainability of a software architecture, such as modularization, technology choices, and design decisions facilitating evolutionary changes. Relevant information is spread across many artifacts and the software architects. An approach to sustainability measurement focusing on a single artifact or perspective is likely to neglect important factors. At ABB, we are measuring and tracking the architecture sustainability of a large-scale industrial control system under development. We have applied a multi-perspective approach called MORPHOSIS. It focuses on requirements, architecture design, and source code. It includes evolution scenario analysis, architecture compliance checks, and tracking of architecture-level code metrics. This article reports our experiences from tracking the selected sustainability measures for two years.

Abstract-In this paper we show that, despite some disadvantageous properties of radio frequency identification (RFID), it is possible to localize a mobile robot quite accurately in environments which are densely tagged. We therefore employ a recently presented probabilistic fingerprinting technique called RFID snapshots. This method interprets short series of RFID measurements as feature vectors and is able to position a mobile robot after a training phase. It requires no explicit sensor model and is capable of exploiting given tag infrastructures, e.g., provided by supermarket shelves containing labeled products.I. INTRODUCTION Radio frequency identification (RFID) has found its way into robotics because of advantageous properties such as the unique identification of RFID tags and the resulting unambiguous association of sensor readings with landmarks. Moreover, since RFID reader and tags (also called transponders) communicate via electromagnetic waves, line of sight is not required between them. On the other hand, especially passive ultra-high frequency (UHF, 868/915 MHz) RFID imposes some difficulties: First, RFID readers of this type only report which tags have been detected; they neither know the distance nor the bearing to a detected tag (and do not even report signal strength information). Second, the successful detection of a tag within the read range of up to 7 m depends largely on factors such as the relative position of the tag with respect to the reader antenna as well as on the materials of nearby objects. Hence, today it is widely assumed that the use of RFID for self-localization is disadvantageous due to sensor noise and potentially low positioning accuracy.In this paper, however, we show that it is possible to localize a mobile robot via RFID and odometry alone at an accuracy of less than 0.3 m. We present empirical results derived from extensive experiments with a mobile robot and off-the-shelf RFID hardware. Our approach is based on a fingerprinting technique called RFID snapshots, which we presented recently [8]. In this method, short series of RFID measurements are interpreted as feature vectors. They can be trained at reference positions during an offline phase and later be used to estimate the pose of the robot corresponding to current tag detections. A particle filter is employed in order to improve accuracy and robustness. The advantages of our approach are that no explicit sensor model is required and that environment characteristics are implicitly learned by the

Summary. In this paper, we present a method of learning a probabilistic RFID reader model with a mobile robot in a semi-automatic fashion. RFID and position data, recorded during an exploration phase, are used to learn the probability of detecting an RFID tag, for which we investigate two non-parametric probability density estimation techniques. The trained model is finally used to localize the robot via a particle filter-based approach and optimized with respect to the resulting localization error. Experiments have shown that the learned models perform comparably well as a grid-based model learned from measurements in a stationary setup, but can be obtained easier.

Abstract-We present a novel approach which enables a mobile robot to estimate its trajectory in an unknown environment with long-range passive radio-frequency identification (RFID). The estimation is based only on odometry and RFID measurements. The technique requires no prior observation model and makes no assumptions on the RFID setup. In particular, it is adaptive to the power level, the way the RFID antennas are mounted on the robot, and environmental characteristics, which have major impact on long-range RFID measurements. Tag positions need not be known in advance, and only the arbitrary, given infrastructure of RFID tags in the environment is utilized. By a series of experiments with a mobile robot, we show that trajectory estimation is achieved accurately and robustly.