This paper presents optimization techniques for implementing software-based redundancy in industrial control devices. Initially, a brief survey of software-based state replication techniques with a special focus on their applicability in industrial control devices is conducted. The scalability, predictability and low latency of the technique are of particular interest in this case. Based on this survey, an analytic evaluation of different implementation alternatives is performed. As part of this analysis, a novel state replication algorithm is introduced. The approach uses support from the compiler or runtime environment to detect changes in the application state with very low overhead. This information is used to replicate the state of an automation controller in a redundant setup. Lastly, experimental results using a prototype implementation of the presented technique demonstrate that the proposed novel approach is able to perform state replication with constant overhead.

In this paper we present Verification-Aided Regression Testing (VART), a novel extension of regression testing that uses model checking to increase the fault revealing capability of existing test suites. The key idea in VART is to extend the use of test case executions from the conventional direct fault discovery to the generation of behavioral properties specific to the upgrade, by (i) automatically producing properties that are proved to hold for the base version of a program, (ii) automatically identifying and checking on the upgraded program only the properties that, according to the developers’ intention, must be preserved by the upgrade, and (iii) reporting the faults and the corresponding counter-examples that are not revealed by the regression tests. Our empirical study on both open source and industrial software systems shows that VART automatically produces properties that increase the effectiveness of testing by automatically detecting faults unnoticed by the existing regression test suites.

In companies with a large portfolio of software or software-intensive products, functional overlaps are often perceived between independent products. In such situations it is advisable to systematically analyze the potential of systematic reuse and Software Product Lines. To this end, several domain analysis approaches, e.g., SEI Technical Probe, have been proposed to decide whether a set of products with a perceived functional overlap should be integrated into a single product line. Based on the principles of those approaches we devised our own approach. One important property is the inherent flexibility of the method to be able to apply it to four different application cases in industrial software products at ABB. In this paper we present our refined approach for domain analysis. The results and lessons learned are meant to support industrial researchers and practitioners alike. Moreover, the lessons learned highlight real-world findings concerning software reuse.

In a large corporate organization there are sometimes similar software products in certain subdomains with a perceived functional overlap. This promises to be an opportunity for systematic reuse to reduce software development and maintenance costs. In such situations companies have used different domain analysis approaches (e.g., SEI Technical Probe) that helped to assess technical and organizational potential for a software product line approach. We applied existing domain analysis approaches for software product line engineering and tailored them to include a feature analysis as well as architecture evaluation. In this paper, we report our experiences from applying the approach in two subdomains of industrial automation.

Robotic devices are increasingly penetrating the human work spaces as stand alone units and helpers. It is believed that a robot could be easily integrated with humans, if the robot can learn how to behave in a socially acceptable manner. This involves a robot to observe, learn and comply with basic rules of human behaviors. As an example, one would expect a robot to travel in an environment without intruding human workspaces unnecessarily. Thus, identifying common motion patterns of people by observing a specific environment is an important task as people's trajectories are usually not random, however are tailored to the way the environment is structured. We propose a learning algorithm to construct a Sampled Hidden Markov Model (SHMM) that captures behavior of people through observations and then demonstrate how this model could be exploited for planning socially aware paths. Experimental results are presented to demonstrate the viability of the proposed approach.

Human robot interaction is an emerging area of research with many challenges. Knowledge about human behaviors could lead to more effective and efficient interactions of a robot in populated environments. This paper presents a probabilistic framework for the learning and representation of human motion patterns in an office environment. It is based on the observation that most human trajectories are not random. Instead people plan trajectories based on many considerations, such as social rules and path length. Motion patterns are learned using an incrementally growing Sampled Hidden Markov Model. This model has a number of interesting properties which can be of use in many applications. For example, the learned knowledge can be used to predict motion, infer social rules, thus improve a robot's operation and its interaction with people in a populated space. The proposed learning method is extensively validated in real world experiments.

Providing safety critical information to the driver is vital in reducing road accidents, especially at intersections. Intersections are complex to deal with due to the presence of large number of vehicle and pedestrian activities, and possible occlusions. Information available from only the sensors onboard a vehicle has limited value in this scenario. In this paper, we propose to utilize sensors on-board the vehicle of interest as well as the sensors that are mounted on nearby vehicles to enhance the driver situation awareness. The resulting major research challenge of sensor registration with moving observers is solved using a mutual information based technique. The response of the sensors to common causes are identified and exploited for computing their unknown relative locations. Experimental results, for a mock up traffic intersection in which mobile robots equipped with laser range finders are used, are presented to demonstrate the efficacy of the proposed technique.

Most of the lane marking detection algorithms reported in the literature are suitable for highway scenarios. This paper presents a novel clustered particle filter based approach to lane detection, which is suitable for urban streets in normal traffic conditions. Furthermore, a quality measure for the detection is calculated as a measure of reliability. The core of this approach is the usage of weak models, i.e. the avoidance of strong assumptions about the road geometry. Experiments were carried out in Sydney urban areas with a vehicle mounted laser range scanner and a ccd camera. Through experimentations, we have shown that a clustered particle filter can be used to efficiently extract lane markings.