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Rugged terrain robot designs are important for field robotics missions. A number of commercial platforms are available, however, at an impressive price. In this paper, we describe the hardware and software component of a low-cost wheeled rugged-terrain robot. The robot is based on an electric children quad bike and is modified to be driven by wire. In terms of climbing properties, operation time and payload it can compete with some of the commercially available platforms, but at a far lower price.
Information and communication technology for integrated mobility concepts such as E-carsharing
(2015)
During the past decade attitude towards sharing things has changed extremely. Not just personal data is shared (e.g. in social networks) but also mobility. Together with the increased ecological awareness of the recent years, new mobility concepts have evolved. E-carsharing has become a symbol for these changes of attitude. The management of a shared car fleet, the energy management of electric mobility and the management of various carsharing users with individual likes and dislikes are just some of the major challenges of e-carsharing. Weaving it into integrated mobility concepts, this raises complexity even further. These challenges can only be overcome by an appropriate amount of well-shaped information available at the right place and time. In order to gather, process and share the required information, fleet cars have to be equipped with modern information and communication technology (ICT) and become so-called fully connected cars. Ensuring the usability of these ICT systems is another challenge that is often neglected, even though it is usability that makes carsharing comfortable, attractive and supports users’ new attitudes. By means of an integrated and consistent concept for human-machine interaction (HMI), the usability of such systems can be raised tremendously.
Pure analytical or experimental methods can only find a control strategy for technical systems with a fixed setup. In former contributions we presented an approach that simultaneously finds the optimal topology and the optimal open-loop control of a system via Mixed Integer Linear Programming (MILP). In order to extend this approach by a closed-loop control we present a Mixed Integer Program for a time discretized tank level control. This model is the basis for an extension by combinatorial decisions and thus for the variation of the network topology. Furthermore, one is able to appraise feasible solutions using the global optimality gap.
Gearboxes are mechanical transmission systems that provide speed and torque conversions from a rotating power source. Being a central element of the drive train, they are relevant for the efficiency and durability of motor vehicles. In this work, we present a new approach for gearbox design: Modeling the design problem as a mixed-integer nonlinear program (MINLP) allows us to create gearbox designs from scratch for arbitrary requirements and—given enough time—to compute provably globally optimal designs for a given objective. We show how different degrees of freedom influence the runtime and present an exemplary solution.
Cyber-physical systems are ever more common in manufacturing industries. Increasing their autonomy has been declared an explicit goal, for example, as part of the Industry 4.0 vision. To achieve this system intelligence, principled and software-driven methods are required to analyze sensing data, make goal-directed decisions, and eventually execute and monitor chosen tasks. In this chapter, we present a number of knowledge-based approaches to these problems and case studies with in-depth evaluation results of several different implementations for groups of autonomous mobile robots performing in-house logistics in a smart factory. We focus on knowledge-based systems because besides providing expressive languages and capable reasoning techniques, they also allow for explaining how a particular sequence of actions came about, for example, in the case of a failure.
Around 60% of the paper worldwide is made from recovered paper. Especially adhesive contaminants, so called stickies, reduce paper quality. To remove stickies but at the same time keep as many valuable fibers as possible, multi-stage screening systems with several interconnected pressure screens are used. When planning such systems, suitable screens have to be selected and their interconnection as well as operational parameters have to be defined considering multiple conflicting objectives. In this contribution, we present a Mixed-Integer Nonlinear Program to optimize system layout, component selection and operation to find a suitable trade-off between output quality and yield.
Highly competitive markets paired with tremendous production volumes demand particularly cost efficient products. The usage of common parts and modules across product families can potentially reduce production costs. Yet, increasing commonality typically results in overdesign of individual products. Multi domain virtual prototyping enables designers to evaluate costs and technical feasibility of different single product designs at reasonable computational effort in early design phases. However, savings by platform commonality are hard to quantify and require detailed knowledge of e.g. the production process and the supply chain. Therefore, we present and evaluate a multi-objective metamodel-based optimization algorithm which enables designers to explore the trade-off between high commonality and cost optimal design of single products.
The energy-efficiency of technical systems can be improved by a systematic design approach. Technical Operations Research (TOR) employs methods known from Operations Research to find a global optimal layout and operation strategy of technical systems. We show the practical usage of this approach by the systematic design of a decentralized water supply system for skyscrapers. All possible network options and operation strategies are modeled by a Mixed-Integer Nonlinear Program. We present the optimal system found by our approach and highlight the energy savings compared to a conventional system design.
Because of customer churn, strong competition, and operational inefficiencies, the telecommunications operator ME Telco (fictitious name due to confidentiality) launched a strategic transformation program that included a Business Process Management (BPM) project. Major problems were silo-oriented process management and missing cross-functional transparency. Process improvements were not consistently planned and aligned with corporate targets. Measurable inefficiencies were observed on an operational level, e.g., high lead times and reassignment rates of the incident management process.
The increasing complexity of Advanced Driver Assistance Systems (ADAS) presents a challenging task to validate safe and reliable performance of these systems under varied conditions. The test and validation of ADAS/AD with real test drives, although important, involves huge costs and time. Simulation tools provide an alternative with the added advantage of reproducibility but often use ideal sensors, which do not reflect real sensor output accurately. This paper presents a new validation methodology using fault injection, as recommended by the ISO 26262 standard, to test software and system robustness. In our work, we investigated and developed a tool capable of inserting faults at different software and system levels to verify its robustness. The scope of this paper is to cover the fault injection test for the Visteon’s DriveCore™ system, a centralized domain controller for Autonomous driving which is sensor agnostic and SoC agnostic. With this new approach, the validation of safety monitoring functionality and its behavior can be tested using real-world data instead of synthetic data from simulation tools resulting in having better confidence in system performance before proceeding with in-vehicle testing.
Information technologies, such as big data analytics, cloud computing,
cyber physical systems, robotic process automation, and the internet of things, provide a sustainable impetus for the structural development of business sectors as well as the digitalization of markets, enterprises, and processes. Within the consulting industry, the proliferation of these technologies opened up the new segment of digital transformation, which focuses on setting up, controlling, and implementing projects for enterprises from a broad range of sectors. These recent developments raise the question, which requirements evolve for IT consultants as important success factors of those digital transformation projects. Therefore, this empirical contribution provides indications regarding the qualifications and competences necessary for IT consultants in the era of digital transformation from a labor market perspective. On the one hand, this knowledge base is interesting for the academic education of consultants, since it supports a market-oriented design of adequate training measures. On the other hand, insights into the competence requirements for consultants are considered relevant for skill and talent management processes in consulting practice. Assuming that consulting companies pursue a strategic human resource management approach, labor market information may also be useful to discover strategic behavioral patterns.
Due to the high number of customer contacts, fault clearances, installations, and product provisioning per year, the automation level of operational processes has a significant impact on financial results, quality, and customer experience. Therefore, the telecommunications operator Deutsche Telekom (DT) has defined a digital strategy with the objectives of zero complexity and zero complaint, one touch, agility in service, and disruptive thinking. In this context, Robotic Process Automation (RPA) was identified as an enabling technology to formulate and realize DT’s digital strategy through automation of rule-based, routine, and predictable tasks in combination with structured and stable data.
In order to maximize the possible travel distance of battery electric vehicles with one battery charge, it is mandatory to adjust all components of the powertrain carefully to each other. While current vehicle designs mostly simplify the powertrain rigorously and use an electric motor in combination with a gearbox with only one fixed transmission ratio, the use of multi-gear systems has great potential. First, a multi-speed system is able to improve the overall energy efficiency. Secondly, it is able to reduce the maximum momentum and therefore to reduce the maximum current provided by the traction battery, which results in a longer battery lifetime. In this paper, we present a systematic way to generate multi-gear gearbox designs that—combined with a certain electric motor—lead to the most efficient fulfillment of predefined load scenarios and are at the same time robust to uncertainties in the load. Therefore, we model the electric motor and the gearbox within a Mixed-Integer Nonlinear Program, and optimize the efficiency of the mechanical parts of the powertrain. By combining this mathematical optimization program with an unsupervised machine learning algorithm, we are able to derive global-optimal gearbox designs for practically relevant momentum and speed requirements.
In product development, numerous design decisions have to be made. Multi-domain virtual prototyping provides a variety of tools to assess technical feasibility of design options, however often requires substantial computational effort for just a single evaluation. A special challenge is therefore the optimal design of product families, which consist of a group of products derived from a common platform. Finding an optimal platform configuration (stating what is shared and what is individually designed for each product) and an optimal design of all products simultaneously leads to a mixed-integer nonlinear black-box optimization model. We present an optimization approach based on metamodels and a metaheuristic. To increase computational efficiency and solution quality, we compare different types of Gaussian process regression metamodels adapted from the domain of machine learning, and combine them with a genetic algorithm. We illustrate our approach on the example of a product family of electrical drives, and investigate the trade-off between solution quality and computational overhead.
Recently, novel AI-based services have emerged in the consumer market. AI-based services can affect the way consumers take commercial decisions. Research on the influence of AI on commercial interactions is in its infancy. In this chapter, a framework creating a first overview of the influence of AI on commercial interactions is introduced. This framework summarizes the findings of comparing numerous customer journeys of novel AI-based services with corresponding non-AI equivalents.
The adoption of the Digital Health Transformation is a tremendous paradigm change in health organizations, which is not a trivial process in reality. For that reason, in this chapter, it is proposed a methodology with the objective to generate a changing culture in healthcare organisations. Such a change culture is essential for the successful implementation of any supporting methods like Interactive Process Mining. It needs to incorporate (mostly) new ways of team-based and evidence-based approaches for solving structural problems in a digital healthcare environment.