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The idea of component-based software engineering was proposed more that 40 years ago, yet only few robotics software frameworks follow these ideas. The main problem with robotics software usually is that it runs on a particular platform and transferring source code to another platform is crucial. In this paper, we present our software framework Fawkes which follows the component-based software design paradigm by featuring a clear component concept with well-defined communication interfaces. We deployed Fawkes on several different robot platforms ranging from service robots to biped soccer robots. Following the component concept with clearly defined communication interfaces shows great benefit when porting robot software from one robot to the other. Fawkes comes with a number of useful plugins for tasks like timing, logging, data visualization, software configuration, and even high-level decision making. These make it particularly easy to create and to debug productive code, shortening the typical development cycle for robot software.
The high-level decision making process of an autonomous robot can be seen as an hierarchically organised entity, where strategical decisions are made on the topmost layer, while the bottom layer serves as driver for the hardware. In between is a layer with monitoring and reporting functionality. In this paper we propose a behaviour engine for this middle layer which, based on formalism of hybrid state machines (HSMs), bridges the gap between high-level strategic decision making and low-level actuator control. The behaviour engine has to execute and monitor behaviours and reports status information back to the higher level. To be able to call the behaviours or skills hierarchically, we extend the model of HSMs with dependencies and sub-skills. These Skill-HSMs are implemented in the lightweight but expressive Lua scripting language which is well-suited to implement the behaviour engine on our target platform, the humanoid robot Nao.
In times of social climate protection movements, such as Fridays for Future, the priorities of society, industry and higher education are currently changing. The consideration of sustainability challenges is increasing. In the context of sustainable development, social skills are crucial to achieving the United Nations Sustainable Development Goals (SDGs). In particular, the impact that educational activities have on people, communities and society is therefore coming to the fore. Research has shown that people with high levels of social competence are better able to manage stressful situations, maintain positive relationships and communicate effectively. They are also associated with better academic performance and career success. However, especially in engineering programs, the social pillar is underrepresented compared to the environmental and economic pillars.
In response to these changes, higher education institutions should be more aware of their social impact - from individual forms of teaching to entire modules and degree programs. To specifically determine the potential for improvement and derive resulting change for further development, we present an initial framework for social impact measurement by transferring already established approaches from the business sector to the education sector. To demonstrate the applicability, we measure the key competencies taught in undergraduate engineering programs in Germany.
The aim is to prepare the students for success in the modern world of work and their future contribution to sustainable development. Additionally, the university can include the results in its sustainability report. Our method can be applied to different teaching methods and enables their comparison.
The work in modern open-pit and underground mines requires the transportation of large amounts of resources between fixed points. The navigation to these fixed points is a repetitive task that can be automated. The challenge in automating the navigation of vehicles commonly used in mines is the systemic properties of such vehicles. Many mining vehicles, such as the one we have used in the research for this paper, use steering systems with an articulated joint bending the vehicle’s drive axis to change its course and a hydraulic drive system to actuate axial drive components or the movements of tippers if available. To address the difficulties of controlling such a vehicle, we present a model-predictive approach for controlling the vehicle. While the control optimisation based on a parallel error minimisation of the predicted state has already been established in the past, we provide insight into the design and implementation of an MPC for an articulated mining vehicle and show the results of real-world experiments in an open-pit mine environment.
In this paper we investigate the use of deep neural networks for 3D object detection in uncommon, unstructured environments such as in an open-pit mine. While neural nets are frequently used for object detection in regular autonomous driving applications, more unusual driving scenarios aside street traffic pose additional challenges. For one, the collection of appropriate data sets to train the networks is an issue. For another, testing the performance of trained networks often requires tailored integration with the particular domain as well. While there exist different solutions for these problems in regular autonomous driving, there are only very few approaches that work for special domains just as well. We address both the challenges above in this work. First, we discuss two possible ways of acquiring data for training and evaluation. That is, we evaluate a semi-automated annotation of recorded LIDAR data and we examine synthetic data generation. Using these datasets we train and test different deep neural network for the task of object detection. Second, we propose a possible integration of a ROS2 detector module for an autonomous driving platform. Finally, we present the performance of three state-of-the-art deep neural networks in the domain of 3D object detection on a synthetic dataset and a smaller one containing a characteristic object from an open-pit mine.
Development of a subject-oriented reference process model for the telecommunications industry
(2016)
Generally the usage of reference models can be structured top-down or bottom-up. The practical need of agile change and flexible organizational implementation requires a consistent mapping to an operational level. In this context, well-established reference process models are typically structured top-down. The subject-oriented Business Process Management (sBPM) offers a modeling concept that is structured bottom-up and concentrates on the process actors on an
operational level. This paper applies sBPM to the enhanced Telecom Operations Map (eTOM), a well-accepted reference process model in the telecommunications industry. The resulting design artifact is a concrete example for a combination of a bottom-up and top-down developed reference model. The results are evaluated and confirmed in practical context through the involvement of the industry body TMForum.
An H2O2 sensor for the application in industrial sterilisation processes has been developed. Therefore, automated sterilisation equipment at laboratory scale has been constructed using parts from industrial sterilisation facilities. In addition, a software tool has been developed for the control of the sterilisation equipment at laboratory scale. First measurements with the developed sensor set-up as part of the sterilisation equipment have been performed and the sensor has been physically characterised by optical microscopy and SEM.
The absence of a general method for endotoxin removal from liquid interfaces gives an opportunity to find new methods and materials to overcome this gap. Activated nanostructured carbon is a promising material that showed good adsorption properties due to its vast pore network and high surface area. The aim of this study is to find the adsorption rates for a carboneous material produced at different temperatures, as well as to reveal possible differences between the performance of the material for each of the adsorbates used during the study (hemoglobin, serum albumin and lipopolysaccharide, LPS).