Rolling element eccentric drives promise to be an easy-to-manufacture and performant gear system for robotic actuators. They share characteristics with other eccentric drives, such as strain wave and cycloidal drives, but use rolling elements instead of an eccentric gear. They offer reduced manufacturing complexity and costs by using readily available standard parts. Little research into rolling element eccentric drives is available, and their characteristics are still underexplored. This work uses a contact-based model to investigate the previously unknown stiffness of rolling element eccentric drives. Such calculation methods are well established for structurally similar components, such as cycloidal drives and roller bearings, and provide a high-level and computationally efficient model. Good stiffness models are critical for accurately predicting robotic actuator behavior and enabling better control of robotic systems. Additionally, the proposed model is used to calculate the contact forces under load occurring in rolling element eccentric drives. Contact forces are critical to calculating a drive’s load capacity, lifetime, and efficiency and serve as the foundation for further research. The mathematical description of the proposed model is derived, and the stiffness of a representative rolling element eccentric drive is calculated. Different manufacturing techniques, characterized by tolerance levels and material choices, are compared. Irrespective of manufacturing precision, similar stiffness curves result for drives made of steel, but higher contact forces result from less precise manufacturing. The stiffness of drives made from 3D printed plastic is considerably lower than that of drives made from steel. Additionally, the stiffness of rolling element eccentric drives is compared to similar eccentric drives, and a comparable twist-over-torque curve is shown.

Processor Layout Utilization for Thermal Optimization (PLUTO) is a research project that started in 2022 and is co-funded by the Bavarian Ministry of Economic Affairs, Regional Development, and Energy. In a consortium of space industry and university, the PLUTO project investigates new methodologies for designing thermally sensitive satellite payloads accommodating distributed processor modules. A top-down design approach, based on so-called solution spaces, is applied to arrange modules within a given payload envelope and find a payload layout with low thermal gradients for various operational modes. A digital twin of the thermal behavior of the system shall then be derived from the design and implemented as a software routine in the processor modules. The temperature gradients within the payload shall be minimized by adjusting the distribution of power consumption within the payload. Therefore, the system control and processing tasks are locally distributed among the modules on the fly based on the prediction of the digital twin. A demonstrator payload with three interconnected processor modules is currently under development and is expected to be thermally tested in 2024.

Optimising mechanical, control and actuator design variables together as a co-design problem enables identifying novel and better-performing robot architectures. Typically, solving such problems using conventional optimisation methods yields a single, point-based solution. Deviating from the computed optima may be necessary to ensure physical feasibility, typically associated with a performance loss. In this work, we present a two-step cascaded optimisation approach to identify non-intuitive designs and recover the loss in performance by constructing a solution space. The solution space provides robustness in the form of permissible ranges of design variable values and enables the selection of a physically feasible design. In our study, we observe (1) up to 20% of the lost performance is recovered and (2) an improvement of 30 % on the task metric in comparison to an existing robot and (3) designs with cost savings of up to 10% can be identified.

One of the limitations of classical SysML is that it does not provide information on how to model system component's constraints, which can result in individual model solutions for the same elements. To address this issue, we propose utilizing the reference mechanism in SysML to refer to REXS and ECLASS data standards, which can unambiguously define characteristic properties. Environmental factors such as temperature or humidity constraints play a vital role in designing components of mechatronic systems, like transmissions, or electrical and electronic components like sensors and actuators. Mechatronic component's properties may change depending on temperature and humidity and may show degradation. Such information is provided to some extent in the component's classified properties, like ECLASS and/or REXS, the engineering tool to configure gears. Such properties and constraints are further detailed in specification sheets provided by the manufacturer, e.g., by multi-dimensional manufacturer-specific parametric curves which are proposed to be explicitly included in the modeling process as SysML constraints. The proposed approach allows to include additional, not yet standardized environmental effects such as dirt layers in a formalized way. The formalized properties are modeled in parametric diagrams of SysML.

The axes of robots and robot-like systems (RLS) usually include e-motor-gearbox-arrangements for optimal connection of the elements. The characteristics of the drive system and thus also of the robot depend strongly on the gears. Different gearbox designs are available which differ in stiffness, efficiency and further properties. For an application-optimal design of RLS a uniform documentation and a comparability of gearbox concepts is a decisive factor. The application-optimal design is supported by an interdisciplinary approach between mechanical engineering and software design, guided by adequate product development methodology. The quite heterogeneous characterization of gearboxes for RLS which is currently the state of the art is a relevant obstacle in the flexible and optimal design of RLS. The paper shows the analysis of the gear structure with unified symbols for specific machine elements and contact types. The introduced method gives insight into the mechanical structure of the gearboxes. Similarities between gear types can thus be revealed. This also enables the classification of new developments in the state of the art. Moreover, the developed method for analyzing the gear structure can be used to determine the characteristics of gears. Examples for these characteristics are backlash, efficiency or stiffness. Specifically, the stiffness of gears can be synthesized by the force action of individual contacts and the individual phenomena that occur with them. The representation by individual phenomena also makes it possible to extend the calculation to include influencing parameters such as temperature that have not been sufficiently taken into account so far.

Robots are increasingly involved in tasks that require them to navigate social spaces shared with humans. Following social norms is considered a key requirement for such robots to ensure their acceptance and long-term use. This paper focuses on delivery robots as these often encounter problems in their operational areas–in this case a busy university campus– when they find their way blocked by people and they cannot move on towards their goal destination. We explored automated cue triggering to resolve this situation autonomously without the help of remote operators. Eighty-three pedestrians participated in a real-world study using a delivery robot. Four different cues were tested for their perceived politeness and ambiguity. The four different cues differed in the presence or absence of an instruction and the presence or absence of a justification for the request to let the robot pass as well as source orientation within the justification, which was either internally (self-) directed or externally (user-) directed. The results reveal a complex picture. Overall, a positive effect of verbal instructions in comparison to staying mute on social attributions to the robot was found. Contrary to our expectations, there was no significant difference in politeness between the different requests. Participants’ willingness to let the robot pass was positively correlated with perceived politeness, and negatively correlated with ambiguity of the requests.

Companies producing Robot-Like Systems (RLS) must increase efficiency in design and production in order to stay competitive in the international market. Such RLS range from small SCARA robots to entire production facilities. Very different systems for similar tasks are published in research or offered in the market. A clear path to an optimal configuration for a given task is apparently not available to the engineer. This indicates that the gear drives of such RLS and their integration with automation technology are still insufficiently researched, and their efficient design poses significant challenges for the industry. This paper identifies and provides a concise overview of requirements and challenges in the context of drives for such RLS. Building on this overview, suggestions are made for the future course of action in research.

This paper assessed four types of human-machine interfaces (HMIs), classified according to the stages of automation proposed by Parasuraman et al. [“A model for types and levels of human interaction with automation,” IEEE Trans. Syst. Man, Cybern. A, Syst. Humans, vol. 30, no. 3, pp. 286-297, May 2000]. We hypothesized that drivers would implement decisions (lane changing or braking) faster and more correctly when receiving support at a higher automation stage during transitions from conditionally automated driving to manual driving. In total, 25 participants with a mean age of 25.7 years (range 19-36 years) drove four trials in a driving simulator, experiencing four HMIs having the following different stages of automation: baseline (information acquisition-low), sphere (information acquisition-high), carpet (information analysis), and arrow (decision selection), presented as visual overlays on the surroundings. The HMIs provided information during two scenarios, namely a lane change and a braking scenario. Results showed that the HMIs did not significantly affect the drivers' initial reaction to the take-over request. Improvements were found, however, in the decision-making process: When drivers experienced the carpet or arrow interface, an improvement in correct decisions (i.e., to brake or change lane) occurred. It is concluded that visual HMIs can assist drivers in making a correct braking or lane change maneuver in a take-over scenario. Future research could be directed toward misuse, disuse, errors of omission, and errors of commission.

A new parameter estimation algorithm, MERLIN, is presented for accurate and robust multiexponential relaxometry using magnetic resonance imaging, a tool that can provide valuable insight into the tissue microstructure of the brain. Multi-exponential relaxometry is used to analyze the myelin water fraction and can help to detect related diseases. However, the underlying problem is ill-conditioned, and as such, is extremely sensitive to noise and measurement imperfections, which can lead to less precise and more biased parameter estimates. MERLIN is a fully automated, multi-voxel approach that incorporates state-of-the-art ℓ1-regularization to enforce sparsity and spatial consistency of the estimated distributions. The proposed method is validated in simulations and in vivo experiments, using a multi-echo gradient-echo (MEGE) sequence at 3 T. MERLIN is compared to the conventional single-voxel ℓ2-regularized NNLS (rNNLS) and a multivoxel extension with spatial priors (rNNLS + SP), where it consistently showed lower root mean squared errors of up to 70 percent for all parameters of interest in these simulations.

A new reconstruction method, coined MIRAGE, is presented for accurate, fast, and robust parameter mapping of multiple-echo gradient-echo (MEGE) imaging, the basis sequence of novel quantitative magnetic resonance imaging techniques such as water content and susceptibility mapping. Assuming that the temporal signal can be modeled as a sum of damped complex exponentials, MIRAGE performs model-based reconstruction of undersampled data by minimizing the rank of local Hankel matrices. It further incorporates multi-channel information and spatial prior knowledge. Finally, the parameter maps are estimated using nonlinear regression. Simulations and retrospective undersampling of phantom and in vivo data affirm robustness, e.g., to strong inhomogeneity of the static magnetic field and partial volume effects. MIRAGE is compared with a state-of-the-art compressed sensing method, -ESPIRiT. Parameter maps estimated from reconstructed data using MIRAGE are shown to be accurate, with the mean absolute error reduced by up to 50% for in vivo results. The proposed method has the potential to improve the diagnostic utility of quantitative imaging techniques that rely on MEGE data.

This article proposes a theory-driven interaction and user interface concept for a cooperative lane-change scenario in a highly automated driving environment. First, cooperative systems are defined and a taxonomy of five levels of cooperation (driver and system intention, mode of cooperation, allocation, interface, and contact) is elaborated. Second, the scenario is introduced and analyzed for each of the levels of cooperation, with a focus on the support of mode awareness, concluded by a detailed analysis based on an interaction diagram. Third, based on this cooperative interaction sequence, a prototypical user interface design is presented, heavily using a scene-linked augmented information display with multimodal amendments.