RobotKube: Orchestrating Large-Scale Cooperative Multi-Robot Systems with Kubernetes and ROS

Modern cyber-physical systems (CPS) such as Cooperative Intelligent Transport Systems (C-ITS) are increasingly defined by the software which operates these systems. In practice, microservice architectures can be employed, which may consist of containerized microservices running in a cluster comprised of robots and supporting infrastructure. These microservices need to be orchestrated dynamically according to ever changing requirements posed at the system. Additionally, these systems are embedded in DevOps processes aiming at continually updating and upgrading both the capabilities of CPS components and of the system as a whole. In this paper, we present RobotKube, an approach to orchestrating containerized microservices for large-scale cooperative multi-robot CPS based on Kubernetes. We describe how to automate the orchestration of software across a CPS, and include the possibility to monitor and selectively store relevant accruing data. In this context, we present two main components of such a system: an event detector capable of, e.g., requesting the deployment of additional applications, and an application manager capable of automatically configuring the required changes in the Kubernetes cluster. By combining the widely adopted Kubernetes platform with the Robot Operating System (ROS), we enable the use of standard tools and practices for developing, deploying, scaling, and monitoring microservices in C-ITS. We demonstrate and evaluate RobotKube in an exemplary and reproducible use case that we make publicly available at https://github.com/ika-rwth-aachen/robotkube .Comment: 7 pages, 2 figures, 2 tables; Accepted to be published as part of the 26th IEEE International Conference on Intelligent Transportation Systems (ITSC), Bilbao, Spain, September 24-28, 202

UNICARagil – New architectures for disruptive vehicle concepts

This paper provides an overview of the research topics of the UNICARagil project with the focus on different architectures, such as the mechatronic, the software, and the mechanic architecture. The main research questions as well as possible solutions, which will be investigated in this project, are described. The project is funded by the Federal Ministry of Education and Research of Germany In terms of the mechatronic and the software architecture, this paper focuses on the ECU concept: the main tasks of the automated driving process are executed on three ECUs, which are called the cerebrum, the brainstem and the spinal cord. This architecture supports the modular approach regarding functional safety, the ability of future updates and upgrades, and the service orientated architecture (SOA) of the software. The well-known SOA approach is transferred to automotive applications and becomes the automotive service orientated architecture (ASOA). Furthermore, the mechanic structure of the four vehicles AUTOtaxi, AUTOelfe, AUTOliefer and AUTOshuttle is described

UNICARagil - Disruptive Modular Architectures for Agile, Automated Vehicle Concepts

This paper introduces UNICARagil, a collaborative project carried out by a consortium of seven German universities and six industrial partners, with funding provided by the Federal Ministry of Education and Research of Germany. In the scope of this project, disruptive modular structures for agile, automated vehicle concepts are researched and developed. Four prototype vehicles of different characteristics based on the same modular platform are going to be build up over a period of four years. The four fully automated and driverless vehicles demonstrate disruptive architectures in hardware and software, as well as disruptive concepts in safety, security, verification and validation. This paper outlines the most important research questions underlying the project

Automation of the UNICARagil Vehicles

The German research project UNICARagil is a collaboration between eight universities and six industrial partners funded by the Federal Ministry of Education and Research. It aims to develop innovative modular architectures and methods for new agile, automated vehicle concepts. This paper summarizes the automation approach of the driverless vehicle concept and its modular realization within the four demonstration vehicles to be built by the consortium. On-board each vehicle, this comprises sensor modules for environment perception and modelling, motion planning for normal driving and safe halts, as well as the respective control algorithms and base functionalities like precise localization. A control room and cloud functionalities provide off-board support to the vehicles, which are additionally addressed in this paper

Traces of trauma – a multivariate pattern analysis of childhood trauma, brain structure and clinical phenotypes

Background: Childhood trauma (CT) is a major yet elusive psychiatric risk factor, whose multidimensional conceptualization and heterogeneous effects on brain morphology might demand advanced mathematical modeling. Therefore, we present an unsupervised machine learning approach to characterize the clinical and neuroanatomical complexity of CT in a larger, transdiagnostic context. Methods: We used a multicenter European cohort of 1076 female and male individuals (discovery: n = 649; replication: n = 427) comprising young, minimally medicated patients with clinical high-risk states for psychosis; patients with recent-onset depression or psychosis; and healthy volunteers. We employed multivariate sparse partial least squares analysis to detect parsimonious associations between combinations of items from the Childhood Trauma Questionnaire and gray matter volume and tested their generalizability via nested cross-validation as well as via external validation. We investigated the associations of these CT signatures with state (functioning, depressivity, quality of life), trait (personality), and sociodemographic levels. Results: We discovered signatures of age-dependent sexual abuse and sex-dependent physical and sexual abuse, as well as emotional trauma, which projected onto gray matter volume patterns in prefronto-cerebellar, limbic, and sensory networks. These signatures were associated with predominantly impaired clinical state- and trait-level phenotypes, while pointing toward an interaction between sexual abuse, age, urbanicity, and education. We validated the clinical profiles for all three CT signatures in the replication sample. Conclusions: Our results suggest distinct multilayered associations between partially age- and sex-dependent patterns of CT, distributed neuroanatomical networks, and clinical profiles. Hence, our study highlights how machine learning approaches can shape future, more fine-grained CT research