Mission programming for flying ensembles: combining planning with self-organization

The application of autonomous mobile robots can improve many situations of our daily lives. Robots can enhance working conditions, provide innovative techniques for different research disciplines, and support rescue forces in an emergency. In particular, flying robots have already shown their potential in many use-cases when cooperating in ensembles. Exploiting this potential requires sophisticated measures for the goal-oriented, application-specific programming of flying ensembles and the coordinated execution of so defined programs. Because different goals require different robots providing different capabilities, several software approaches emerged recently that focus on specifically designed robots. These approaches often incorporate autonomous planning, scheduling, optimization, and reasoning attributable to classic artificial intelligence. This allows for the goal-oriented instruction of ensembles, but also leads to inefficiencies if ensembles grow large or face uncertainty in the environment. By leaving the detailed planning of executions to individuals and foregoing optimality and goal-orientation, the selforganization paradigm can compensate for these drawbacks by scalability and robustness. In this thesis, we combine the advantageous properties of autonomous planning with that of self-organization in an approach to Mission Programming for Flying Ensembles. Furthermore, we overcome the current way of thinking about how mobile robots should be designed. Rather than assuming fixed-design robots, we assume that robots are modifiable in terms of their hardware at run-time. While using such robots enables their application in many different use cases, it also requires new software approaches for dealing with this flexible design. The contributions of this thesis thus are threefold. First, we provide a layered reference architecture for physically reconfigurable robot ensembles. Second, we provide a solution for programming missions for ensembles consisting of such robots in a goal-oriented fashion that provides measures for instructing individual robots or entire ensembles as desired in the specific use case. Third, we provide multiple self-organization mechanisms to deal with the system’s flexible design while executing such missions. Combining different self-organization mechanisms ensures that ensembles satisfy the static requirements of missions. We provide additional self-organization mechanisms for coordinating the execution in ensembles ensuring they meet the dynamic requirements of a mission. Furthermore, we provide a solution for integrating goal-oriented swarm behavior into missions using a general pattern we have identified for trajectory-modification-based swarm behavior. Using that pattern, we can modify, quantify, and further process the emergent effect of varying swarm behavior in a mission by changing only the parameters of its implementation. We evaluate results theoretically and practically in different case studies by deploying our techniques to simulated and real hardware.Der Einsatz von autonomen mobilen Robotern kann viele Abläufe unseres täglichen Lebens erleichtern. Ihr Einsatz kann Arbeitsbedingungen verbessern, als innovative Technik für verschiedene Forschungsdisziplinen dienen oder Rettungskräfte im Einsatz unterstützen. Insbesondere Flugroboter haben ihr Potenzial bereits in vielerlei Anwendungsfällen gezeigt, gerade wenn mehrere in Ensembles eingesetzt werden. Das Potenzial fliegender Ensembles zielgerichtet und anwendungsspezifisch auszuschöpfen erfordert ausgefeilte Programmiermethoden und Koordinierungsverfahren. Zu diesem Zweck sind zuletzt viele unterschiedliche und auf speziell entwickelte Roboter zugeschnittene Softwareansätze entstanden. Diese verwenden oft klassische Planungs-, Scheduling-, Optimierungs- und Reasoningverfahren. Während dies vor allem den zielgerichteten Einsatz von Ensembles ermöglicht, ist es jedoch auch oft ineffizient, wenn die Ensembles größer oder deren Einsatzumgebungen unsicher werden. Die genannten Nachteile können durch das Paradigma der Selbstorganisation kompensiert werden: Falls Anwendungen nicht zwangsläufig auf Optimalität und strikte Zielorientierung ausgelegt sind, kann so Skalierbarkeit und Robustheit im System erreicht werden. In dieser Arbeit werden die vorteilhaften Eigenschaften klassischer Planungstechniken mit denen der Selbstorganisation in einem Ansatz zur Missionsprogrammierung für fliegende Ensembles kombiniert. In der dafür entwickelten Lösung wird von der aktuell etablierten Ansicht einer unveränderlichen Roboterkonstruktion abgewichen. Stattdessen wird die Hardwarezusammenstellung der Roboter als zur Laufzeit modifizierbar angesehen. Der Einsatz solcher Roboter erfordert neue Softwareansätze um mit genannter Flexibilität umgehen zu können. Die hier vorgestellten Beiträge zu diesem Thema lassen sich in drei Punkten zusammenfassen: Erstens wird eine Schichtenarchitektur als Referenz für physikalisch konfigurierbare Roboterensembles vorgestellt. Zweitens wird eine Lösung zur zielorientierten Missions-Programmierung für derartige Ensembles präsentiert, mit der sowohl einzelne Roboter als auch ganze Ensembles instruiert werden können. Drittens werden mehrere Selbstorganisationsmechanismen vorgestellt, die die autonome Ausführung so erstellter Missionen ermöglichen. Durch die Kombination verschiedener Selbstorganisationsmechanismen wird sichergestellt, dass Ensembles die missionsspezifischen Anforderungen erfüllen. Zusätzliche Selbstorganisationsmechanismen ermöglichen die koordinierte Ausführung der Missionen durch die Ensembles. Darüber hinaus bietet diese Lösung die Möglichkeit der Integration zielorientierten Schwarmverhaltens. Durch ein allgemeines algorithmisches Verfahren für auf Trajektorien-Modifikation basierendes Schwarmverhalten können allein durch die Änderung des Parametersatzes unterschiedliche emergente Effekte in einer Mission erzielt, quantifiziert und weiterverarbeitet werden. Zur theoretischen und praktischen Evaluierung der Ergebnisse dieser Arbeit wurden die vorgestellten Techniken in verschiedenen Fallstudien auf simulierter sowie realer Hardware zum Einsatz gebracht

Maple-Swarm: programming collective behavior for ensembles by extending HTN-planning

Programming goal-oriented behavior in collective adaptive systems is complex, requires high effort, and is failure-prone. If the system's user wants to deploy it in a real-world environment, hurdles get even higher: Programs urgently require to be situation-aware. With our framework Maple, we previously presented an approach for easing the act of programming such systems on the level of particular robot capabilities. In this paper, we extend our approach for ensemble programming with the possibility to address virtual swarm capabilities encapsulating collective behavior to whole groups of agents. By using the respective concepts in an extended version of hierarchical task networks and by adapting our self-organization mechanisms for executing plans resulting thereof, we can achieve that all agents, any agent, any other set of agents, or a swarm of agents execute (swarm) capabilities. Moreover, we extend the possibilities of expressing situation awareness during planning by introducing planning variables that can get modified at design-time or run-time as needed. We illustrate the possibilities with examples each. Further, we provide a graphical front-end offering the possibility to generate mission-specific problem domain descriptions for ensembles including a lightweight simulation for validating plans

RACIPE: a computational tool for modeling gene regulatory circuits using randomization.

BACKGROUND: One of the major challenges in traditional mathematical modeling of gene regulatory circuits is the insufficient knowledge of kinetic parameters. These parameters are often inferred from existing experimental data and/or educated guesses, which can be time-consuming and error-prone, especially for large networks. RESULTS: We present a user-friendly computational tool for the community to use our newly developed method named random circuit perturbation (RACIPE), to explore the robust dynamical features of gene regulatory circuits without the requirement of detailed kinetic parameters. Taking the network topology as the only input, RACIPE generates an ensemble of circuit models with distinct randomized parameters and uniquely identifies robust dynamical properties by statistical analysis. Here, we discuss the implementation of the software and the statistical analysis methods of RACIPE-generated data to identify robust gene expression patterns and the functions of genes and regulatory links. Finally, we apply the tool on coupled toggle-switch circuits and a published circuit of B-lymphopoiesis. CONCLUSIONS: We expect our new computational tool to contribute to a more comprehensive and unbiased understanding of mechanisms underlying gene regulatory networks. RACIPE is a free open source software distributed under (Apache 2.0) license and can be downloaded from GitHub ( https://github.com/simonhb1990/RACIPE-1.0 )

EBF1-deficient bone marrow stroma elicits persistent changes in HSC potential

Crosstalk between mesenchymal stromal cells (MSCs) and hematopoietic stem cells (HSCs) is essential for hematopoietic homeostasis and lineage output. Here, we investigate how transcriptional changes in bone marrow (BM) MSCs result in long-lasting effects on HSCs. Single-cell analysis of Cxcl12-abundant reticular (CAR) cells and PDGFRα+Sca1+ (PαS) cells revealed an extensive cellular heterogeneity but uniform expression of the transcription factor gene Ebf1. Conditional deletion of Ebf1 in these MSCs altered their cellular composition, chromatin structure and gene expression profiles, including the reduced expression of adhesion-related genes. Functionally, the stromal-specific Ebf1 inactivation results in impaired adhesion of HSCs, leading to reduced quiescence and diminished myeloid output. Most notably, HSCs residing in the Ebf1-deficient niche underwent changes in their cellular composition and chromatin structure that persist in serial transplantations. Thus, genetic alterations in the BM niche lead to long-term functional changes of HSCs

Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics.

BackgroundSingle-cell transcriptomics allows researchers to investigate complex communities of heterogeneous cells. It can be applied to stem cells and their descendants in order to chart the progression from multipotent progenitors to fully differentiated cells. While a variety of statistical and computational methods have been proposed for inferring cell lineages, the problem of accurately characterizing multiple branching lineages remains difficult to solve.ResultsWe introduce Slingshot, a novel method for inferring cell lineages and pseudotimes from single-cell gene expression data. In previously published datasets, Slingshot correctly identifies the biological signal for one to three branching trajectories. Additionally, our simulation study shows that Slingshot infers more accurate pseudotimes than other leading methods.ConclusionsSlingshot is a uniquely robust and flexible tool which combines the highly stable techniques necessary for noisy single-cell data with the ability to identify multiple trajectories. Accurate lineage inference is a critical step in the identification of dynamic temporal gene expression

Rat olfactory mucosa mesenchymal stem/stromal cells (om-mscs): a characterization study

Stem/stromal cell-based therapies are a branch of regenerative medicine and stand as an attractive option to promote the repair of damaged or dysfunctional tissues and organs. Olfactory mucosa mesenchymal stem/stromal cells have been regarded as a promising tool in regenerative therapies because of their several favorable properties such as multipotency, high proliferation rate, helpful location, and few associated ethical issues. These cells are easily accessible in the nasal cavity of most mammals, including the rat, can be easily applied in autologous treatments, and do not cope with most of the obstacles associated with the use of other stem cells. Despite this, its application in preclinical trials and in both human and animal patients is still limited because of the small number of studies performed so far and to the nonexistence of a standard and unambiguous protocol for collection, isolation, and therapeutic application. In the present work a validation of a protocol for isolation, culture, expansion, freezing, and thawing of olfactory mucosa mesenchymal stem/stromal cells was performed, applied to the rat model, as well as a biological characterization of these cells. To investigate the therapeutic potential of OM-MSCs and their eventual safe application in preclinical trials, the main characteristics of OMSC stemness were addressed.info:eu-repo/semantics/publishedVersio

Launching the T-cell-lineage developmental programme

Multipotent blood progenitor cells enter the thymus and begin a protracted differentiation process in which they gradually acquire T-cell characteristics while shedding their legacy of developmental plasticity. Notch signalling and basic helix-loop-helix E-protein transcription factors collaborate repeatedly to trigger and sustain this process throughout the period leading up to T-cell lineage commitment. Nevertheless, the process is discontinuous with separately regulated steps that demand roles for additional collaborating factors. This Review discusses new evidence on the coordination of specification and commitment in the early T-cell pathway; effects of microenvironmental signals; the inheritance of stem-cell regulatory factors; and the ensemble of transcription factors that modulate the effects of Notch and E proteins, to distinguish individual stages and to polarize T-cell-lineage fate determination

Stem cell expansion and bioreactor development

PhDA major challenge to the clinical success of cell-based tissue engineering strategies is the ability to obtain sufficient numbers of cells within an acceptable time frame. The expansion of cells on microcarriers within spinner flask bioreactor has shown promise in meeting that challenge. Spinner flask microcarrier technology is space-saving and media utilisation efficient. However, further optimisation in terms of, for example, seeding efficiency, expansion rates and harvest efficiency is necessary to realise the clinical potential of this technology. The present work is designed to improve cell expansion rates. It involves investigation of microcarrier composition and surface structure and spinner flask shear stress on cell growth. BMSC growth on PHBV microcarriers was superior to PCL and PLGA microcarriers and comparable to Cytodex 1 microcarriers. Lower density PHBV microcarriers showed promise as a superior alternative to Cytodex 1. Two different impeller designs employed in the w/o/w method of microcarrier synthesis resulted in smoother and rougher PCL microcarriers with Ra = 1.77 ± 0.42 μm to 6.4 ± 1.48 μm respectively. Superior BMSC growth was observed on the rougher PCL microcarriers. Differentiation potential along the osteogenic and adipogenic lineages of BMSCs expanded on the microcarrier types was retained. Particle Image Velocimetry was used to quantify shear stress within a spinner flask bioreactor. It was found that 80% of the shear stress was localised within the impeller region which occupied 55% of the bioreactor working volume. Shear stress increased as Cytodex 1 microcarrier concentration and impeller rotational speed increased. Superior BMSC growth rates on microcarriers were observed for the lowest shear stress experimental group (3.4 x 10-3 N/m2 ≤ impeller region mean shear stress ≤ 4.6 x 10-3 N/m2) as compared to the three higher shear stress groups (5.5 x 10-3 N/m2 ≤ mean shear stress ≤ 1.3 x 10-2 N/m2). Expanded BMSCs on the cytodex 1 microcarriers retained multipotentiality for the range of shear stresses investigated

The chromatin landscape and transcription factors in T cell programming

T cell development from multipotent progenitors to specialized effector subsets of mature T cells is guided by the iterative action of transcription factors. At each stage, transcription factors interact not only with an existing landscape of histone modifications and nucleosome packing, but also with other bound factors, while they modify the landscape for later-arriving factors in ways that fundamentally affect the control of gene expression. This review covers insights from genome-wide analyses of transcription factor binding and resulting chromatin conformation changes that reveal roles of cytokine signaling in effector T cell programming, the ways in which one factor can completely transform the impacts of previously bound factors, and the ways in which the baseline chromatin landscape is established during early T cell lineage commitment

Computational Models of Intracellular and Intercellular Processes in Developmental Biology

Systems biology takes a holistic approach to biological questions as it applies mathematical modeling to link and understand the interaction of components in complex biological systems. Multiscale modeling is the only method that can fully accomplish this aim. Mutliscale models consider processes at different levels that are coupled within the modeling framework. A first requirement in creating such models is a clear understanding of processes that operate at each level. This research focuses on modeling aspects of biological development as a complex process that occurs at many scales. Two of these scales were considered in this work: cellular differentiation, the process of in which less specialized cells acquired specialized properties of mature cell types, and morphogenesis, the process in which an organism develops its shape and tissue architecture. In development, cellular differentiation typically is required for morphogenesis. Therefore, cellular differentiation is at a lower scale than morphogenesis in the overall process of development. In this work, cellular differentiation and morphogenesis were modeled in a variety of biological contexts, with the ultimate goal of linking these different scales of developmental events into a unified model of development. Three aspects of cellular differentiation were investigated, all united by the theme of how the dynamics of gene regulatory networks (GRNs) control differentiation. Two of the projects of this dissertation studied the effect of noise and robustness in switching between cell types during differentiation, and a third deals with the evaluation of hypothetical GRNs that allow the differentiation of specific cell types. All these projects view cell types as high-dimensional attractors in the GRNs and use random Boolean networks as the modeling framework for studying network dynamics. Morphogenesis was studied using the emergence of three-dimensional structures in biofilms as a relatively simple model. Many strains of bacteria form complex structures during growth as colonies on a solid medium. The morphogenesis of these structures was modeled using an agent-based framework and the outcomes were validated using structures of biofilm colonies reported in the literature