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This degree project addresses poaching challenges by employing predictive analysis of animal movements and their correlation with the dynamic environment using a machine learning approach. The goal is to provide accurate predictions of animal movements, enabling rangers to intercept potential threats and safeguard wildlife from snares. A wide analysis considers previous studies on animal movements and both animal and environment data availability. To efficiently represent the dynamic environment and correlate it with animal movement data, accurate matching of environment variables to each animal measurement is crucial. We selected multiple environment datasets to capture a sufficient amount ofenvironmental properties. Due to practical constraints, daily representation of the environment is not achievable, and weekly mean or monthly mode values are used instead. Data insights are obtained through the training of a regression neural network using the filtered environmental and animal movement data. The results highlight the significant role ofenvironmental features in predicting animal movements, emphasizing their importance for accurate predictions. Despite some offset and few erroneous predictions, a strong similarity between animal predicted trajectory and animal true trajectory was achieved, indicating that the model is capable to capture general patterns and to correctly tune in predictions of detailed movements as well. The overall offset of the trajectories is still a weak point of this model, but it may just indicate the presence of some underlying systematic error that can be corrected through further work. The integration of such a developed prediction model into existing frameworks could assist law enforcingauthorities in preventing poaching activities.

Recent progress in multiple micro aerial vehicle (MAV) systems has demonstrated autonomous navigation in static environments. Yet, there are limited works regarding the autonomous navigation of multiple MAVs in dynamic and unknown environments. The challenge arises from the complexity of the motion planning problem, which requires the MAVs to coordinate with each other while avoiding dynamic obstacles. This thesis presents a novel risk-aware decentralized multi-agent motion planning framework to address this issue. For perception, we rely on a particle-based dynamic map, which utilizes particles to represent dynamic obstacles and predict future states of dynamic obstacles. Leveraging this map representation and the shared trajectory from other agents, we evaluate the future collision risk with dynamic obstacles and other agents in a coupled manner. During the planning phase, a risk-aware kino-dynamic A* algorithm tailored to the particle-based map representation is developed, ensuring dynamically feasible paths with risks under a given safe level. Subsequently, spatio-temporal safety corridors with maximum volume are optimized by inflating from path segments, taking map particles as constraints. These corridors act as constraints for the trajectory optimization problem, which is simplified to a convex optimization problem by using Bézier splines. The proposed method is thoroughly evaluated in simulation environments featuring various quantities and shapes of dynamic obstacles. Comparative results with state-of-the-art multi-agent planners that rely on precise obstacle observations demonstrate the efficiency and safety of the proposed method. Furthermore, the effectiveness of the proposed method is validated in a more realistic simulation environment with pedestrians, using depth cameras onboard for perception., Mechanical Engineering | Vehicle Engineering | Cognitive Robotics

Phytohormone auxin plays a key role in regulating plant organogenesis. However, understanding the complex feedback signaling network that involves at least 29 proteins in Arabidopsis in the dynamic context remains a significant challenge. To address this, we transplanted an auxin-responsive feedback circuit responsible for plant organogenesis into yeast. By generating dynamic microfluidic conditions controlling gene expression, protein degradation, and binding affinity of auxin response factors to DNA, we illuminate feedback signal processing principles in hormone-driven gene expression. In particular, we recorded the regulatory mode shift between stimuli counting and rapid signal integration that is context-dependent. Overall, our study offers mechanistic insights into dynamic auxin response interplay trackable by synthetic gene circuits, thereby offering instructions for engineering plant architecture.

Particle-based dynamic occupancy maps were proposed in recent years to model the obstacles in dynamic environments. Current particle-based maps describe the occupancy status in discrete grid form and suffer from the grid size problem, wherein a large grid size is unfavorable for motion planning while a small grid size lowers efficiency and causes gaps and inconsistencies. To tackle this problem, this paper generalizes the particle-based map into continuous space and builds an efficient 3D egocentric local map. A dual-structure subspace division paradigm, composed of a voxel subspace division and a novel pyramid-like subspace division, is proposed to propagate particles and update the map efficiently with the consideration of occlusions. The occupancy status at an arbitrary point in the map space can then be estimated with the weights of the particles. To reduce the noise in modeling static and dynamic obstacles simultaneously, an initial velocity estimation approach and a mixture model are utilized. Experimental results show that our map can effectively and efficiently model both dynamic obstacles and static obstacles. Compared to the state-of-the-art grid-form particle-based map, our map enables continuous occupancy estimation and substantially improves the mapping performance at different resolutions., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Learning & Autonomous Control

The field of mobile robotics is a rapidly evolving space with a constant stream of new advancements. As the available technology matures and new features get accessible for lower prices, the scope of applicability for autonomous mobile robots naturally expands. Apart from simple home appliances, a particular area that currently attracts a surge of transformative research is that of industrial facilities and intelligent factories. A higher emphasis is gradually being placed on autonomous operations, where mobile robots start being suitable complements to human workers. Such an application, however, requires complex mechanisms to be able to handle even simple tasks, such as transferring cargo from one place to another while navigating a changing environment and avoiding dynamic obstacles. Robust localization and navigation solutions utilizing the most effective algorithms are necessary for such long and continuous operations that require a high degree of reliability and low error rates. Robot Operating System (ROS) is a highly flexible open-source framework enjoying wide adoption over the years. Its next-generation implementation (ROS2), together with the Navigation2 project (created as a successor to the original ROS Navigation Stack), provide substantial conceptual enhancements with a strong focus on reliability, security, and performance, promising better compatibility with numerous sensor types, higher modularity, and a more controllable deterministic behavior. This paper will guide the reader through necessary steps to deploy the Navigation2 stack onto an existing custom-built ROS2-operating robotic test-bed platform, serving as a concept for future research.

Humans and other biological bipedal walkers are extraordinarily agile and robust. This is especially apparent when certain features of the environment are unknown such as unexpected downsteps (for example, suddenly walking off the side-walk or not expecting the last stair when descending). Humans can negotiate these downsteps ---both expected and unexpected--- with remarkable agility and ease. The contributions from active compensation, passive dynamics, and reflexes that humans employ to overcome these downsteps are however not inherently present in bipedal robots. This motivates us to assess whether externally observed behavior can be used to achieve the same capabilities in bipedal robots. One of the challenges with this proposal is the morphological differences between humans and robots. Taking the bipedal robot Cassie as an example, these differences predominantly constitute to overall- and segment mass, leg morphology, and a lack of an upper body. The observed behavior from the human should subsequently be scaled to represent an appropriate nominal and compensatory behavior of the robot. This thesis aims to systematically study the translation of this human behavior to bipedal walking robots, regardless of the morphology of the walkers. We start from human experimental data where nominal walking, expected downstep, and unexpected downstep trials were conducted. The human data is analyzed from the perspective of a reduced-order representation of the human. The reduced-order representation encodes the center of mass dynamics and contact forces. An equivalent reduced-order model is used to represent the bipedal robot, which allows for the translation of the nominal walking and downstep behaviors between human and robot. The morphological differences between the human and the robot are therefore resolved by realizing dynamically equivalent behavior which is embedded into the full-order dynamics of a bipedal robot via optimization-based controllers. The re, Mechanical Engineering

Numerous applications in 3D underwater sensor networks (UWSNs), such as pollution detection, disaster prevention, animal monitoring, navigation assistance, and submarines tracking, heavily rely on accurate localization techniques. However, due to the limited batteries of sensor nodes and the difficulty for energy harvesting in UWSNs, it is challenging to localize sensor nodes successfully within a short sensor node lifetime in an unspecified underwater environment. Therefore, we propose the Adaptive Energy-Efficient Localization Algorithm (Adaptive EELA) to enable energy-efficient node localization while adapting to the dynamic environment changes. Adaptive EELA takes a fuzzy game-theoretic approach, whereby the Stackelberg game is used to model the interactions among sensor and anchor nodes in UWSNs and employs the adaptive neurofuzzy method to set the appropriate utility functions. We prove that a socially optimal Stackelberg-Nash equilibrium is achieved in Adaptive EELA. Through extensive numerical simulations under various environmental scenarios, the evaluation results show that our proposed algorithm accomplishes a significant energy reduction, e.g., 66% lower compared to baselines, while achieving a desired performance level in terms of localization coverage, error, and delay.

The global environment of organizations is changing. And with it the challenge for strategic decision makers to cope with economic, political, social, and technological shifts. Managers who are overly clinging to traditional methods of working will be hampered in their ability to make effective and successful decisions. This thesis provides an overview of how the professional identity of managers has evolved in the context of a dynamic environment and derives implications for potential qualities that might characterize the manager of tomorrow. Given that not only the nature of challenges but also the way managers address them has altered in recent years, a systematic literature review was chosen as the method to widen the lens of existing research by means of an integrative and problematizing analysis. The results indicate that managers, as cognitive, human, and social resources, are capable of developing dynamic capabilities that enable them to become adaptive decision makers and change ambassadors. Overall, the findings conclude that a dynamic environment drives the managerial practice where the manager’s artistic, craft and scientific skills serve as a catalyst for creative, meaningful, and sound solutions., submitted by David Smolcic, Masterarbeit University of Innsbruck 2022

Environmental variability often degrades the performance of algorithms designed to capture the global convergence of a given search space. Several approaches have been developed to challenge environmental uncertainty by incorporating biologically inspired notions, focusing on crossover, mutation, and selection. This study proposes a bio-inspired approach called NEAT-HD, which focuses on parent selection based on genetic similarity. The originality of the proposed approach rests on its use of a sigmoid function to accelerate species formation and contribute to population diversity. Experiments on two classic control tasks were performed to demonstrate the performance of the proposed method. The results show that NEAT-HD can dynamically adapt to its environment by forming hybrid individuals originating from genetically distinct parents. Additionally, an increase in diversity within the population was observed due to the formation of hybrids and novel individuals, which has never been observed before. Comparing two tasks, the characteristics of NEAT-HD were improved by appropriately setting the algorithm to include the distribution of genetic distance within the population. Our key finding is the inherent potential of newly formed individuals for robustness against dynamic environments.

The increasing number of successful latecomer enterprises has led to a growing research interest in the area, but there is a lack of consensus in academic circles on the relationship between imitation strategy and firm growth. While some enterprises achieved sustainable growth based on an imitation strategy, others withdrew from the market soon after their initial market entry. In this context, this meta-analysis synthesizes empirical findings including 23 independent samples (N = 66,110) to obtain evidence and explore the extent to which an imitation strategy affects firm growth. Moreover, by further examining the moderating effects of industry conditions, country-specific factors, and performance time horizons, this research also aims to address a complementary research question: in which context is imitation strategy more beneficial for the firm growth? We found that an imitation strategy is more effective in promoting firm growth in low-tech industries than in high-tech industries and in non-OECD countries than in OECD countries. It fosters the short-term performance rather than the long-term performance of a firm. Our research findings are meaningful for enterprises to choose an appropriate imitation strategy according to their unique attributes, enabling sustainable growth in a dynamic environment.

Positioning and stabilizing a catheter at the required location inside a vessel or the heart is a complicated task in interventional cardiology. In this review we provide a structured classification of catheter stabilization mechanisms to systematically assess their challenges during cardiac interventions. Commercially available, patented, and experimental prototypes of catheters were classified with respect to their stabilizing mechanisms. Subsequently, the classification was used to define requirements for future cardiac catheters and persisting challenges in catheter stabilization. The classification showed that there are two main stabilization mechanisms: surface-based and volume-based. Surface-based mechanisms apply attachment through surface anchoring, while volume-based mechanisms make use of locking through shape or force against the vessel or cardiac wall. The classification provides insight into existing catheter stabilization mechanisms and can possibly be used as a tool for future design of catheter stabilization mechanisms to keep the catheter at a specific location during an intervention. Additionally, insight into the requirements and challenges for catheter stabilization inside the heart and vasculature can lead to the development of more dedicated systems in the future, allowing for intervention- and patient-specific instrument manipulation., Medical Instruments & Bio-Inspired Technology

Currently, organizations must be prepared for the technological changes presented by their environment and anticipate these changes to adjust their technological strategy, this is achieved through technological foresight. The present work aims to analyze how technological foresight can be a dynamic capacity of an organization, specifically a research center. For the present investigation, the electrochemistry area of ​​the Center for Research and Technological Development in Electrochemistry (CIDETEQ) was taken as a case study. Semi-structured interviews were carried out with the main actors in charge of technological foresight. Five characteristics are identified in common between dynamic capabilities and technological foresight. These characteristics are: they are a built and not purchased capacity., En la actualidad, las organizaciones deben de estar preparadas para los cambios tecnológicos que les presenta su entorno y anticiparse a estos cambios para ajustar su estrategia tecnológica, esto se logra a través de la prospectiva tecnológica. El presente trabajo tiene como objetivo analizar como la prospectiva tecnológica puede ser una capacidad dinámica de una organización, específicamente de un centro de investigación. Para la presente investigación se tomó como caso de estudio el área de electroquímica del Centro de Investigación y Desarrollo Tecnológico en Electroquímica (CIDETEQ). Se realizaron entrevistas semiestructuradas a los actores principales encargados de la prospectiva tecnológica. Se identifican cinco características en común entre las capacidades dinámicas y la prospectiva tecnológica, estas características son: son una capacidad construida y no comprada, integran recursos, reconfiguran recursos, ganan recursos y permiten a las organizaciones adaptarse y crear cambios en el entorno. Se concluye que la prospectiva tecnológica es una capacidad dinámica construida capaz de integrar, reconfigurar y ganar recursos que permite adaptarse al entorno de una organización.

In order for the companies to be competitive in today’s market, it is vital to adapt quickly to the market trends. The steady shift towards mass customization from mass production has been challenging many industries globally, which demands the use of digital tools and technologies in various areas to improve performance throughout the supply chain processes. One of these areas is short-term scheduling of jobs on the shop floor. Short-term scheduling of jobs plays a very vital role in many production systems. Optimisation of short-term scheduling help the companies in improving their operational Key Performance Indicators (KPIs), thus saving both money and resources. Today’s complex production systems with multiple constraints, system level interactions and the dynamic environment have challenged the traditional static scheduling approaches. These complex production systems require new scheduling approaches which can consider all the dynamics and should be capable of real-time reconfiguring in case of uncertainties in the shop floor. In this thesis, a case study was performed in a steel manufacturing company over the period of five months to validate Discrete Event Simulation (DES) as a tool for short-term scheduling of heavy plates in a dynamic environment. The challenges related to DES for short-term scheduling during model design, development and implement phases were also identified. In addition to this, the requirements to implement DES model for short-term scheduling in a dynamic environment were also discussed. Furthermore, a Systematic Literature Review (SLR) was also conducted to support the empirical findings from the case study. The idea of this study was to generate an optimal schedule by minimizing overall makespan and maximizing resource utilization using DES model. The findings from both SLR and DES model has clearly proven that DES as a digital tool is exceptional for short-term scheduling in a dynamic environment, nevertheless there are still some c, För att företagen ska vara konkurrenskraftiga på dagens marknad är det viktigt att snabbt anpassa sig till marknadstrenderna. Vägen mot mass-anpassning från massproduktion har utmanat många industrier globalt, vilket kräver användning av digitala verktyg och tekniker inom olika områden för att förbättra prestandan under hela leveranskedjans processer. Ett av dessa områden är kortsiktig schemaläggning av arbeten på fabriksgolvet. Kortsiktig schemaläggning av arbeten spelar en mycket viktig roll i många produktionssystem. Optimering av kortsiktig schemaläggning hjälper företagen att förbättra sina operativa nyckeltal, vilket sparar både pengar och resurser. Dagens komplexa produktionssystem med flera begränsningar, systemnivåinteraktioner och den dynamiska miljön har utmanat de traditionella statiska schemaläggningsmetoderna. Dessa komplexa produktionssystem kräver nya schemaläggningsmetoder som kan ta hänsyn till all dynamik och bör ha möjligheten att omkonfigurera i realtid vid osäkerheter på fabriksgolvet. I denna avhandling genomfördes en fallstudie i ett ståltillverkningsföretag under fem månader för att validera Diskret händelsestyrd simulering som verktyg för kortsiktig schemaläggning av grovplåtstillverkning i en dynamisk miljö. Utmaningarna relaterade till Diskret händelsestyrd simulering för kortsiktig schemaläggning under modellens design, utveckling och implementeringsfaser identifierades också. Utöver detta diskuteras också kraven för att implementera Diskret händelsestyrd simulering för kortsiktig schemaläggning i en dynamisk miljö. Dessutom genomfördes en systematisk litteraturstudie för att stödja de empiriska resultaten från fallstudien. Tanken med den här studien var att generera ett optimalt schema genom att minimera den totala schemalängden och maximera resursutnyttjandet med hjälp av Diskret händelsestyrd simuleringsmodellen. Resultaten från både den systematiska litteraturöversynen och Diskreta händelse simuleringsmodellen har tydligt bevisat att

The task of exploration does not end when the robot has covered the entire environment. The world is dynamic and to model this property and to keep the map up to date the robot needs to re-explore. In this work, we present an approach to long-term exploration that builds on prior work on dynamic mapping, volumetric representations of space, and exploration planning. The main contribution of our work is a novel formulation of the information gain function that controls the exploration so that it trades off revisiting highly dynamic areas where changes are very likely with covering the rest of the environment to ensure both coverage and up-to-date estimates of the dynamics. We provide experimental validation of our approach in three different simulated environments., QC 20210818

I takt med att organisationers omgivande miljö beskrivs som alltmer dynamisk, ökar kritiken mot den budgeteringen, då budget anses var ett statiskt ekonomistyrningsverktyg. Trots medhållet som kritiken får visar empiriska studier att få företag faktiskt överger budget som huvudsakligt planerings- och kontrollverktyg (Sandalgaard 2012; Ekholm & Wallin 2000; Dokulil, Zlámalová & Popesko 2017). Till följd av Covid-19-pandemin under år 2020 förlorande många marknader sin förutsägbarhet vilket resulterade i osäkra omgivningar för många organisationer och branscher. Bilförsäljningen visade en hög variation under pandemiåret. Först tappade marknaden 40% av försäljningen och några månader senare fick den uppgång som täckte upp för de föregående förlorade intäkterna. Marknaden upplevde därmed en berg-och-dal-bana som ger insikt i hur budgetens praktiska användning sammanfaller med kritiserade svagheter. Genom att använda fem av de främsta argumenten i kritiken mot budgetering undersöker studien hur budgeten påverkas hos svenska bilåterförsäljare. Detta illustrerar behovet som bilåterförsäljarna har i en bransch vars omgivning påverkats av en oväntad osäkerhet. Sju bilåterförsäljare kontaktades och intervjuades i syfte att tillhandahålla den empiriska datan som i efterhand kompletterades genom de utvalda återförsäljarnas årsredovisningar. Genom detta urvalet utforskar studien en variation i behovet av budgeten. Studien kommer bland annat fram till att budgeten och dess komplement samspelar för att tillgodose de observerade bilåterförsäljarnas individuella behov av planering och kontroll., The critique of budgeting has been growing louder during recent years as the influencing factors in the organizational environment is increasingly described as dynamic, while budgeting itself is seen as a static appliance in organizational management. However, despite the support this critique receives, empirical evidence shows that few companies actually abandon budgeting as one of their main tools for planning and control in financial management (Sandalgaard 2012; Ekholm & Wallin 2000; Dokulil, Zlámalová & Popesko 2017). Due to the Covid-19 pandemic year 2020, the wide consumer market lost its predictability, thus making it an uncertain environment for a majority of companies to act in. Sales of cars showed a wide variety during the pandemic year. First a decline of 40% in sales in the overall market and then an upswing which made up for previous loss. The car market had therefore experienced an interchangeable environment which contribute to giving insight to how the praxis of the budget coincide with the criticism. Using five of the main points in the critique against budgeting, the research area of this study, car retailers, were chosen to illuminate the need for the budget of retailers in an industry unexpectedly affected by uncertainty in the environment. Seven car retailer companies were contacted and interviewed to provide the empirical data which were later supplemented by the chosen companies’ annual reports. Through this selection this study explores a variety in the need for the budget. The results include that there is an interaction between the budget and the budget complement to satisfy the observed car retailers' individual needs of planning and control. This study is written in Swedish.

[EN] A necessity in the design of a path planning algorithm is to account for the environment. If the movement of the mobile robot is through a dynamic environment, the algorithm needs to include the main constraint: real-time collision avoidance. This kind of problem has been studied by different researchers suggesting different techniques to solve the problem of how to design a trajectory of a mobile robot avoiding collisions with dynamic obstacles. One of these algorithms is the artificial potential field (APF), proposed by O. Khatib in 1986, where a set of an artificial potential field is generated to attract the mobile robot to the goal and to repel the obstacles. This is one of the best options to obtain the trajectory of a mobile robot in real-time (RT). However, the main disadvantage is the presence of deadlocks. The mobile robot can be trapped in one of the local minima. In 1988, J.F. Canny suggested an alternative solution using harmonic functions satisfying the Laplace partial differential equation. When this article appeared, it was nearly impossible to apply this algorithm to RT applications. Years later a novel technique called proper generalized decomposition (PGD) appeared to solve partial differential equations, including parameters, the main appeal being that the solution is obtained once in life, including all the possible parameters. Our previous work, published in 2018, was the first approach to study the possibility of applying the PGD to designing a path planning alternative to the algorithms that nowadays exist. The target of this work is to improve our first approach while including dynamic obstacles as extra parameters.

Folgende Masterthesis beschäftigt sich mit dem Thema der Führungskompetenzen in einer volatilen, unsicheren, komplexen und mehrdeutigen Welt, kurz VUCA genannt. Ziel dieser Arbeit ist es, Führungskompetenzen aufzuzeigen, die heute und in Zukunft wichtig sind, um erfolgreich führen zu können. Die zentrale Fragestellung lautet wie folgt: Welche Kompetenzen brauchen Führungskräfte in einer VUCA-Welt, um heute und in Zukunft erfolgreich zu sein? Um diese Frage beantworten zu können, erfolgt zunächst eine theoretische Auseinandersetzung auf drei Ebenen. Auf erster Ebene sollen die zentralen Einflussfaktoren, mit denen sich Organisationen konfrontiert sehen, behandelt werden. Auf der nächsten Ebene werden die Auswirkungen auf die Führungskräfte analysiert. Die letzte Ebene beschäftigt sich mit den Kompetenzen und im Speziellen mit den Führungskompetenzen und deren Entwicklung. Die empirische Untersuchung versucht, die zentrale Fragestellung zu beantworten. Das Ergebnis stellt ein Kompetenzprofil in Form des KODE-Modells dar. Dieses Kompetenzprofil soll Aufschluss darüber geben, auf welchen Teil der Kompetenzen der Fokus in der Führungskräfteentwicklung gelegt werden sollte. *****This Masters thesis deals with leadership competences in a VUCA world. VUCA describes our environment as volatile, uncertain, complex and ambiguous. The aim of this thesis is to determine which leadership skills are both currently required as well as in the future. The main question deals with the topic: Which leadership skills are needed in a VUCA world that allows a leader to be successful both today and in the future? In order to answer the previous mentioned question a theoretical analysis is carried out dealing with three different topics. First, main driving forces that companies are dealing with, will be discussed. Further, effects of the volatile environment that influence leaders, will be analyzed. The last part deals with the general topic competences, in particular with focus on leaders

The usage of calibration curves for the determination of microalgae concentration is common, and it consists of a correlation between the optical density and the number of cells. Although this tool is very useful for determining the concentration of microalgae, it requires the generation of a new calibration curve when the environmental conditions (e.g., temperature, light, etc.) change, since the size of microalgae may vary from one condition to another. We propose a novel methodology to determine the number of microalgae in a solution, emphasizing a correlation with optical density and the size distribution of cells. Considering that microalgal physiology is affected by environmental conditions, the proposed procedure allows for the calculation of the concentration of microalgae in changing ambient conditions without requiring the preparation of additional calibration curves. © 2019 Elsevier B.V.

The leaf, as the vital part of plants, can be affected by physical and physiological factors which might lead to changes in its shape, color and size. These unique parts play an essential role in the design and implementation of plant recognition systems, as the shapes of leaves vary among different plants. Weather type and related factors, such as light intensity, humidity, temperature and wind-speed, may have effects on the number of leaves that grow on a plant, the amount of fresh and dried leaves, the deformation of leaves, color of leaves, positions of leaves on branches, etc. In addition, photographing in outdoor environments with different weather conditions has undesired impacts on images. For instance, light scattering changes severely when there are water droplets during photographing which influences the images captured in rainy weather. Despite the importance of the proposed factors and the relevant effects on the plants, leaves and images taken from plants, a plant recognition system should be independent from all environmental and non-environmental factors to be practically useful and applicable for identifying plant species in uncontrolled outdoor environments. Moreover, changes in the time of day (morning, noon and evening), the distance between camera and plant species as well as the angle and illumination of the object and environment when the images are taken should be considered in the process of plant recognition. For instance, in a windy weather condition, for an observer even from a short distance, it is challenging to distinguish all single leaves and identify the plant type from the shape of its leaf. Furthermore, the unstructured background of the images is another challenge which affects the images captured in fields and outdoor environments. These are only some difficulties for identification of plants in natural environments such as farms, forests, etc. A consideration of mentioned factors contributes to developing a novel, efficient and

The usage of calibration curves for the determination of microalgae concentration is common, and it consists of a correlation between the optical density and the number of cells. Although this tool is very useful for determining the concentration of microalgae, it requires the generation of a new calibration curve when the environmental conditions (e.g., temperature, light, etc.) change, since the size of microalgae may vary from one condition to another. We propose a novel methodology to determine the number of microalgae in a solution, emphasizing a correlation with optical density and the size distribution of cells. Considering that microalgal physiology is affected by environmental conditions, the proposed procedure allows for the calculation of the concentration of microalgae in changing ambient conditions without requiring the preparation of additional calibration curves.

Bakgrund och problem: Vi lever idag i ett samhälle som till stor grad karaktäriseras av förändring och osäkerhet där snabba förändringar i den externa omgivningen tenderar att göra budgeteringen mindre användbar. Till följd av detta har budgeteringen som styrmedel varit hårt kritiserad. Trots den kritik som budgeteringen utsatts för har budgeten alltjämt än idag en viktig roll i företagets styrning. Vi finner en kunskapslucka i forskning som berör budgetering i en dagens mer dynamiska omgivning och som även inkluderar styrelsens roll i budgeteringen. Syfte: Det övergripande syftet med denna studie är att beskriva och förstå budgeteringens användning och syfte i företag som är verksamma i dagens dynamiska omgivning och som på grund av olika faktorer har svårigheter att förutse framtiden. Vidare är syftet att beskriva styrelsens roll i budgetarbetet för en mer omfattande förståelse, då hänsyn tas till hela styrkedjan. Metod: I denna studie tillämpas en kvalitativ forskningsstrategi varigenom en flerfallsstudie utförts. Studiens empiri grundas främst i semistrukturerade intervjuer med respondenter som har insikt i företagens budgetering. Slutsats: Budgetering används idag främst för att gå igenom verksamheten noggrant men används även genom att verka motiverande då delaktighet och engagemang främjas. Det ligger en större användning i den process varigenom budgeteringen sker än i budgeten som dokument. Med anledning av den dynamiska omgivning som företag verkar i och svårigheter som däri ligger att förutse framtiden finns ändå användning av budgetering om man arbetar på rätt sätt och har rätt inställning till budgeten. Styrelsen har en aktiv och betydande roll i företags budgetering, under förutsättningen att styrelsen aktivt verkar som en styrelse. I budgetarbetet har styrelsen en bestämmande roll som främst urskiljs vid budgetförutsättningar samt vid fastställande och uppföljning av budgeten. Ett extra fokus på strategirollen kan urskiljas varigenom denna roll också u, Background: Today, we live in a society that is largely characterized by change and uncertainty where rapid changes in the external environment tend to make budgeting less useful. As a result, budgeting has been heavily criticized. However, despite the criticisms of budgeting, the budget still has an important role in the management of the company. We find a knowledge gap in research that concerns budgeting in today's more dynamic environment and also includes the board's role in budgeting. Purpose: The purpose of this study is to describe and understand the use and purpose of budgeting in companies that are active in today's dynamic environment and due to various factors have difficulties predicting the future. Furthermore, the purpose is to describe the role of the board in the budget work for a more comprehensive understanding, taking into account the entire control chain. Method: A qualitative research strategy is applied in this study, whereby a multiple case study is conducted. The empirical study is based primarily on semi-structured interviews with respondents who have insight into the companies’ budgeting. Conclusions: Today's budgeting is used primarily to review the business thoroughly but it is also used by acting motivating as participation and dedication are promoted. There is a greater use in the process whereby budgeting takes place than in the budget as a document. However, due to the dynamic environment that companies engage in and difficulties in anticipating the future, budgeting is still available if you work properly and have the right attitude to it. The board has an active and significant role in budgeting, provided that the board actively acts as a board. In the budget work, the board has a determining role that is primarily distinguished by budget conditions, as well as in determining and reviewing the budget. An extra focus on the strategy role can be identified, whereby this role is also distinguished in budgeting by linking budget and st

Micro Aerial Vehicles (MAVs) are increasingly being used for aerial transportation in remote and urban spaces where portability can be exploited to reach previously inaccessible and inhospitable spaces. Current approaches to MAV swung payload system path planning have primarily focused on pre-generating (agile) collision-free, or conservative minimal-swing trajectories in static environments. However, these approaches have failed to address the prospect of online re-planning in uncertain and dynamic environments which is a prerequisite for real-world deployability. This article describes a novel Non-Linear Model Predictive Controller (NMPC) for online, agile and closed-loop local trajectory planning and control addressing the limitations mentioned of contemporary approaches. We integrate the controller in a full system framework and demonstrate the algorithm’s effectiveness in simulation and experimental studies. Results show the scalability and adaptability of our method to various dynamic setups with repeatable performance over several complex tasks which include flying through a narrow opening and avoiding moving humans., Aerospace Engineering | Control & Simulation

Recent industrial developments in autonomous systems, or agents, which assume that humans and the agents share the same space or even work in close proximity, open for new challenges in robotics, especially in motion planning and control. In these settings, the control system should be able to provide these agents a reliable path following control when they are working in a group or in collaboration with one or several humans in complex and dynamic environments. In such scenarios, these agents are not only moving to reach their goals, i.e., locations, they are also aware of the movements of other entities to find a collision-free path. Thus, this paper proposes a dependable, i.e, safe, reliable and effective, path planning algorithm for a group of agents that share their working space with humans. Firstly, the method employs the Theta* algorithm to initialize the paths from a starting point to a goal for a set of agents. As Theta* algorithm is computationally heavy, it only reruns when there is a significant change of the environment. To deal with the movements of the agents, a static flow field along the configured path is defined. This field is used by the agents to navigate and reach their goals even if the planned trajectories are changed. Secondly, a dipole field is calculated to avoid the collision of agents with other agents and human subjects. In this approach, each agent is assumed to be a source of a magnetic dipole field in which the magnetic moment is aligned with the moving direction of the agent. The magnetic dipole-dipole interactions between these agents generate repulsive forces to help them to avoid collision. The effectiveness of the proposed approach has been evaluated with extensive simulations. The results show that the static flow field is able to drive agents to the goals with a small number of requirements to update the path of agents. Meanwhile, the dipole flow field plays an important role to prevent collisions. The combination of these tw

With customers demanding more and more holistic answers to their problems, solution providers respond with complex systems, integrating product, service and ICT elements into their offer. These solutions need to be aligned to a high number of requirements, coming not only from the individual customer but also from an environment of network partners, technology providers and other stakeholders. Especially for Product-Service Systems, where the solution provider takes responsibility in the operational phase, this environment is dynamic over the system life cycle. Stakeholders may enter or leave, as well as changing needs and technological capabilities. This makes the requirements towards the solution volatile, demanding a suitable Requirements Engineering approach. In this paper, it is discussed how environmental dynamicity can be monitored for its effect on requirements, with a special focus on organizational issues. Through a literature review and industrial case studies it is analysed, how it can be ensured that environmental changes can be taken into account in Requirements Engineering, leading to an optimal system configuration to address the customer problem., QC 20171030

Among various ranging techniques, Radio Signal Strength (RSS) based approaches attract intensive research interests because of its low cost and wide applicability. RSS-based ranging is prone to be affected by the multipath phenomenon which allows the radio signals to reach the destination through multiple propagation paths. To address this issue, previous works try to profile the environment and refer this profile during run-time. In a practical dynamic environment, however, the profile frequently changes and the painful retraining is needed. Rather than such static ways of profiling the environments, in this paper, we try to accommodate the environmental dynamics automatically in real-time. The key observation is that given a pair of nodes, the RSS at different spectrum channels will be different. This difference carries the valuable phase information of the radio signals. By analyzing these RSS values, we are able to identify the amplitude of signals solely from the Line-of-Sight (LOS) path. This LOS amplitude is a simple function of the path length (the physical distance). We find that the analysis is a typical non-linear curvature fitting problem that has no general routing algorithms. We prove that, this problem format is ill-conditioned which has no stable and trustable solutions. To deal with this issue, we further explore the practical considerations for the problem and modify it to a greatly improved conditioning shape. We solve the problem by numerical iterations and implement these ideas in a real-time indoor tracking system called MuD. MuD employs only three TelosB nodes as anchors. The experiment results show that in a dynamic environment where five people move around, the averaged localization error is about 1 meter. Compared with the traditional RSS-based approaches in dynamic environments, the accuracy improves up to 10 times.

Quadrotor UAV's have become extremely popular over the last decade, as they combine great agility with mechanic simplicity. For tasks such as area surveillance or maintaining a communication network, the deployment of multi-agent systems is required. In these scenarios, it often is necessary for the robot teams to navigate in formation while avoiding static and dynamic obstacles. In situations, where team keeping is not crucial it can be beneficial to split and merge robot teams. This could be the case, if multiple targets are supposed to be tracked, or due to obstacle avoidance in order to improve goal progress and help avoiding deadlocks. Within this thesis a novel approach for splitting and merging of robot teams moving in formation is developed. The method extends an existing geometric method for local formation control, which uses the approximation of typically non-convex workspaces by obstacle free convex regions. The regions are used to constrain a non-linear cost function, which is minimized through Sequential Convex Programming (SCP). This determines the references position for the robots and furthermore allows for the adjustment of the formation shape according to the environment. This thesis presents a goal directed version of the obstacle-free convex regions. These regions are used to determine team splitting and merging based on region intersections. The method takes into account limited sensing and communication range of the agents. Furthermore computations are done in a distributed manner to allow for better scaling with the number of team members. The presented method is validated through simulations in various scenarios. Furthermore, an experimental framework is recreated and experiments with up to four quadrotor UAVs of the type Parrot Bebop 2 are conducted. The approach shows sufficient real-time capabilities in static and dynamic environments, while maintaining safe navigation of the drones.

Interactive robot doing collaborative work in hybrid work cell need adaptive trajectory planning strategy. Indeed, systems must be able to generate their own trajectories without colliding with dynamic obstacles like humans and assembly components moving inside the robot workspace. The aim of this paper is to improve collision-free motion planning in dynamic environment in order to insure human safety during collaborative tasks such as sharing production activities between human and robot. Our system proposes a trajectory generating method for an industrial manipulator in a shared workspace. A neural network using a supervised learning is applied to create the waypoints required for dynamic obstacles avoidance. These points are linked with a quintic polynomial function for smooth motion which is optimized using least-square to compute an optimal trajectory. Moreover, the evaluation of human motion forms has been taken into consideration in the proposed strategy. According to the results, the proposed approach is an effective solution for trajectories generation in a dynamic environment like a hybrid workspace.

Previous research on situation awareness (SA) predominantly focused on its explicit, reasoned, conscious features rather than on the implicit, intuitive, unconscious aspects that are often identified with expert operators. This research investigated implicit levels of SA of train traffic controllers (TTCs) in order to contribute to the body of knowledge on rail human factors research and SA. A novel approach was used to uncover levels of implicit SA through a set of three analyses: (1) fairly low SAGAT values with correlations between SAGAT scores and multiple performance indicators; (2) negative correlations between work experience and SAGAT scores; and (3) structurally lower level-1 SA (perception) scores in comparison to level-2 SA (comprehension) scores in accordance with Endsley's three-level model. Two studies were conducted: A pilot study which focused on SA measurements with TTCs in a monitoring mode (N = 9) and the main study, which involved TTCs from another control center (N = 20) and three different disrupted conditions. In the pilot study, SA was measured through the situation-awareness global assessment technique (SAGAT), perceived SA and observed SA, and performance was measured through punctuality and unplanned stops of trains before red signals. In the main study, SA was measured through SAGAT, and perceived SA and multiple performance indicators, such as arrival and departure punctuality and platform consistency, were assessed. In both studies, the set of three analyses showed consistent and persistent indications of the presence of implicit SA. Endsley's three-level model and related SAGAT method can be constrained by the presence of these intuitive, unconscious processes and inconsistent findings on correlations between SAGAT scores and performance. These findings provide insights into the SA of TTCs in the Netherlands and can support the development of training programs and/or the design of a new traffic management system., QC 20170102

Radio frequency (RF) based technologies play an important role in indoor localization, since Radio Signal Strength (RSS) can be easily measured by various wireless devices without additional cost. Among these, radio map based technologies (also referred as fingerprinting technologies) are attractive due to high accuracy and easy deployment. However, these technologies have not been extensively applied on real environment for two fatal limitations. First, it is hard to localize multiple objects. When the number of target objects is unknown, constructing a radio map of multiple objects is almost impossible. Second, environment changes will generate different multipath signals and severely disturb the RSS measurement, making laborious retraining inevitable. Motivated by these, in this paper, we propose a novel approach, called Line-of-sight radio map matching, which only reserves the LOS signal among nodes. It leverages frequency diversity to eliminate the multipath behavior, making RSS more reliable than before. We implement our system MODLoc based on TelosB sensor nodes and commercial 802.11 NICs with Channel State Information (CSI) as well. Through extensive experiments, it shows that the accuracy does not decrease when localizing multiple targets in a dynamic environment. Our work outperforms the traditional methods by about 60 percent. More importantly, no calibration is required in such environment. Furthermore, our approach presents attractive flexibility, making it more appropriate for general RF-based localization studies than just the radio map based localization.

Radio frequency (RF) based technologies play an important role in indoor localization, since Radio Signal Strength (RSS) can be easily measured by various wireless devices without additional cost. Among these, radio map based technologies (also referred as fingerprinting technologies) are attractive due to high accuracy and easy deployment. However, these technologies have not been extensively applied on real environment for two fatal limitations. First, it is hard to localize multiple objects. When the number of target objects is unknown, constructing a radio map of multiple objects is almost impossible. Second, environment changes will generate different multipath signals and severely disturb the RSS measurement, making laborious retraining inevitable. Motivated by these, in this paper, we propose a novel approach, called Line-of-sight radio map matching, which only reserves the LOS signal among nodes. It leverages frequency diversity to eliminate the multipath behavior, making RSS more reliable than before. We implement our system MODLoc based on TelosB sensor nodes and commercial 802.11 NICs with Channel State Information (CSI) as well. Through extensive experiments, it shows that the accuracy does not decrease when localizing multiple targets in a dynamic environment. Our work outperforms the traditional methods by about 60 percent. More importantly, no calibration is required in such environment. Furthermore, our approach presents attractive flexibility, making it more appropriate for general RF-based localization studies than just the radio map based localization.

Evolutionary computation is an effective tool for solving optimization problems. However, its significant computational demand has limited its real-time and on-line applications, especially in embedded systems with limited computing resources, e.g., mobile robots. Heuristic methods such as the genetic algorithm (GA) based approaches have been investigated for robot path planning in dynamic environments. However, research on the simulated annealing (SA) algorithm, another popular evolutionary computation algorithm, for dynamic path planning is still limited mainly due to its high computational demand. An enhanced SA approach, which integrates two additional mathematical operators and initial path selection heuristics into the standard SA, is developed in this work for robot path planning in dynamic environments with both static and dynamic obstacles. It improves the computing performance of the standard SA significantly while giving an optimal or near-optimal robot path solution, making its real-time and on-line applications possible. Using the classic and deterministic Dijkstra algorithm as a benchmark, comprehensive case studies are carried out to demonstrate the performance of the enhanced SA and other SA algorithms in various dynamic path planning scenarios.

How do honey bees track changes in their foraging environment? Previously, 2 complementary mechanisms have been identified by which bees can effectively switch between food sources when their relative quality changes. First, an increase in profitability of a food source elicits an increase in waggle dances (the bees' recruitment mechanism) for that source. Second, bees that have retired from foraging at a food source make occasional inspection visits to that food source and resume foraging if its quality improves. Here, we investigate, using both field experiments and a mathematical model, the relative importance of these 2 mechanisms. By manipulating dance information available to the bees, we find that when food sources change quality frequently, inspector bees provide a rapid response to changes, whereas the waggle dance contributes to a response over a longer time period. The bees' ability to switch feeders without dance language information was found to be robust with respect to the spatial configuration of the feeders. Our results show that individual memory, in the form of inspector bees, and collective communication can interact to allow an insect colony to adapt to changes on both short and long timescales.

Radio Frequency (RF) based technologies play an important role in indoor localization, since Radio Signal Strength (RSS) is easily achieved by various wireless devices without additional cost. Among these, radio map based technologies (also referred as fingerprinting technologies) are attractive. They are able to accurately localize the targets without introducing many reference nodes. Therefore, their hardware cost is low. However, this technology has two fatal limitations. First, it is hard to localize multiple objects, since radio map has to collect all the RSS information when targets are at different possible positions. But due to the multipath phenomenon, different number of target nodes at different positions often generates different multipath signals. So when the target object number is unknown, constructing a radio map of multiple objects is almost impossible. Second, environment changes will generate different multipath signals and severely disturb the RSS measurement, making laborious retraining inevitable. In this paper, we propose a novel method, called Line-Of-Sight (LOS) map matching. It leverages frequency diversity of wireless nodes to eliminate the multipath behavior, making RSS more reliable than before. These reliable RSS signals are able to construct the radio map, which only reserves the LOS signal among nodes. We call it LOS radio map. The number of objects and environment changes will not affect the LOS signal between the targets and reference nodes. Such map is able to be constructed easily and require no training if reference nodes are carefully pre-deployed. Our basic idea is to utilize the frequency diversity of each wireless node to transmit data in different spectrum channel. Then it solves the optimization problem to get the LOS signal. Our experiments are based on TelosB sensor platform with three reference nodes. It shows that the accuracy will not decrease when localizing multiple targets in a dynamic environment. It outperforms th

Successful and high precision localization is an important feature for autonomous vehicles in an urban environment. GPS solutions are not good on their own and laser, sonar and radar are often used as complementary sensors. Localization with these sensors requires the use of techniques grouped under the acronym SLAM (Simultaneous Localization And Mapping). These techniques work by comparing the current sensor inputs to either an incrementally built or known map, also adding the information to the map.Most of the SLAM techniques assume the environment to be static, which means that dynamics and clutter in the environment might cause SLAM to fail. To ob-tain a more robust algorithm, the dynamics need to be dealt with. This study seeks a solution where measurements from different points in time can be used in pairwise comparisons to detect non-static content in the mapped area. Parked cars could for example be detected at a parking lot by using measurements from several different days.The method successfully detects most non-static objects in the different test datasets from the sensor. The algorithm can be used in conjunction with Pose-SLAM to get a better localization estimate and a map for later use. This map is good for localization with SLAM or other techniques since only static objects are left in it.

Product platforms are applied in order to increase the stability of the product architecture and to reap benefits of scale and scope. However, many firms face a dynamic environment with quickly changing market demands. In such an environment, firms may have to continuously renew and improve their product platforms to achieve desired flexibility. The modifications comprise two types of innovations, component as well as architectural innovations. Combining these types to simultaneously achieve stability and strategic flexibility demands continuous innovation capability. This paper provides a viable approach to coordinate architectural and component innovations. The paper is based on a case study of an international truck manufacturer. Two major issues, decision-making and the management of knowledge, stand out as important for realising continuous innovation and improvement of product platforms. Moreover, product platform management needs to consider modularity and its related economies of substitution., QC 20111121

In today’s competitive business world, every company wants to be more profitable. In order to keep the profit margin high, the companies need to work more. It is very hard to find any company which works on a single project. Actually it is not only any specific company’s practice rather it is a practice of the entire human being to do multiple works at the same time. The practice has also become quite common in the companies. Although the practice of working on multiple projects simultaneously has become popular for the companies overtime, the important thing is to manage all the projects efficiently so that all projects can be finished according to the plan. Multiple project management deals with a lot of challenges in terms of resource and time. The facts like resource sharing and resource constraint add more complexity to the management of such multiple simultaneous projects. It is difficult to understand the correct workload during the project and the effects of running all the simultaneous projects to the project deployment time. The situation becomes even critical if the projects run in a dynamic environment which may provide changes during any phase of it. Software development is such a process that deals with multiple projects and also the dynamic environment. The master study will focus on such case where the study has conducted in a software development company named ActionWave Development AB. ActionWave Development AB is dealing with multiple projects with having resource constraint. In this study, readers will come to know about the challenges of ActionWave Development AB in terms of resource scenario and deployment time in case of managing multiple projects simultaneously. The study will analyze the effect of having multiple simultaneous projects to the limited resource of ActionWave Development AB and will also show the effects on projects deployment time. The study does the analysis for the current case and an imaginary test case. Later solution has b

In this paper we investigate the foraging activity of an invasive ant species, the big headed ant Pheidole megacephala. We establish that the ants' behavior is consistent with the use of two different pheromone signals, both of which recruit nestmates. Our experiments suggest that during exploration the ants deposit a long-lasting pheromone that elicits a weak recruitment of nestmates, while when exploiting food the ants deposit a shorter lasting pheromone eliciting a much stronger recruitment. We further investigate experimentally the role of these pheromones under both static and dynamic conditions and develop a mathematical model based on the hypothesis that exploration locally enhances exploitation, while exploitation locally suppresses exploration. The model and the experiments indicate that exploratory pheromone allows the colony to more quickly mobilize foragers when food is discovered. Furthermore, the combination of two pheromones allows colonies to track changing foraging conditions more effectively than would a single pheromone. In addition to the already known causes for the ecological success of invasive ant species, our study suggests that their opportunistic strategy of rapid food discovery and ability to react to changes in the environment may have strongly contributed to their dominance over native species.

This paper proposes a simulated annealing based approach to determine the optimal or near-optimal path quickly for a mobile robot in dynamic environments with static and dynamic obstacles. The approach uses vertices of the obstacles to define the search space. It processes off-line computation based on known static obstacles, and re-computes the route online if a moving obstacle is detected. The contributions of the work include the employment of the simulated annealing algorithm for robot path planning in dynamic environments, and the development of a new algorithm planner for enhancement of the efficiency of the path planning algorithm. The effectiveness of the proposed approach is demonstrated through simulations under typical dynamic environments and comparisons with existing methods.

This paper is concerned with multi-robot hunting in dynamic environments. A BCSLA approach is proposed to allow mobile robots to capture an intelligent evader. During the process of hunting, four states including dispersion-random-search, surrounding, catch and prediction are employed. In order to ensure each robot appropriate movement in each state, a series of strategies are developed in this paper. The dispersion-search strategy enables the robots to find the evader effectively. The leader-adjusting strategy aims to improve the hunting robots’ response to environmental changes and the outflank strategy is proposed for the hunting robots to force the evader to enter a besieging circle. The catch strategy is designed for shrinking the besieging circle to catch the evader. The predict strategy allows the robots to predict the evader’s position when they lose the tracking information about the evader. A novel collision-free motion strategy is also presented in this paper, which is called the direction-optimization strategy. To test the effect of cooperative hunting, the target to be captured owns a safety-motion strategy, which helps it to escape being captured. The computer simulations support the rationality of the approach.

Det är viktigt med ett effektivt samarbete och en välfungerande kommunikation i dynamiska miljöer, till exempel vid släckningsarbetet av en brand. Bristande kommunikation i en sådan miljö skulle kunna få förödande konsekvenser. Denna uppsats bygger på material från studien ”Bridging Cultural Barriers to Collaborative Decision Making in On-Site Operations Coordination Centers”, där 32 svenskar samarbetade i olika lagkonstellationer med att släcka simulerade eldar i mikrovärlden C3Fire. Kommunikationsteorier visar på att personer samtalar med varandra efter en viss struktur; deras yttranden tenderar att komma i par, så kallade närhetspar. Denna studie syftade till att undersöka närhetspar i beställningssekvenser, i form av en beställning av vatten och/eller bränsle följd av en bekräftelse. Till sin hjälp hade försöksdeltagarna en datorskärm med en karta samt tillgång till ett e-postverktyg för att kommunicera inom laget. Varje person genomgick åtta spelomgångar, vilket resulterade i totalt 64 spelomgångar. De lag som lyckades släcka elden antogs ha större andel närhetspar i beställningssekvenserna, vilket innebar att de skulle bekräfta varandras beställningar mer kontinuerligt än de lag som inte lyckades släcka elden. För studiet av närhetsparen kategoriserades e-postmeddelandena från samtliga spelomgångar, för att se hur strukturen i kommunikationen såg ut. Resultatet visade på att något annat måste ha påverkat de medverkandes prestation, eftersom det inte fanns någon påtaglig skillnad i andelen närhetspar i beställningssekvenserna mellan de olika lagen. Det uppmärksammades att de inte bekräftade varandras beställningar endast genom e-postmeddelanden utan även genom direkta manipulationer. Anledningen till detta antas vara att alla lagmedlemmar blev presenterade samma information på sina datorskärmar, vilket frambringade en mer implicit kommunikation., Efficient communication and cooperation is important in dynamic environments, for instance in fire-fighting teamwork. A lack of communication in this environment could cause devastating consequences. This Master’s thesis is based on material from ”Bridging Cultural Barriers to Collaborative Decision Making in On-Site Operations Coordination Centers”, a study where 32 Swedes collaborated in teams conducting fire-fighting exercises in the micro-world C3Fire. Communication theories propose that people talk with each other after a certain structure; their utterances tend to come in pairs, so-called adjacency pairs. The purpose of this study was to examine adjacency pairs in request sequences, in form of a request for water and/or fuel followed by a confirmation. The participants had access to a computer screen with a map together with an e-mail tool, to communicate within the team. Every person participated in eight rounds of the game, which resulted in 64 rounds of the game altogether. The teams who managed to extinguish the fire were assumed to have a larger amount of adjacency pairs in their request sequences than the teams who did not extinguish the fire. For the study of the adjacency pairs, the e-mails were categorized, to see what the structure of the communication looked like. The result showed that some other factor must have influenced the participants’ performance, since there was no obvious difference in the amount of the adjacency pairs in the request sequence between the teams. It was observed that the team members did not confirm each other’s requests just by sending e-mail, but also by direct manipulations. The reason for this is assumed to be that every member of the team was shown the same information on his computer screen, which gave rise to a more implicit communication.

Real environments in which agents operate are inherently dynamic - the environment changes beyond the agents' control. We advocate that, for multi-agent simulation, this dynamism must be modeled explicitly as part of the simulated environment, preferably using concepts and constructs that relate to the real world. In this paper, we describe such concepts and constructs, and we provide a formal framework to unambiguously specify their relations and meaning. We apply the formal framework to model a dynamic RoboCup Soccer environment and elaborate on how the framework poses new challenges for exploring the modeling of environments in multi-agent simulation.

This paper is an experimental study investigating the capability of Self-Organizing Migrating Algorithm (SOMA) to solve dynamic problems. We have proposed an extension of SOMA algorithm based on the phenomenon of the limited lifetime of population individuals to keep the population diversity during the environment changes. We tested the performance of the algorithm on three representative dynamic benchmarks including moving peak, the 3rd de Jong function and two-extremes time-varying function. The experimental results confirmed the capability of the proposed dynamic SODOMA algorithms to effectively adapt the search process towards the nonstationary global optimum.

The goal of this project was to develop new techniques for the construction, description, and analysis of multi-agent systems. The characteristics of systems being addressed include a high degree of non-determinism (resulting form a large number of interactions) and unpredictability of the environment in which the agents operate. Specifically, we implemented tools for building robust and dependable large-scale multi-agent systems and studied methods for predicting and analyzing the behaviors of such systems. The project developed coordination methods for systems consisting of large numbers of agents., The original document contains color images. Sponsored in part by DARPA.

The manufacturing processes in today's decentralized manufacturing systems are rather complex, especially at shop floors where highly mixed products in small batch sizes are handled simultaneously. In addition to the fluctuating job shop operations, unpredictable issues like job delay, urgent job insertion, fixture shortage, missing tool, and even machine break-down, are regularly challenging the job shop operations. Targeting the fluctuation, this research proposes a DPP (distributed process planning) approach to generate process plans that are responsive and adaptive to the changes. In this paper, a concept of enriched machining feature is introduced, based on which several rules and algorithms are formulated for generic machining sequence generation. It is expected that the new approach can largely enhance the dynamism of fluctuating job shop operations., QC 20170904

The current study aimed at investigating the concepts of team situation awareness, mental models, and team processes in relation to performance in the complex, dynamic environment of breathing apparatus rescues. Data was collected during exercises at Ågesta training center through questionnaires and after action reviews. 28 firemen and 5 instructors participated in the exercises. Also, a stimulated recall interview was conducted with 2 of the firemen that had participated in one of the exercises. The different data collection methods all indicated that well-developed mental models or a high degree of pre-task knowledge affected performance in a positive way. Moreover, a multiple regression analysis showed that both pre-task knowledge and team processes significantly can predict performance. The results of the analysis of team situation awareness in relation to performance were fairly ambiguous. Therefore, further research is needed to establish the relation between these concepts in the domain at matter.

This paper presents a self- adapting approach to global level path planning in dynamic environments. The aim of this work is to minimize risk and delays in possible applications of mobile robots ( e. g., in industrial processes). We introduce a hybrid system that uses case-based reasoning as well as grid-based maps for decision-making. Maps are used to suggest several alternative paths between specific start and goal point. The casebase stores these solutions and remembers their characteristics. Environment representation and casebase design are discussed. To solve the problem of exploration vs. exploitation, a decision-making strategy is proposed that is based on the irreversibility of decisions. Forgetting strategies are discussed and evaluated in the context of case-based maintenance. The adaptability of the system is evaluated in a domain based on real sensor data with simulated occupancy probabilities. Forgetting strategies and decision-making strategies are evaluated in simulated environments. Experiments show that a robot is able to adapt in dynamic environments and can learn to use paths that are less risky to follow.

Supervisor:櫻井 彰人, 知識科学研究科, 修士

Maria Dolores Romero Morales was born on Augustus 5th, 1971, in Sevilla (Spain). She studied Mathematics at University of Sevilla from 1989 to 1994 and specialized in Statistics and Operations Research. She wrote her Master's thesis on Global Optimization in Location Theory under the supervision of Dr. Emilio Carrizosa Priego and Dr. Eduardo Conde S?nchez. During the academic year 1995-1996 she was assistant professor at the department of Mathematics of the Business School, University of C?diz, Spain. From September 1996 to July 2000, she has been research assistant at the department of Decision and Information Sciences of the Faculty of Business Administration, Erasmus University, The Netherlands. During the second semester of the academic year 1997-1998, she was assistant professor at the department of Statistics and Operations Research of the Faculty of Mathematics, University of Sevilla, Spain., The goal of this thesis is the study of optimization models, which integrate both transportation and inventory decisions, to search for opportunities for improving the logistics distribution network. In contrast to Anily and Federgruen, who also study a model integrating these two aspects in a tactical-operational setting, we utilize the models to answer strategic and tactical questions. We evaluate an estimate of the total costs of a given design of the logistics distribution network, including production, handling, inventory holding, and transportation costs. The models are also suitable for clustering customers with respect to the warehouses, and through this they can be used as a first step towards estimating operational costs in the logistics distribution network related to the daily delivery of the customers in tours. The main focus of this thesis is the search for solution procedures for these optimization models. Their computational complexity makes the use of heuristics solution procedures for large-size problem instances advisable. We will look for feasible solutions with a class of greedy heuristics. For small or medium-size problem instances, we will make use of a Branch and Price scheme. Relevant characteristics of the performance of a solution procedure are the computation time required and the quality of the solution obtained if an optimal solution is not guaranteed. Conclusions about these characteristics are drawn by testing it on a collection of problem instances. The validity of the derived conclusions strongly depends on the set of problem instances chosen for this purpose. Therefore, the second focus of this thesis is the generation of experimental data for these optimization models to test these solution methods adequately. The well-known generalized assignment problem (GAP) can be seen as a static model to evaluate a two-level logistics distribution network where production and storage take place at the same location. Moreover, some of the dyn