Thesis (MEng)--Stellenbosch University, 2014. / ENGLISH ABSTRACT: The logical allocation of tasks in engineering offices currently relies heavily on the experience and intuition of project managers. In large scale projects the complexity of the task allocation procedure exceeds the capacity of human intuition, and a systematic technique is required to aid project managers in assigning tasks to individuals. In this project such a systematic technique is modelled and implemented using the Java programming language. An equation was developed to calculate an individual’s workload, and used in conjunction with other criteria to intelligently and systematically select an optimal individual to complete engineering tasks. The software solution is network-based, and also aims to aid project managers in various managerial duties. / AFRIKAANSE OPSOMMING: Die logiese toekenning van ingenieurstake steun tans swaar op die ervaring en aanvoeling van projek bestuurders. In grootskaalse projekte is die kompleksiteit van die taak toekenningsproses veel groter as die kapasiteit van menslike intuïsie. Dus word ‘n sistematiese proses wat projek-bestuurders kan help met die toeken van take aan individue vereis. In hierdie projek is so 'n sistematiese tegniek ontwikkel en geïmplementeer met behulp van die Java-programmeringstaal. 'n Vergelyking is ontwikkel om 'n individu se werklading te bereken en is in samewerking met ander kriteria gebruik om take intelligent en sistematies toe te ken. Die sagteware is network en databasis-gebaseerd en kan ook gebruik word om projek-bestuurders te help met verskeie bestuurspligte.

O Brasil é uma referência mundial na produção e exportação de citros, entretanto esse cultivo pode sofrer diversos problemas e perdas de produtividade por motivos diversos, como por exemplo, pragas. Para reduzir os riscos e perdas, torna-se interessante o uso de sistemas automatizados de monitoramento, justificando a necessidade de realizar a coleta de dados para determinar diversos fatores. Determinadas plantações, como a de citros, não podem ser monitoradas somente via solo ou somente via imagens aéreas, tornando necessário mesclar ambas as abordagens de acordo com o parâmetro a ser monitorado. Para a realização desse monitoramento devem ser utilizados robôs com habilidades distintas, robôs aéreos e robôs terrestres. Assim, é preciso designar as tarefas que cada robô realizará e também coordenar todos os robôs durante a execução do sistema como um todo, visando otimizar o processo de coleta de dados. Esse problema pode ser analisado e modelado como um problema de alocação de tarefas para robôs (Multi-Robot Task Allocation (MRTA)). Para resolver esse problema propõe-se um framework baseado em técnicas de cobertura de conjuntos e em mecanismo de mercado baseado em leilão. Teste simulados são realizados e demonstram que a presente proposta cumpre o papel na alocação das tarefas aos robôs. Além disso, visando a aplicação da solução proposta é projetado e desenvolvido uma plataforma robótica aérea (quadrirotor) de baixo custo utilizando peças prototipadas. Para o controle de estabilidade dessa plataforma, propõe-se um modelo matemático de acordo com os parâmetros inerciais do quadrirotor. Esse quadrirotor é utilizado em diversas aplicações reais, mostrando que o projeto desenvolvido pode ser reproduzido e destinado a execução de tarefas reais, como por exemplo a coleta de dados na agricultura de precisão. / Brazil is a world reference in the production and export of citrus, although this crop can suffer several problems and losses of productivity for diverse reasons, as for example, pests. In order to reduce risks and losses, it is interesting to use automated monitoring systems, justifying the need to perform data collection to determine several factors. Certain plantations, such as citrus plantations, can not be monitored only via soil or only via aerial images, making it necessary to merge both approaches according to the parameter to be monitored. To perform this monitoring, robots with different abilities, such asunmanned aerial vehicle (UAV) and unmanned ground vehicle (UCV) should be used. Therefore, it is necessary to assign the tasks that each robot will perform and also to coordinate all the robots during the execution of the system as a whole, in order to optimize the process of data collection. The problem can be studied and modeled as a task allocation problem for robots (MRTA). To solve this problem we propose a framework based set covering techniques and auction-based market mechanism. Simulated tests are performed and demonstrate that the present proposal fulfills the role in assigning tasks to robots. In addition, aiming at the application of the proposed solution is designed and developed a low cost aerial robotic platform (quadrirotor) which use prototyped parts. This quadrirotor is used in several real applications, showing that the developed project can be reproduced and destined to perform real tasks, such as data collection in precision agriculture.

Autonomy in multi-robot systems is bounded by coordination among its agents. Coordination implies simultaneous task decomposition, task allocation, team formation, task scheduling and routing; collectively termed as task planning. In many real-world applications of multi-robot systems such as commercial cleaning, delivery systems, warehousing and inventory management: spatial & temporal constraints, variable execution time, and energy limitations need to be integrated into the planning module. Spatial constraints comprise of the location of the tasks, their reachability, and the structure of the environment; temporal constraints express task completion deadlines. There has been significant research in multi-robot task allocation involving spatio-temporal constraints. However, limited attention has been paid to combine them with team formation and non- instantaneous task execution time. We achieve team formation by including quota constraints which ensure to schedule the number of robots required to perform the task. We introduce and integrate task activation (time) windows with the team effort of multiple robots in performing tasks for a given duration. Additionally, while visiting tasks in space, energy budget affects the robots operation time. We map energy depletion as a function of time to ensure long-term operation by periodically visiting recharging stations. Research on task planning approaches which combines all these conditions is still lacking. In this thesis, we propose two variants of Team Orienteering Problem with task activation windows and limited energy budget to formulate the simultaneous task allocation and scheduling as an optimization problem. A complete mixed integer linear programming (MILP) formulation for both variants is presented in this work, implemented using Gurobi Optimizer and analyzed for scalability. This work compares the different objectives of the formulation like maximizing the number of tasks visited, minimizing the total distance travelled, and/or maximizing the reward, to suit various applications. Finally, analysis of optimal solutions discover trends in task selection based on the travel cost, task completion rewards, robot's energy level, and the time left to task inactivation.

Cette thèse s'inscrit dans le cadre du projet Cart-O-Matic mis en place pour participer au défi CAROTTE (CArtographie par ROboT d'un TErritoire) organisé par l'ANR et la DGA. Le but de ce défi est de construire une carte en deux et trois dimensions et de localiser des objets dans un environnement inconnu statique de type appartement. Dans ce contexte, l'utilisation de plusieurs robots est avantageuse car elle permet d'augmenter l'efficacité en temps de la couverture. Cependant, comme nous le montrons, le gain est conditionné par le niveau de coopération entre les robots. Nous proposons une stratégie de coopération pour une cartographie multirobot efficace. Une difficulté est la construction d'une carte commune, nécessaire, afin que chaque robot puisse connaître les zones de l'environnement encore inexplorées. Pour obtenir une bonne coopération avec un algorithme simple nous proposons une technique de déploiement fondée sur le choix d'une cible par chaque robot. L'algorithme proposé cherche à distribuer les robots vers différentes directions. Il est fondé sur le calcul partiel de champs de potentiels permettant à chaque robot de calculer efficacement son prochain objectif. En complément de ces contributions théoriques, nous décrivons le système robotique complet mis en oeuvre au sein de l'équipe Cart-O-Matic ayant permis de remporter la dernière édition du défi CAROTTE / This thesis is part of Cart-O-Matic project set up to participate in the challenge CARROTE (mapping of a territory) organized by the ANR and the DGA. The purpose of this challenge is to build 2D and 3D maps of a static unknown 'apartment-like' environment. In this context, the use of several robots is advantageous because it increases the time efficiency to discover fully the environment. However, as we show, the gain is determined by the level of cooperation between robots. We propose a cooperation strategy for efficient multirobot mapping. A difficulty is the construction of a common map, necessary so that each robot can know the areas of the environment which remain unexplored.For a good cooperation with a simple algorithm we propose a deployment technique based on the choice of a target by each robot. The proposed algorithm tries to distribute the robots in different directions. It is based on calculation of the partial potential fields allowing each robot to compute efficiently its next target. In addition to these theoretical contributions, we describe the complete robotic system implemented in the Cart-O-Matic team that helped win the last edition of the CARROTE challenge

Large display environments (LDEs) are interactive physical workspaces featuring one or more static large displays as well as rich interaction capabilities, and are meant to visualize and manipulate very large datasets. Research about mid-air interactions in such environments has emerged over the past decade, and a number of interaction techniques are now available for most elementary tasks such as pointing, navigating and command selection. However these techniques are often designed and evaluated separately on specific platforms and for specific use-cases or operationalizations, which makes it hard to choose, compare and combine them.In this dissertation I propose a framework and a set of guidelines for analyzing and combining the input and output channels available in LDEs. I analyze the characteristics of LDEs in terms of (1) visual output and how it affects usability and collaboration and (2) input channels and how to combine them in rich sets of mid-air interaction techniques. These analyses lead to four design requirements intended to ensure that a set of interaction techniques can be used (i) at a distance, (ii) together with other interaction techniques and (iii) when collaborating with other users. In accordance with these requirements, I designed and evaluated a set of mid-air interaction techniques for panning and zooming, for invoking commands while pointing and for performing difficult pointing tasks with limited input requirements. For the latter I also developed two methods, one for calibrating high-precision techniques with two levels of precision and one for tuning velocity-based transfer functions. Finally, I introduce two higher-level design considerations for combining interaction techniques in input-constrained environments. Designers should take into account (1) the trade-off between minimizing limb usage and performing actions in parallel that affects overall performance, and (2) the decision and adaptation costs incurred by changing the resolution function of a pointing technique during a pointing task.

A Inteligência de Enxame foi proposta a partir da observação do comportamento social de espécies de insetos, pássaros e peixes. A ideia central deste comportamento coletivo é executar uma tarefa complexa decompondo-a em tarefas simples, que são facilmente executadas pelos indivíduos do enxame. A realização coordenada destas tarefas simples, respeitando uma proporção pré-definida de execução, permite a realização da tarefa complexa. O problema de alocação de tarefas surge da necessidade de alocar as tarefas aos indivíduos de modo coordenado, permitindo o gerenciamento do enxame. A alocação de tarefas é um processo dinâmico pois precisa ser continuamente ajustado em resposta a alterações no ambiente, na configuração do enxame e/ou no desempenho do mesmo. A robótica de enxame surge deste contexto de cooperação coletiva, ampliada à robôs reais. Nesta abordagem, problemas complexos são resolvidos pela realização de tarefas complexas por enxames de robôs simples, com capacidade de processamento e comunicação limitada. Objetivando obter flexibilidade e confiabilidade, a alocação deve emergir como resultado de um processo distribuído. Com a descentralização do problema e o aumento do número de robôs no enxame, o processo de alocação adquire uma elevada complexidade. Desta forma, o problema de alocação de tarefas pode ser caracterizado como um processo de otimização que aloca as tarefas aos robôs, de modo que a proporção desejada seja atendida no momento em que o processo de otimização encontre a solução desejada. Nesta dissertação, são propostos dois algoritmos que seguem abordagens distintas ao problema de alocação dinâmica de tarefas, sendo uma local e a outra global. O algoritmo para alocação dinâmica de tarefas com abordagem local (ADTL) atualiza a alocação de tarefa de cada robô a partir de uma avaliação determinística do conhecimento atual que este possui sobre as tarefas alocadas aos demais robôs do enxame. O algoritmo para alocação dinâmica de tarefas com abordagem global (ADTG) atualiza a alocação de tarefas do enxame com base no algoritmo de otimização PSO (Particle swarm optimization). No ADTG, cada robô possui uma possível solução para a alocação do enxame que é continuamente atualizada através da troca de informação entre os robôs. As alocações são avaliadas quanto a sua aptidão em atender à proporção-objetivo. Quando é identificada a alocação de maior aptidão no enxame, todos os robôs do enxame são alocados para as tarefas definidas por esta alocação. Os algoritmos propostos foram implementados em enxames com diferentes arranjos de robôs reais demonstrando sua eficiência e eficácia, atestados pelos resultados obtidos. / Swarm Intelligence has been proposed based on the observation of social behavior of insect species, birds and fishes. The main idea of this collective behavior is to perform a complex task decomposing it into many simple tasks, that can be easily performed by individuals of the swarm. Coordinated realization of these simple tasks while adhering to a pre-defined distribution of execution, allows for the achievement of the original complex task. The problem of task allocation arises from the need of assigning tasks to individuals in a coordinated fashion, allowing a good management of the swarm. Task allocation is a dynamic process because it requires a continuous adjustment in response to changes in the environment, the swarm configuration and/or the performance of the swarm. Swarm robotics emerges from this context of collective cooperation applied to swarms of real robots. In this approach, complex problems are solved by performing complex tasks using swarms of simple robots, with a limited processing and communication capabilities. Aiming at achieving flexibility and reliability, the allocation should emerge as a result of a distributed process. With the decentralization of the problem and the increasing number of robots in the swarm, the allocation process acquires a high complexity. Thus, the problem of task allocation can be characterized as an optimization process that assigns tasks to robots, so that the desired proportion is met at the end of the optimization process, find the desired solution. In this dissertation, we propose two algorithms that follow different to the problem of dynamic task allocation approaches: one is local and the other global. The algorithm for dynamic allocation of tasks with a local approach (ADTL) updates the task assignment of each robot based on a deterministic assessment of the current knowledge it has so far about the tasks allocated to the other robots of the swarm. The algorithm for dynamic task allocation with a global approach (ADTG) updates the allocation of tasks based on a swarm optimization process, inspired by PSO (Particle swarm optimization). In ADTG, each robot has a possible solution to the swarm allocation, which is continuously updated through the exchange of information between the robots. The allocations are evaluated for their fitness in meeting the goal proportion. When the allocation of highest fitness in the swarm is identified, all robots of the swarm are allocated to the tasks defined by this allocation. The proposed algorithms were implemented on swarms of different arrangements of real robots demonstrating their efficacy, robustness and efficiency, certified by obtained the results.

A Inteligência de Enxame foi proposta a partir da observação do comportamento social de espécies de insetos, pássaros e peixes. A ideia central deste comportamento coletivo é executar uma tarefa complexa decompondo-a em tarefas simples, que são facilmente executadas pelos indivíduos do enxame. A realização coordenada destas tarefas simples, respeitando uma proporção pré-definida de execução, permite a realização da tarefa complexa. O problema de alocação de tarefas surge da necessidade de alocar as tarefas aos indivíduos de modo coordenado, permitindo o gerenciamento do enxame. A alocação de tarefas é um processo dinâmico pois precisa ser continuamente ajustado em resposta a alterações no ambiente, na configuração do enxame e/ou no desempenho do mesmo. A robótica de enxame surge deste contexto de cooperação coletiva, ampliada à robôs reais. Nesta abordagem, problemas complexos são resolvidos pela realização de tarefas complexas por enxames de robôs simples, com capacidade de processamento e comunicação limitada. Objetivando obter flexibilidade e confiabilidade, a alocação deve emergir como resultado de um processo distribuído. Com a descentralização do problema e o aumento do número de robôs no enxame, o processo de alocação adquire uma elevada complexidade. Desta forma, o problema de alocação de tarefas pode ser caracterizado como um processo de otimização que aloca as tarefas aos robôs, de modo que a proporção desejada seja atendida no momento em que o processo de otimização encontre a solução desejada. Nesta dissertação, são propostos dois algoritmos que seguem abordagens distintas ao problema de alocação dinâmica de tarefas, sendo uma local e a outra global. O algoritmo para alocação dinâmica de tarefas com abordagem local (ADTL) atualiza a alocação de tarefa de cada robô a partir de uma avaliação determinística do conhecimento atual que este possui sobre as tarefas alocadas aos demais robôs do enxame. O algoritmo para alocação dinâmica de tarefas com abordagem global (ADTG) atualiza a alocação de tarefas do enxame com base no algoritmo de otimização PSO (Particle swarm optimization). No ADTG, cada robô possui uma possível solução para a alocação do enxame que é continuamente atualizada através da troca de informação entre os robôs. As alocações são avaliadas quanto a sua aptidão em atender à proporção-objetivo. Quando é identificada a alocação de maior aptidão no enxame, todos os robôs do enxame são alocados para as tarefas definidas por esta alocação. Os algoritmos propostos foram implementados em enxames com diferentes arranjos de robôs reais demonstrando sua eficiência e eficácia, atestados pelos resultados obtidos. / Swarm Intelligence has been proposed based on the observation of social behavior of insect species, birds and fishes. The main idea of this collective behavior is to perform a complex task decomposing it into many simple tasks, that can be easily performed by individuals of the swarm. Coordinated realization of these simple tasks while adhering to a pre-defined distribution of execution, allows for the achievement of the original complex task. The problem of task allocation arises from the need of assigning tasks to individuals in a coordinated fashion, allowing a good management of the swarm. Task allocation is a dynamic process because it requires a continuous adjustment in response to changes in the environment, the swarm configuration and/or the performance of the swarm. Swarm robotics emerges from this context of collective cooperation applied to swarms of real robots. In this approach, complex problems are solved by performing complex tasks using swarms of simple robots, with a limited processing and communication capabilities. Aiming at achieving flexibility and reliability, the allocation should emerge as a result of a distributed process. With the decentralization of the problem and the increasing number of robots in the swarm, the allocation process acquires a high complexity. Thus, the problem of task allocation can be characterized as an optimization process that assigns tasks to robots, so that the desired proportion is met at the end of the optimization process, find the desired solution. In this dissertation, we propose two algorithms that follow different to the problem of dynamic task allocation approaches: one is local and the other global. The algorithm for dynamic allocation of tasks with a local approach (ADTL) updates the task assignment of each robot based on a deterministic assessment of the current knowledge it has so far about the tasks allocated to the other robots of the swarm. The algorithm for dynamic task allocation with a global approach (ADTG) updates the allocation of tasks based on a swarm optimization process, inspired by PSO (Particle swarm optimization). In ADTG, each robot has a possible solution to the swarm allocation, which is continuously updated through the exchange of information between the robots. The allocations are evaluated for their fitness in meeting the goal proportion. When the allocation of highest fitness in the swarm is identified, all robots of the swarm are allocated to the tasks defined by this allocation. The proposed algorithms were implemented on swarms of different arrangements of real robots demonstrating their efficacy, robustness and efficiency, certified by obtained the results.

O Brasil é uma referência mundial na produção e exportação de citros, entretanto esse cultivo pode sofrer diversos problemas e perdas de produtividade por motivos diversos, como por exemplo, pragas. Para reduzir os riscos e perdas, torna-se interessante o uso de sistemas automatizados de monitoramento, justificando a necessidade de realizar a coleta de dados para determinar diversos fatores. Determinadas plantações, como a de citros, não podem ser monitoradas somente via solo ou somente via imagens aéreas, tornando necessário mesclar ambas as abordagens de acordo com o parâmetro a ser monitorado. Para a realização desse monitoramento devem ser utilizados robôs com habilidades distintas, robôs aéreos e robôs terrestres. Assim, é preciso designar as tarefas que cada robô realizará e também coordenar todos os robôs durante a execução do sistema como um todo, visando otimizar o processo de coleta de dados. Esse problema pode ser analisado e modelado como um problema de alocação de tarefas para robôs (Multi-Robot Task Allocation (MRTA)). Para resolver esse problema propõe-se um framework baseado em técnicas de cobertura de conjuntos e em mecanismo de mercado baseado em leilão. Teste simulados são realizados e demonstram que a presente proposta cumpre o papel na alocação das tarefas aos robôs. Além disso, visando a aplicação da solução proposta é projetado e desenvolvido uma plataforma robótica aérea (quadrirotor) de baixo custo utilizando peças prototipadas. Para o controle de estabilidade dessa plataforma, propõe-se um modelo matemático de acordo com os parâmetros inerciais do quadrirotor. Esse quadrirotor é utilizado em diversas aplicações reais, mostrando que o projeto desenvolvido pode ser reproduzido e destinado a execução de tarefas reais, como por exemplo a coleta de dados na agricultura de precisão. / Brazil is a world reference in the production and export of citrus, although this crop can suffer several problems and losses of productivity for diverse reasons, as for example, pests. In order to reduce risks and losses, it is interesting to use automated monitoring systems, justifying the need to perform data collection to determine several factors. Certain plantations, such as citrus plantations, can not be monitored only via soil or only via aerial images, making it necessary to merge both approaches according to the parameter to be monitored. To perform this monitoring, robots with different abilities, such asunmanned aerial vehicle (UAV) and unmanned ground vehicle (UCV) should be used. Therefore, it is necessary to assign the tasks that each robot will perform and also to coordinate all the robots during the execution of the system as a whole, in order to optimize the process of data collection. The problem can be studied and modeled as a task allocation problem for robots (MRTA). To solve this problem we propose a framework based set covering techniques and auction-based market mechanism. Simulated tests are performed and demonstrate that the present proposal fulfills the role in assigning tasks to robots. In addition, aiming at the application of the proposed solution is designed and developed a low cost aerial robotic platform (quadrirotor) which use prototyped parts. This quadrirotor is used in several real applications, showing that the developed project can be reproduced and destined to perform real tasks, such as data collection in precision agriculture.

This dissertation consists of three chapters, which focus jointly on the effects of education policy on the functioning of labor markets.De-industrialization and technological progress have changed job markets fundamentally. The most fundamental change is that the concept of a worker as a unit of production relatively insensitive to inherent characteristics has been overthrown. Service sectors that have taken over from manufacturing as the engines of economic activity rely primarily on human capital for autonomous production. This is especially true for internationally tradable services. Their stark development was rendered possible by the informationcommunication revolution. Skills and talent, as well as their allocation to suitable tasks matter for production, now more than ever. We argue in this dissertation that the ability of education policy to facilitate optimal task allocation plays a role in maximizing aggregate production and in influencing education earnings premia, as well as employment volumes in various sectors of activity. / Doctorat en Sciences économiques et de gestion / info:eu-repo/semantics/nonPublished

The overarching goal of this thesis is to investigate and explore the barriers that a company would face while increasing the Levels of Automation (LoA), in the preassembly production unit. To achieve the primary goal of investigating the barriers this study takes a threefold approach. Firstly, the current LoA was measured for the preassembly workstations. This measurement was conducted by incorporating an existing methodology adapted from the literature review known as DYNAMO++ methodology. This method is incorporated such that, the current LoA of the preassembly workstations could be measured and analysed. The current LoA of the preassembly workstations are analysed to investigate the potential workstations where LoA could be increased, in line with the company’s triggers for implementing automation. For this, experiences of the personnel’s belonging to the operational level of preassembly workstations were incorporated, to find the scope of improvements for increasing the LoA. Additionally, the company’s triggers for implementing automation was investigated from the managerial level. The research questions were answered by adapting an explorative, single case study method. Additionally, four types of data collection techniques were used, such as – interviews, focus groups, observations, and document analysis. Subsequently, source triangulation was adapted to analyse the data collected; to develop a comprehensive understanding of the barriers identified., Finally, the barriers faced by the company to increase LoA are identified by considering the implications that the improvement opportunities would impose upon the production systems environment. The identified barriers were then categorised further based on factors that exist internal as well as external to the production systems environment. The barriers identified in this study highlights various factors that the management must consider beforehand while initiating automation decisions in future automation projects in the preassembly area. Regardless of the barriers faced by the company, there are more opportunities to improve manufacturing processes through automation technologies. This thesis contributes to the knowledge of the factors that restrain the implementation of automation technologies and how companies could deal with it.