Orientador: Marconi Kolm Madrid / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-15T23:54:05Z (GMT). No. of bitstreams: 1 Nicolato_Fabricio_D.pdf: 1516280 bytes, checksum: 96229803f3bca54f669d4dcc22108c02 (MD5) Previous issue date: 2007 / Resumo: Esta tese trata o problema da resolução do modelo cinemático inverso para manipuladores industriais redundantes ou não. O problema foi abordado por um método de busca heurística no qual a solução da cinemática inversa é construída passo a passo calculando-se a contribuição do movimento de apenas uma junta a cada iteração. Dessa forma, o problema n-dimensional é transformado em problemas unidimensionais mais simples, cuja solução analítica tanto para juntas rotacionais quanto para juntas prismáticas é apresentada em termos da representação de Denavit-Hartenberg. O método proposto não possui singularidades internas. Além disso, o método foi expandido para incorporar informações de sensores externos visando fazer com que o processo seja mais robusto a incertezas nas modelagens envolvidas. Foram realizadas diversas simulações e comparações com técnicas tradicionais que evidenciaram as vantagens da abordagem proposta. O trabalho também englobou o projeto e a construção de um ambiente experimental e a implementação das técnicas desenvolvidas na parte teórica. Desenvolveu-se um sistema com um robô planar redundante de 3 DOF, assim como seus sistemas de controle, acionamento e interfaceamento usando técnicas de sistemas hardware-inthe-loop e lógica programável. As técnicas desenvolvidas foram aplicadas no ambiente experimental demonstrando características como: facilidade de lidar com redundâncias, capacidade de resolução em tempo real, robustez a incertezas de parâmetros etc / Abstract: This thesis deals with the problem of solving the inverse kinematics model of redundant and nonredundant industrial manipulators. The work was developed in a theoretical and a practical part. The problem was approached by an heuristic search method in which the solution of the inverse kinematics is built step by step calculating the movement contribution of just a single joint for each iteration. In that way, the n-dimensional problem is transformed in simpler one-dimensional problems, whose analytic solution for both rotational joints and prismatic joints is presented in terms of the Denavit and Hartenberg representation. The proposed method does not possess internal singularities. Furthermore, the method was expanded to incorporate information of external sensor in order to make the process more robust to uncertainties in the involved modelings. Several results of simulations and comparisons with traditional techniques, which evidence the advantages of the proposed approach, are presented. The work also included the construction of an experimental environment and the implementation of the techniques developed in the theoretical part. The details of a system with a 3-DOF redundant robot as well as its control system, drivers and interfaces using hardware-in-theloop techniques and programmable logic are presented. The developed techniques were applied in the experimental environment are demonstrating their efficiency and evidencing characteristics like: easiness of dealing with redundancies, real time capacity, robustness for parameters uncertainties etc / Doutorado / Automação

Modern day technological advancements are moving at a rapid pace. In the field of Artificial Intelligence, algorithms are becoming ever faster and process larger amounts of data. These fast algorithms call for data structures that can store this processed data compactly. This premise also holds true in the AI subfield of planning. In the common planning approach of state space search, found states are memorized as to not unnecessarily revisit them. Research has put a big focus on improving the speed of state space searches which in turn leads to a lot of states being stored. A crucial bottleneck then occurs when memory runs out due to storing these large amounts of states. This is where this project, with its exploration of compact state set representations, comes into the picture. This project's focus is on exploring memory usage for planning by state space search. More specifically, the project investigates compact state set representations for an A* state space search's closed- and open lists. It was hypothesized that the closed list would be the larger of the two which is why a focus was put on testing compact representations of that state set. Results from this project confirm this hypothesis as it is shown that the closed list is the largest and most critical of the two (although the differences between the two become increasingly small for strong heuristics).  Four different state set representations were tested for use as closed lists in an A* algorithm: Level-Ordered Edge Sequences (LOES), compressed LOES (cLOES), Binary Decision Diagram (BDD) and an explicit representation. A primary focus was put on exploring the LOES data structure because of the limited amount of research done on the data structure. Explicit representation was used as the main point of comparison with it being a very commonly used standard in state space search. The results from this project show that LOES managed to lower memory usage significantly for large tasks when compared to the explicit representation. The lower memory usage did, however, come at the cost of speed with LOES being noticeably slower. Although less drastic, the same differences could be seen when comparing LOES to its compressed version, cLOES. Out of all the tested state set representations, cLOES was shown to be the most compact but also the slowest. Moreover, the results indicated that, even if most tasks didn't benefit from the additional compression provided by cLOES in comparison to normal LOES, the tasks that did, benefited a lot. Lastly, the BDD data structure gave more inconclusive results. The poor BDD results were seemingly caused by an unfit implementation for the closed list use case. The results did, however, suggest that BDD was faster but less compact than LOES for large tasks. The different closed list state set representations were also tested with four different heuristics: blind, max, CEGAR and Merge-and-Shrink heuristic. A takeaway from these tests was that stronger heuristics resulted in fewer states being stored in the open- and closed list. Moreover, the closed states made up a smaller portion of the total amount states for the stronger heuristics. This smaller number of stored closed states made, as a consequence, the differences between the tested state set representations less pronounced. For large tasks, however, the closed list did get big enough to experience the effect of the efficient closed list implementations. Conclusively, LOES and cLOES proved strong replacements to explicit representation. Especially in use cases where compactness is more critical than speed such as in embedded systems. Additionally, even though strong heuristics lessened the effect of efficient state set representations, there are still notable advantages to be found for big tasks where the closed list grows large enough.

In Artificial Intelligence, especially in area of constraint programming, it's popular to design various modeling languages which allow solving problems on domain level and by using domain specific abstractions. Techniques known from research on Domain-Specific Languages are often useful in this effort. Functional programming languages offer new tools for designing such languages, particularly Domain-Specific Embedded Languages. This work investigates the advantages and disadvantages of using functional programming for designing and implementing a Domain-Specific Embedded Language for state space search problems.

The diploma thesis deals with the use of a simulation model for the development of an automatic algorithm for the creation of a routing table and route evaluation in a transport system. It includes a search of modeling and simulation issues and state-space search issues with an analysis of available search methods. The simulation model of the transport system is created in the software Tecnomatix Plant Simulation, where an algorithm for automatic creation of routing tables for routing and evaluation of routes according to static criteria is subsequently developed and tested. The work presents a proposal for the principle of the algorithm for evaluating the routes of the transport system, including dynamic criteria and their optimization.