{"id":{"repo_id":"brazil-ufrn","oai_identifier":"oai:repositorio.ufrn.br:123456789/48351"},"canonical_url":"https://search.dev.ndltd.org/etd/brazil-ufrn/oai:repositorio.ufrn.br:123456789/48351","repository":{"repo_id":"brazil-ufrn","name":"Brazil UFRN","base_url":"https://repositorio.ufrn.br/server/oai/request"},"display":{"title":"Controle inteligente de um robô móvel utilizando modos deslizantes, redes neurais artificiais e aprendizagem por reforço","abstract":"Research on intelligent and autonomous mobile robots has grown significantly due to its military, civil and industrial applications, such as the monitoring of agricultural plantations, the use in actions to support environmental disasters, border patrol, mapping of submarine territories or even the study of animal behavior. This work rescues the multi and interdisciplinary motivation of artificial intelligence, starting from philosophical questions to reach the characterization of intelligent and autonomous systems. Thus, only after building the theoretical bases for the concept of these agents, a bioinspired approach is presented for the trajectory tracking task by a omnidirectional mobile robot, the Robotino® produced by Festo® . For this purpose, the strategy consists of robust non-linear intelligent control using Sliding Modes, artificial neural networks and the Upper Confidence Bound algorithm. Each of these fundamentals techniques are presented, in order to justify, in advance, their consistent use with the theoretical proposal, to be later incorporated into the controller. Thus, Sliding Modes and their limitations regarding residual error are presented; artificial neural networks are then applied with the purpose of reducing them, however, they also have their restrictions; the Upper Confidence Bound is therefore added in order to mitigate them. The characteristics of each technique give the robot robustness in the control task, learning and autonomy with decision-making, respectively, as explained from the numerical and experimental results. The designed algorithm not only achieved the purposes, but also brought other positive points, such as avoiding the neural networks divergence resulting from the continuous updating of their weights. The approach developed based on the most recent arguments about autonomous agents obtained excellent results in both simulations and in experiments for the Robotino® trajectory tracking problem and represents the growing trend of research in embodied cognitive science.","abstract_html":"Research on intelligent and autonomous mobile robots has grown significantly due to its military, civil and industrial applications, such as the monitoring of agricultural plantations, the use in actions to support environmental disasters, border patrol, mapping of submarine territories or even the study of animal behavior. This work rescues the multi and interdisciplinary motivation of artificial intelligence, starting from philosophical questions to reach the characterization of intelligent and autonomous systems. Thus, only after building the theoretical bases for the concept of these agents, a bioinspired approach is presented for the trajectory tracking task by a omnidirectional mobile robot, the Robotino® produced by Festo® . For this purpose, the strategy consists of robust non-linear intelligent control using Sliding Modes, artificial neural networks and the Upper Confidence Bound algorithm. Each of these fundamentals techniques are presented, in order to justify, in advance, their consistent use with the theoretical proposal, to be later incorporated into the controller. Thus, Sliding Modes and their limitations regarding residual error are presented; artificial neural networks are then applied with the purpose of reducing them, however, they also have their restrictions; the Upper Confidence Bound is therefore added in order to mitigate them. The characteristics of each technique give the robot robustness in the control task, learning and autonomy with decision-making, respectively, as explained from the numerical and experimental results. The designed algorithm not only achieved the purposes, but also brought other positive points, such as avoiding the neural networks divergence resulting from the continuous updating of their weights. The approach developed based on the most recent arguments about autonomous agents obtained excellent results in both simulations and in experiments for the Robotino® trajectory tracking problem and represents the growing trend of research in embodied cognitive science.","abstract_has_math":false,"creators":["Baumann, Gabriel de Albuquerque Barbosa"],"institution":"Universidade Federal do Rio Grande do Norte","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Bessa, Wallace Moreira"],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-12-20","date_published":"2021-12-20","updated_at":"2026-07-24T01:20:54Z","subjects":["Controle inteligente não linear","Robôs móveis","Controle por modos deslizantes","Redes neurais artificiais","Aprendizagem por reforço"],"languages":["pt_BR"],"rights":["Acesso Aberto"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://repositorio.ufrn.br/handle/123456789/48351","outbound_label":"Repository record","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Bessa, Wallace Moreira"]},{"key":"dc:creator","label":"Author","values":["Baumann, Gabriel de Albuquerque Barbosa"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2022-07-05T23:24:56Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2022-07-05T23:24:56Z"]},{"key":"dc:date.issued","label":"Date","values":["2021-12-20"]},{"key":"dc:publisher","label":"Institution","values":["Universidade Federal do Rio Grande do Norte"]},{"key":"dc:type","label":"Dc Type","values":["masterThesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Controle inteligente não linear","Robôs móveis","Controle por modos deslizantes","Redes neurais artificiais","Aprendizagem por reforço"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["pt_BR"]},{"key":"dc:rights","label":"Dc Rights","values":["Acesso Aberto"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://repositorio.ufrn.br/handle/123456789/48351"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Research on intelligent and autonomous mobile robots has grown significantly due to its military, civil and industrial applications, such as the monitoring of agricultural plantations, the use in actions to support environmental disasters, border patrol, mapping of submarine territories or even the study of animal behavior. This work rescues the multi and interdisciplinary motivation of artificial intelligence, starting from philosophical questions to reach the characterization of intelligent and autonomous systems. Thus, only after building the theoretical bases for the concept of these agents, a bioinspired approach is presented for the trajectory tracking task by a omnidirectional mobile robot, the Robotino® produced by Festo® . For this purpose, the strategy consists of robust non-linear intelligent control using Sliding Modes, artificial neural networks and the Upper Confidence Bound algorithm. Each of these fundamentals techniques are presented, in order to justify, in advance, their consistent use with the theoretical proposal, to be later incorporated into the controller. Thus, Sliding Modes and their limitations regarding residual error are presented; artificial neural networks are then applied with the purpose of reducing them, however, they also have their restrictions; the Upper Confidence Bound is therefore added in order to mitigate them. The characteristics of each technique give the robot robustness in the control task, learning and autonomy with decision-making, respectively, as explained from the numerical and experimental results. The designed algorithm not only achieved the purposes, but also brought other positive points, such as avoiding the neural networks divergence resulting from the continuous updating of their weights. The approach developed based on the most recent arguments about autonomous agents obtained excellent results in both simulations and in experiments for the Robotino® trajectory tracking problem and represents the growing trend of research in embodied cognitive science."]},{"key":"dc:title","label":"Title","values":["Controle inteligente de um robô móvel utilizando modos deslizantes, redes neurais artificiais e aprendizagem por reforço"]}]}],"canonical_facts":{"dc:contributor.advisor":["Bessa, Wallace Moreira"],"dc:creator":["Baumann, Gabriel de Albuquerque Barbosa"],"dc:date.accessioned":["2022-07-05T23:24:56Z"],"dc:date.available":["2022-07-05T23:24:56Z"],"dc:date.issued":["2021-12-20"],"dc:description.abstract":["Research on intelligent and autonomous mobile robots has grown significantly due to its military, civil and industrial applications, such as the monitoring of agricultural plantations, the use in actions to support environmental disasters, border patrol, mapping of submarine territories or even the study of animal behavior. 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Thus, Sliding Modes and their limitations regarding residual error are presented; artificial neural networks are then applied with the purpose of reducing them, however, they also have their restrictions; the Upper Confidence Bound is therefore added in order to mitigate them. The characteristics of each technique give the robot robustness in the control task, learning and autonomy with decision-making, respectively, as explained from the numerical and experimental results. The designed algorithm not only achieved the purposes, but also brought other positive points, such as avoiding the neural networks divergence resulting from the continuous updating of their weights. The approach developed based on the most recent arguments about autonomous agents obtained excellent results in both simulations and in experiments for the Robotino® trajectory tracking problem and represents the growing trend of research in embodied cognitive science."],"dc:identifier.uri":["https://repositorio.ufrn.br/handle/123456789/48351"],"dc:language":["pt_BR"],"dc:publisher":["Universidade Federal do Rio Grande do Norte"],"dc:rights":["Acesso Aberto"],"dc:subject":["Controle inteligente não linear","Robôs móveis","Controle por modos deslizantes","Redes neurais artificiais","Aprendizagem por reforço"],"dc:title":["Controle inteligente de um robô móvel utilizando modos deslizantes, redes neurais artificiais e aprendizagem por reforço"],"dc:type":["masterThesis"]},"updated_at":"2026-07-24T01:20:54Z"}