Optimization and learning applied to control and modeling of mechatronic systems.

• Ano de defesa: 2022
• Autor(a) principal: Peixoto, Bruno Henrique Lobo Netto
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Algoritmos; Barycenter; Modelagem; Optimization; Otimização; Robótica; Source seeking
• Link de acesso: https://www.teses.usp.br/teses/disponiveis/3/3139/tde-31032023-083634/

The source seeking is a relevant topic on autonomous robotics. In a few words, it consists of seeking a scalar signal source position with only local information on base X-space. In such, the seek agent, for instance, a mobile tracking A-robot, samples by hypothesis C-class source signal -map constrained by hull (A) X. Among available seeking methods, this work utilizes the barycenter method, first presented on work [2], as a direct optimization method due to derivatives absence. The applied algorithm estimates the source yn-position and designs a suitable reference (t)-curve, hopefully towards a sufficiently close vicinity of the actual source y s-position. In case there are multiple critical points, the {d(y s , yn)}-sequence may not converge asymptotically to a sufficiently close neighborhood of zero due to its local behavior, a challenge for these garden-like optimization algorithms. This work succeeds to obtain results in direction of the source signal position. Therefore, the proposed methodology provides an alternative for source-seeking applications by defined-to-be exploration strategy by different agent seekers, source signal maps, and obstacle modeling.