Field dose radiation determination by active learning with gaussian process for autonomous robot guiding

• Autor(a) principal: Pereira, Claudio Márcio Nacimento Abreu
• Data de Publicação: 2017
• Outros Autores: Instituto de Engenharia Nuclear
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional do IEN
• Texto Completo: http://carpedien.ien.gov.br:8080/handle/ien/2331
• topic: Virtual reality; INAC 2017; Gaussian Process; Radioactive material; Human risk

This article proposes an approach for determination of radiation dose pro le in a radiation-susceptible environment, aiming to guide an autonomous robot in acting on those environments, reducing the human exposure to dangerous amount of dose. The approach consists of an active learning method based on information entropy reduction, using log-normally warped Gaussian Process (GP) as surrogate model, resulting in non-linear online regression with sequential measurements. Experiments with simulated radiation dose elds of varying complexity were made, and results showed that the approach was e ective in reconstruct the eld with high accuracy, through relatively few measurements. The technique was also shown some robustness in presence measurement noise, present in real measurements, by assuming Gaussian noise.