Details

Autor(en) / Beteiligte

• Girbés-Juan, Vicent
• Moll, Joaquín
• Sala, Antonio
• Armesto, Leopoldo

Titel

Cautious Bayesian Optimization: A Line Tracker Case Study

Ist Teil von

• Sensors (Basel, Switzerland), 2023-08, Vol.23 (16), p.7266

Ort / Verlag

Basel: MDPI AG

Erscheinungsjahr

2023

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• In this paper, a procedure for experimental optimization under safety constraints, to be denoted as constraint-aware Bayesian Optimization, is presented. The basic ingredients are a performance objective function and a constraint function; both of them will be modeled as Gaussian processes. We incorporate a prior model (transfer learning) used for the mean of the Gaussian processes, a semi-parametric Kernel, and acquisition function optimization under chance-constrained requirements. In this way, experimental fine-tuning of a performance objective under experiment-model mismatch can be safely carried out. The methodology is illustrated in a case study on a line-follower application in a CoppeliaSim environment.