Automatic placement of cameras in a particle accelerator using a 3D engine and computational optimization

Abstract

Particle accelerators have high-radiation parts requiring remote maintenance tasks, which involve using cameras generally placed by human experts. This work addresses camera placement through black-box global optimization. This approach allows opting for automated solutions and relieving experts of that responsibility. The formulation of the objective function hides the environment-specific placement constraints and shows domain ones only. The proposed methodology allows the use of regular meta-heuristics for standard black-box box-constrained problems, as it is only necessary to honor the bounds of variables. Accordingly, the optimization algorithm used is the genetic algorithm provided by the Global Optimization Toolbox of MATLAB. The objective function computation relies on the simulation capabilities of the widespread Unity game engine. It brings us an integrated framework for defining the target process to consider and for checking the different proposals visually. Based on this framework, this work reconstructs the virtual reality simulation of a maintenance task in a particle accelerator. The results obtained show that the camera positioning achieved by the proposal for four cameras and one target process outperforms the arrangement defined by a human expert.