Multi-objective optimization of a self-propelled capsule for small bowel endoscopy considering the influence of intestinal environment

This work studies the control optimization of a self-propelled capsule moving in the small intestinal environment for endoscopic diagnosis. For this purpose, we combine an existing capsule model with the intestinal peristalsis and its internal environment in order to gain a better understanding of dynamics of the self-propelled capsule. For the optimization study, a number of different realistic targets are considered, including the capsule's average progression speed, the impact force acting on the small intestine and the capsule's energy consumption. In addition, the uncertainty of the small intestine environment is taking into account by varying its internal radius. In this setting, we develop a multi-objective optimization strategy based on NSGA-II, Monte Carlo, and Six-Sigma algorithms considering both the control and structural model parameters, such as excitation frequency and impact stiffness. The effectiveness of the proposed optimization strategy is demonstrated via extensive numerical simulations with the consideration of a wide range of realistic scenarios.

Automated summary

This work focuses on the control optimization of a self-propelled capsule used for endoscopic diagnosis in the small intestine. The study combines an existing capsule model with the intestinal peristalsis and its internal environment to better understand the dynamics of the self-propelled capsule. Various realistic targets, including the capsule's speed, impact force, and energy consumption, are considered in the optimization study. The effects of uncertainty in the small intestine environment are also accounted for. A multi-objective optimization strategy based on NSGA-II, Monte Carlo, and Six-Sigma algorithms is developed, considering both control and structural model parameters. Extensive numerical simulations demonstrate the effectiveness of the proposed optimization strategy.