# Contact-Aided Navigation of Flexible Robotic Endoscope Using Deep Reinforcement Learning in Dynamic Stomach

## TL;DR

This paper presents a deep reinforcement learning approach that uses contact force feedback to improve the navigation accuracy and stability of flexible endoscopes in dynamic, deformable stomach environments, achieving high success rates.

## Contribution

The study introduces a novel contact-aided navigation strategy using DRL and contact force feedback, trained in a physics-based simulation for flexible endoscopes in deformable stomachs, outperforming previous methods.

## Key findings

- Achieved 100% success rate within 3 mm in static and dynamic environments.
- Maintained 85% success rate in unseen, challenging scenarios.
- Significantly outperformed baseline navigation policies.

## Abstract

Navigating a flexible robotic endoscope (FRE) through the gastrointestinal tract is critical for surgical diagnosis and treatment. However, navigation in the dynamic stomach is particularly challenging because the FRE must learn to effectively use contact with the deformable stomach walls to reach target locations. To address this, we introduce a deep reinforcement learning (DRL) based Contact-Aided Navigation (CAN) strategy for FREs, leveraging contact force feedback to enhance motion stability and navigation precision. The training environment is established using a physics-based finite element method (FEM) simulation of a deformable stomach. Trained with the Proximal Policy Optimization (PPO) algorithm, our approach achieves high navigation success rates (within 3 mm error between the FRE's end-effector and target) and significantly outperforms baseline policies. In both static and dynamic stomach environments, the CAN agent achieved a 100% success rate with 1.6 mm average error, and it maintained an 85% success rate in challenging unseen scenarios with stronger external disturbances. These results validate that the DRL-based CAN strategy substantially enhances FRE navigation performance over prior methods.