Autonomic Robotic Ultrasound Imaging System Based on Reinforcement Learning

Abstract

Objective: In this paper, we introduce an autonomous robotic ultrasound (US) imaging system based on reinforcement learning (RL). The proposed system and framework are committed to controlling the US probe to perform fully autonomous imaging of a soft, moving and marker-less target based only on single RGB images of the scene.

Methods: We propose several different approaches and methods to achieve the following objectives: real-time US probe controlling, soft surface constant force tracking and automatic imaging. First, to express the state of the robotic US imaging task, we proposed a state representation model to reduce the dimensionality of the imaging state and encode the force and US information into the scene image space. Then, an RL agent is trained by a policy gradient theorem based RL model with the single RGB image as the only observation. To achieve adaptable constant force tracking between the US probe and the soft moving target, we propose a force-to-displacement control method based on an admittance controller.

Results: In the simulation experiment, we verified the feasibility of the integrated method. Furthermore, we evaluated the proposed force-to-displacement method to demonstrate the safety and effectiveness of adaptable constant force tracking. Finally, we conducted phantom and volunteer experiments to verify the feasibility of the method on a real system.

Conclusion: The experiments indicated that our approaches were stable and feasible in the autonomic and accurate control of the US probe.

Significance: The proposed system has potential application value in the image-guided surgery and robotic surgery.