Paper Info
Title
Force-guided autonomous robotic ultrasound scanning control method for soft uncertain environment
Abstract
Purpose Autonomous ultrasound imaging by robotic ultrasound scanning systems in complex soft uncertain clinical environments is important and challenging to assist in therapy. To cope with the complex environment faced by the ultrasound probe during the scanning process, we propose an autonomous robotic ultrasound (US) control method based on reinforcement learning (RL) model to build the relationship between the environment and the system. The proposed method requires only contact force as input information to achieve robot control of the posture and contact force of the probe without any a priori information about the target and the environment. Methods First, an RL agent is proposed and trained by a policy gradient theorem-based RL model with the 6-degree-of-freedom (DOF) contact force of the US probe to learn the relationship between contact force and output force directly. Then, a force control strategy based on the admittance controller is proposed for synchronous force, orientation and position control by defining the desired contact force as the action space. Results The proposed method was evaluated via collected US images, contact force and scan trajectories by scanning an unknown soft phantom. The experimental results indicated that the proposed method differs from the free-hand scanned approach in the US images within 3 +/- 0.4%. The analysis results of contact forces and trajectories indicated that our method could make stable scanning processes on a soft uncertain skin surface and obtained US images. Conclusion We propose a concise and efficient force-guided US robot scanning control method for soft uncertain environment based on reinforcement learning. Experimental results validated our method's feasibility and validity for complex skin surface scanning, and the volunteer experiments indicated the potential application value in the complex clinical environment of robotic US imaging system especially with limited visual information.
Year
DOI
Venue
2021
10.1007/s11548-021-02462-6
INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY
Keywords
DocType
Volume
Autonomous medical robot, Ultrasound robot, Reinforcement learning, Force control
Journal
16
Issue
ISSN
Citations 
12
1861-6410
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Guochen Ning132.75
Jiaqi Chen200.34
Xinran Zhang33812.02
Hongen Liao439070.91