A physical information neural network-based path planning method for flexible needle puncture of soft tissues
Abstract
BACKGROUND: Flexible needle puncture is a critical minimally invasive technique where path planning accuracy directly impacts clinical outcomes. Traditional finite element methods are computationally complex and inadequate for real-time procedures. METHODS: We propose a physics-informed neural network (PINN) approach for soft tissue puncture path planning. PINN models generate puncturable regions where rapidly-exploring random tree star (RRT*) performs path optimization using neural network-derived soft tissue mechanics models, avoiding collision risks. Progressive learning control strategies provide real-time path optimization, ensuring accurate needle targeting. RESULTS: Experimental results demonstrate dynamic puncture path correction with errors
Fields of Study & Subjects
Soft Robotics and Applications
Robot Manipulation and Learning
Intravenous Infusion Technology and Safety
Publication details
- Journal / source
- Minimally Invasive Therapy & Allied Technologies
- Publisher
- Informa UK Limited
- Volume
- 35
- Issue
- 2
- Pages
- 116–124
- Publication date
- 5 February 2026
- Language
- EN