Dataset Information

An Efficient and Adaptable Path Planning Algorithm for Automated Fiber Placement Based on Meshing and Multi Guidelines.

ABSTRACT: Path planning algorithms for automated fiber placement are used to determine the directions of the fiber paths and the start and end positions on the mold surfaces. The quality of the fiber paths determines largely the efficiency and quality of the automated fiber placement process. The presented work investigated an efficient path planning algorithm based on surface meshing. In addition, an update method of the datum direction vector via a guide-line update strategy was proposed to make the path planning algorithm applicable for complex surfaces. Finally, accuracy analysis was performed on the proposed algorithm and it can be adopted as the reference for the triangulation parameter selection for the path planning algorithm.

SUBMITTER: Xiao H

PROVIDER: S-EPMC7560477 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

An Efficient and Adaptable Path Planning Algorithm for Automated Fiber Placement Based on Meshing and Multi Guidelines.

Xiao Hong H Han Wei W Tang Wenbin W Duan Yugang Y

Materials (Basel, Switzerland) 20200922 18

Path planning algorithms for automated fiber placement are used to determine the directions of the fiber paths and the start and end positions on the mold surfaces. The quality of the fiber paths determines largely the efficiency and quality of the automated fiber placement process. The presented work investigated an efficient path planning algorithm based on surface meshing. In addition, an update method of the datum direction vector via a guide-line update strategy was proposed to make the pat ...[more]

PMID: 32971856

Similar Datasets

Project description:Deep Brain Stimulation (DBS) is a neurosurgical procedure consisting in the stereotactic implantation of stimulation electrodes to specific brain targets, such as deep gray matter nuclei. Current solutions to place the electrodes rely on rectilinear stereotactic trajectories (RTs) manually defined by surgeons, based on pre-operative images. An automatic path planner that accurately targets subthalamic nuclei (STN) and safeguards critical surrounding structures is still lacking. Also, robotically-driven curvilinear trajectories (CTs) computed on the basis of state-of-the-art neuroimaging would decrease DBS invasiveness, circumventing patient-specific obstacles. This work presents a new algorithm able to estimate a pool of DBS curvilinear trajectories for reaching a given deep target in the brain, in the context of the EU's Horizon EDEN2020 project. The prospect of automatically computing trajectory plans relying on sophisticated newly engineered steerable devices represents a breakthrough in the field of microsurgical robotics. By tailoring the paths according to single-patient anatomical constraints, as defined by advanced preoperative neuroimaging including diffusion MR tractography, this planner ensures a higher level of safety than the standard rectilinear approach. Ten healthy controls underwent Magnetic Resonance Imaging (MRI) on 3T scanner, including 3DT1-weighted sequences, 3Dhigh-resolution time-of-flight MR angiography (TOF-MRA) and high angular resolution diffusion MR sequences. A probabilistic q-ball residual-bootstrap MR tractography algorithm was used to reconstruct motor fibers, while the other deep gray matter nuclei surrounding STN and vessels were segmented on T1 and TOF-MRA images, respectively. These structures were labeled as obstacles. The reliability of the automated planner was evaluated; CTs were compared to RTs in terms of efficacy and safety. Targeting the anterior STN, CTs performed significantly better in maximizing the minimal distance from critical structures, by finding a tuned balance between all obstacles. Moreover, CTs resulted superior in reaching the center of mass (COM) of STN, as well as in optimizing the entry angle in STN and in the skull surface.

Dataset Information

An Efficient and Adaptable Path Planning Algorithm for Automated Fiber Placement Based on Meshing and Multi Guidelines.

Publications

An Efficient and Adaptable Path Planning Algorithm for Automated Fiber Placement Based on Meshing and Multi Guidelines.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets