Work

Model-based tracking of miniaturized grippers using Particle Swarm Optimization

Year: 2016

Type: conference-paper

Abstract: Micro-sized agents can benefit robotic minimally invasive surgery since they can be inserted into the human body and use natural pathways such as arteries and veins or the gastrointestinal tract, to reach their target for drug delivery or diagnosis. Recently, miniaturized agents with shape-changing and gripping capabilities have provided significant advantages in performing grasping, transportation, and manipulation tasks. In order to robustly perform such tasks, it is of utmost importance to properly estimate their overall configuration. This paper presents a novel solution to the problem of estimating and tracking the 3D position, orientation and configuration of the tips of miniaturized grippers from RGB marker-less visual observations obtained by a microscope. We consider this as an optimization problem, seeking for the gripper model parameters that minimize the discrepancy between hypothesized instances of the gripper model and actual observations of the miniaturized gripper. This optimization problem is solved using a variant of the Particle Swarm Optimization algorithm. The proposed approach has been evaluated on several image sequences showing the grippers moving, rotating, opening/closing and grasping biological material.

Source: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

DOI: 10.1109/iros.2016.7759093

Authors: Stefano Scheggi, ChangKyu Yoon, David H. Gracias, Sarthak Misra

Institutions: University of Twente, Johns Hopkins University, University Medical Center Groningen, University of Groningen

Language: English

Topic: Micro and Nano Robotics

Subfield: Condensed Matter Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funders: European Commission, H2020 European Research Council

Awards: G434337789, Robot-Assisted Flexible Needle Steering for Targeted Delivery of Magnetic Agents