Nathan A Wood1, David Schwartzman2, Michael J Passineau3, M Scott Halbreiner4, Robert J Moraca4, Marco A Zenati5, Cameron N Riviere1. 1. The Robotics Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania. 2. Cardiovascular Institute, University of Pittsburgh, Pittsburgh, Pennsylvania. 3. Gene Therapy Program, Allegheny Health Network, Pittsburgh, Pennsylvania. 4. Cardiovascular Institute, Allegheny General Hospital, Pittsburgh, Pennsylvania. 5. BHS Department of Cardiothoracic Surgery, Harvard Medical School, West Roxbury, Massachusetts.
Abstract
BACKGROUND: Organ-mounted robots adhere to the surface of a mobile organ as a platform for minimally invasive interventions, providing passive compensation of physiological motion. This approach is beneficial during surgery on the beating heart. Accurate localization in such applications requires accounting for the heartbeat and respiratory motion. Previous work has described methods for modeling quasi-periodic motion of a point and registering to a static preoperative map. The existing techniques, while accurate, require several respiratory cycles to converge. METHODS: This paper presents a general localization technique for this application, involving function approximation using radial basis function (RBF) interpolation. RESULTS: In an experiment in the porcine model in vivo, the technique yields mean localization accuracy of 1.25 mm with a 95% confidence interval of 0.22 mm. CONCLUSIONS: The RBF approximation provides accurate estimates of robot location instantaneously.
BACKGROUND: Organ-mounted robots adhere to the surface of a mobile organ as a platform for minimally invasive interventions, providing passive compensation of physiological motion. This approach is beneficial during surgery on the beating heart. Accurate localization in such applications requires accounting for the heartbeat and respiratory motion. Previous work has described methods for modeling quasi-periodic motion of a point and registering to a static preoperative map. The existing techniques, while accurate, require several respiratory cycles to converge. METHODS: This paper presents a general localization technique for this application, involving function approximation using radial basis function (RBF) interpolation. RESULTS: In an experiment in the porcine model in vivo, the technique yields mean localization accuracy of 1.25 mm with a 95% confidence interval of 0.22 mm. CONCLUSIONS: The RBF approximation provides accurate estimates of robot location instantaneously.
Authors: Shelten G Yuen; Daniel T Kettler; Paul M Novotny; Richard D Plowes; Robert D Howe Journal: Int J Rob Res Date: 2009-10-01 Impact factor: 4.703
Authors: Nathan A Wood; David Schwartzman; Michael J Passineau; Robert J Moraca; Marco A Zenati; Cameron N Riviere Journal: Int J Med Robot Date: 2018-03-06 Impact factor: 2.547
Authors: Adam D Costanza; Macauley S Breault; Nathan A Wood; Michael J Passineau; Robert J Moraca; Cameron N Riviere Journal: IEEE Robot Autom Lett Date: 2016-02-15