SENSOR AND SAMPLING-BASED MOTION PLANNING FOR MINIMALLY INVASIVE ROBOTIC EXPLORATION OF OSTEOLYTIC LESIONS

Wen P. Liua, Blake C. Lucasa,b, Kelleher Guerina , Erion Plakuc

a Department of Computer Science, Johns Hopkins Universityb The Johns Hopkins University Applied Physics Laboratory

C Dept. of Electrical Engineering and Computer Science, Catholic University

Johns Hopkins University Applied Physics Lab

NSF Engineering Research Center for Computer Integrated Surgical Systems and Technology

Introduction OverviewApplication: Total hip replacements require revision surgery after many years of use. Wear on interface between implant and pelvis causes an inflammatory reaction.Lesions form behind the implant’s cup near the screw channels.Lesions have the consistency of “strawberry jam.”Treatment: Remove the lesions via the screw holes in the cup. Surgical Scenario:The femural implant is removed, exposing the cup.Screws in the cup are removed, although the cup itself cannot be removed.Surgeon uses a metal pick and vacuum to clean out the lesion.Problem: It’s difficult to clean out the osteolytic cavity due to constraints imposed by the screw channels.Robotic Solution:Use an articulated controllable cannula to improve coverage of the osteolytic cavity.

Algorithm

1) Construct global road map.2) Decompose workspace into grid cells.3) Locally explore each grid cell. If there are no neighboring configurations that increase the explored volume, then either: LE-Frontier: Move to an unvisited configuration that is closest to the free boundary. LE-Backtrack: Move to the previous configuration on the local path.

4) Continue local exploration until there is no information gain, the current configuration has been visited before, and all neighboring configurations have been visited.5) Move to next unexplored grid cell and repeat from 3.

Current Exploration Grid Cell

Explored Volume (EV)

Global Exploration Path

Local Exploration Path

Results

Ratio of EV (explored volume) in the case of the hard-difficulty osteolytic cavity.

Ratio of EV (explored volume) by ORE vs the optimal method, which, unlike ORE, has a 3D mesh of the cavity. Results are shown as a function of the number of regions when combining ORE with the two local exploration strategies and exploring the osteolytic cavity of Fig. 4(a) and when using a roadmap with (left) 5000 and (right) 10000 configurations.

Osteolytic cavities regarded as hard and medium difficulty.

Osteolytic Cavity

ConclusionORE assumes no prior information about the geometry of the osteolytic cavity and relies only on a representative kinematic model of the robot, its capability of collision detection, and its exploration tool.

Simulation experiments with a snake-like robot and surgically-relevant osteolytic cavities indicated that by combining local exploration, information gain, and global path planning, ORE effectively explored the cavity.

Acknowledgement. The authors gratefully acknowledge the work on the Osteolysis Robot design by Dr. R. Taylor, Dr. M. Armand, and Mr. M. Kutzer of Johns Hopkins University. We would like to also thank the project’s clinical consultants, Dr. Simon Mears from Dept. of Orthopaedic surgery, Johns Hopkins medical school, and Dr. Jyri Lepisto of Orton hospital, Helsinki.