Electronic Thesis and Dissertation Repository


Doctor of Philosophy


Mechanical and Materials Engineering


Dr. Rajni Patel

2nd Supervisor

Dr. Michael Naish

Joint Supervisor


In this thesis, a computational framework for patient-specific preoperative planning of Robotics-Assisted Minimally Invasive Cardiac Surgery (RAMICS) is developed. It is expected that preoperative planning of RAMICS will improve the rate of success by considering robot kinematics, patient-specific thoracic anatomy, and procedure-specific intraoperative conditions. Given the significant anatomical features localized in the preoperative computed tomography images of a patient's thorax, port locations and robot orientations (with respect to the patient's body coordinate frame) are determined to optimize characteristics such as dexterity, reachability, tool approach angles and maneuverability. In this thesis, two approaches for preoperative planning of RAMICS are proposed that enable contemplation of uncertainties in preoperative data and surgical tasks. In the first approach, the problem is formulated as a Generalized Semi-Infinite Program (GSIP) with a convex lower-level problem to maximize the tolerable geometric uncertainty in the neighborhood of surgical targets. It is demonstrated that with a proper formulation of the problem, the GSIP can be replaced by a tractable constrained nonlinear program that uses a multi-criteria objective function to balance between the nominal task performance and robustness to collisions and joint limit violations. In the second approach, the proposed formulation attempts to increase the chance of success by maximizing robustness with respect to uncertainties at the task level. It is assumed that the surgical tasks can be represented by Gaussian distributions, and the planner is formulated as a chance-constrained entropy maximization problem. The efficacy of the proposed formulations is demonstrated by comparisons between the plans generated by the algorithms and those recommended by an experienced surgeon for several case studies.