Cognitive Supervision for Robot-Assisted Minimally Invasive Laser Surgery

Biomedical Engineering, Robotics and Automation, User Interfaces and Human Computer Interaction, Minimally Invasive Surger

realization of a cognitive supervisory system for laser microsurgery

Based on the models presented in Chaps. 4 and 5, here we describe the development of a prototypical supervisory system for laser microsurgery

modeling the laser ablation process

This chapter focuses on the problem of modeling the laser ablation process from a geometrical point of view. The objective is to create a model capable of describing the laser incision depth based on the knowledge of the laser parameters and inputs. The discussion starts with a statement of the problem, which is defined in terms of a supervised regression. Our approach is compared with existing heuristic models for the prediction of ablation depth

Assistive control for non-contact machining of random shaped contours

Recent achievements in robotics and automation technology has opened the door towards different machining methodologies based on material removal. Considering the non force feedback nature of non-contact machining methods, careful attention on motion control design is a primary requirement for successful achievement of precise cutting both in machining and in surgery processes. This thesis is concerned with the design of pre-processing methods and motion control techniques to provide both automated and human-assistive non-contact machining of random and complex shaped contours. In that sense, the first part of the thesis focuses on extraction of contours and generation of reference trajectories or constraints for the machining system. Based on generated trajectories, two different control schemes are utilized for high precision automated machining. In the first scheme, preview control is adopted for enhancing the tracking performance. In the second scheme, control action is generated based on direct computation of contouring error in the operational space by introducing a new coordinate frame moving with the reference contour. Further, non-contact machining is extended for realization in a master/slave telerobotic framework to enable manual remote cutting by a human operator. With the proposed approach, the human operator (i.e. a surgeon) is limited to conduct motion within a desired virtual constraint and is equipped with the ability of adjusting the cutting depth over a that contour providing advantage for laser surgery applications. The proposed framework is experimentally tested and results of the experiments prove the applicability of proposed motion control schemes and show the validity of contributions made in the context of thesis

cognitive supervision for transoral laser microsurgery

This chapter introduces the problem of the automatic supervision of laser-induced effects during laser surgery. A top-down approach is used to tackle this problem: specific circumstances in which surgeons would value enhanced information regarding the effects of their laser actions on tissues are identified. The problem is grounded in the identification of variables of interest that are selected as target for the supervision. In the scope of this thesis, we explore the application of artificial cognitive approaches to monitor these variables in a surgical scenario