Explicit Model Predictive Control of a Magnetic Flexible Endoscope

Multiparametric Programming in Process Systems Engineering: Recent Developments and Path Forward

The inevitable presence of uncertain parameters in critical applications of process optimization can lead to undesirable or infeasible solutions. For this reason, optimization under parametric uncertainty was, and continues to be a core area of research within Process Systems Engineering. Multiparametric programming is a strategy that offers a holistic perspective for the solution of this class of mathematical programming problems. Specifically, multiparametric programming theory enables the derivation of the optimal solution as a function of the uncertain parameters, explicitly revealing the impact of uncertainty in optimal decision-making. By taking advantage of such a relationship, new breakthroughs in the solution of challenging formulations with uncertainty have been created. Apart from that, researchers have utilized multiparametric programming techniques to solve deterministic classes of problems, by treating specific elements of the optimization program as uncertain parameters. In the past years, there has been a significant number of publications in the literature involving multiparametric programming. The present review article covers recent theoretical, algorithmic, and application developments in multiparametric programming. Additionally, several areas for potential contributions in this field are discussed, highlighting the benefits of multiparametric programming in future research efforts.

Enabling the future of colonoscopy with intelligent and autonomous magnetic manipulation

Early diagnosis of colorectal cancer significantly improves survival. However, over half of cases are diagnosed late due to demand exceeding the capacity for colonoscopy - the "gold standard" for screening. Colonoscopy is limited by the outdated design of conventional endoscopes, associated with high complexity of use, cost and pain. Magnetic endoscopes represent a promising alternative, overcoming drawbacks of pain and cost, but struggle to reach the translational stage as magnetic manipulation is complex and unintuitive. In this work, we use machine vision to develop intelligent and autonomous control of a magnetic endoscope, for the first time enabling non-expert users to effectively perform magnetic colonoscopy in-vivo. We combine the use of robotics, computer vision and advanced control to offer an intuitive and effective endoscopic system. Moreover, we define the characteristics required to achieve autonomy in robotic endoscopy. The paradigm described here can be adopted in a variety of applications where navigation in unstructured environments is required, such as catheters, pancreatic endoscopy, bronchoscopy, and gastroscopy. This work brings alternative endoscopic technologies closer to the translational stage, increasing availability of early-stage cancer treatments.

Magnetically Actuated Soft Capsule Endoscope for Fine-Needle Biopsy

Wireless capsule endoscopes have revolutionized diagnostic procedures in the gastrointestinal (GI) tract by minimizing discomfort and trauma. Biopsy procedures, which are often necessary for a confirmed diagnosis of an illness, have been incorporated recently into robotic capsule endoscopes to improve their diagnostic functionality beyond only imaging. However, capsule robots to date have only been able to acquire biopsy samples of superficial tissues of the GI tract, which could generate false-negative diagnostic results if the diseased tissue is under the surface of the GI tract. To improve their diagnostic accuracy for submucosal tumors/diseases, we propose a magnetically actuated soft robotic capsule robot, which takes biopsy samples in a deep tissue of a stomach using the fine-needle biopsy technique. We present the design, control, and human-machine interfacing methods for the fine-needle biopsy capsule robot. Ex vivo experiments in a porcine stomach show 85% yield for the biopsy of phantom tumors located underneath the first layers of the stomach wall.

Sensorless Estimation of the Planar Distal Shape of a Tip-Actuated Endoscope

Traditional endoscopes consist of a flexible body and a steerable tip with therapeutic capability. Although prior endoscopes have relied on operator pushing for actuation, recent robotic concepts have relied on the application of a tip force for guidance. In such case, the body of the endoscope can be passive and compliant; however, the body can have significant effect on mechanics of motion and may require modeling. As the endoscope body's shape is often unknown, we have developed an estimation method to recover the approximate distal shape, local to the endoscope's tip, where the tip position and orientation are the only sensed parameters in the system. We leverage a planar dynamic model and extended Kalman filter to obtain a constant-curvature shape estimate of a magnetically guided endoscope. We validated this estimator in both dynamic simulations and on a physical platform. We then used this estimate in a feed-forward control scheme and demonstrated improved trajectory following. This methodology can enable the use of inverse-dynamic control for the tip-based actuation of an endoscope, without the need for shape sensing.