Auto-tuning for cascade PID height position controller of rotorcraft

In this article, we present a method for tuning controller parameters for cascade PID based on the step response performance characteristics of a closed loop system with application to an unmanned aerial vehicle(UAV). With specifically designed system identification procedures, a neural network mapping is obtained automatically when the UAV is flying around the target height. With this network system model, a gradual regression and optimization algorithm is proposed to tune the controller. The regression model primly illustrated the relation between PID parameters with controller performance, and the construction for optimization cost function takes the physical significance of step response performance of flying machine into account. Experimental data collected from fight experiment when auto-tuner is implemented on a quadrotorcraft demonstrate the efficiency of the proposed method

Distortion and instability compensation with deep learning for rotational scanning endoscopic optical coherence tomography

Optical Coherence Tomography (OCT) is increasingly used in endoluminal procedures since it provides high-speed and high resolution imaging. Distortion and instability of images obtained with a proximal scanning endoscopic OCT system are significant due to the motor rotation irregularity, the friction between the rotating probe and outer sheath and synchronization issues. On-line compensation of artefacts is essential to ensure image quality suitable for real-time assistance during diagnosis or minimally invasive treatment. In this paper, we propose a new online correction method to tackle both B-scan distortion, video stream shaking and drift problem of endoscopic OCT linked to A-line level image shifting. The proposed computational approach for OCT scanning video correction integrates a Convolutional Neural Network (CNN) to improve the estimation of azimuthal shifting of each A-line. To suppress the accumulative error of integral estimation we also introduce another CNN branch to estimate a dynamic overall orientation angle. We train the network with semi-synthetic OCT videos by intentionally adding rotational distortion into real OCT scanning images. The results show that networks trained on this semi-synthetic data generalize to stabilize real OCT videos, and the algorithm efficacy is demonstrated on both ex vivo and in vivo data, where strong scanning artifacts are successfully corrected. (c) 2022 The Authors. Published by Elsevier B.V

Analysis and correction of OCT images for the control of robotic flexible endoscopes

This doctoral research is focused on overcoming problems in autonomous surgical procedures when instruments have to navigate towards the clinical target by accurate self-localization in the front of certain tissue and, simultaneously, to build a map of the luminal environment for medical diagnosis. Vision-based approaches using stable tissue texture are highly desirable for a wide range of applications. Optical Coherence Tomography (OCT) [1] is an imaging technique of great importance in biomedical optical applications. The backscattered light is measured of the internal structure of biological tissues to provide high resolution, axial and three-dimensional images of the sample. Endoscopic OCT catheter has been applied into cardiovascular, respiratory and digestive systems for imaging of internal structures. In gastroenterology, a balloon and capsule based catheters have been developed for imaging of the esophagus. Catheter-based imaging systems have limited Field of View (FoV), especially when considering OCT systems which emphasize more on the image resolution. For small lumens such as the vasculature and esophagus, volume reconstruction from one pull back scanning using an OCT system can be sufficient for accessing the entire lumen. However, for larger luminal environments as the colon or stomach, the link between reconstruction, robot planning and robot control needs to be established e.g. the link with robot control is needed in order to realize a certain scanning behavior, which would be necessary to make reconstruction efficient and accurate. This side-viewing catheter could be employed to actively follow the lumen wall with a robotic endoscope. The OCT augmented endoscope can provide more accurate navigation feedback for the control system. The robotic endoscope also has a camera in the distal part, which can perform a rough global navigation to aid the OCT system’s local scanning. In the local scanning process, ideally, the distance between the OCT probe and the tissue is controlled to be constant. This could keep the tissue always in the FoV of the OCT, especially for luminal tissue with a complex geometry like the colon. Another type of safe scanning mode could also be realized with contact between the OCT catheter and the colon tissue surface. In this case, a segmentation algorithm is required to provide real-time quantitative feedback about the contact or the distance. For volumetric reconstruction from the robotic scanning, computer vision and imaging processing techniques including incremental mapping or Structure from-Motion (SfM) can be deployed. The main aims could be divided into the following three: - Find an efficient configuration for robotic endoscope navigation. To achieve this task, the OCT images first need to be stabilized to improve its orientation accuracy. - Information perception for both diagnosis and navigation purpose. Tailor the machine learning based computer vision algorithm for side-viewing imaging modalities. - Design automatic scanning strategies for larger lumen environment with small FoV side-viewing probes, incorporate local navigation information with global navigation information

Colon phantoms with cancer lesions for endoscopic characterization with optical coherence tomography

International audienceOptical coherence tomography (OCT) is a growing imaging technique for real-time early diagnosis of digestive system diseases. As with other well-established medical imaging modalities, OCT requires validated imaging performance and standardized test methods for performance assessment. A major limitation in the development and testing of new imaging technologies is the lack of models for simultaneous clinical procedure emulation and characterization of healthy and diseased tissues. Currently, the former can be tested in large animal models and the latter can be tested in small animal disease models or excised human biopsy samples. In this study, a 23 cm by 23 cm optical phantom was developed to mimic the thickness and near-infrared optical properties of each anatomical layer of a human colon, as well as the surface topography of colorectal polyps and visual appearance compatible with white light endoscop

Data Stream Stabilization for Optical Coherence Tomography Volumetric Scanning

Optical Coherence Tomography (OCT) is an emerging medical imaging modality for luminal organ diagnosis. The non-constant rotation speed of optical components in the OCT catheter tip causes rotational distortion in OCT volumetric scanning. By improving the scanning process, this instability can be partially reduced. To further correct the rotational distortion in the OCT image, a volumetric data stabilization algorithm is proposed. The algorithm first estimates the Non-Uniform Rotational Distortion (NURD) for each B-scan by using a Convolutional Neural Network (CNN). A correlation map between two successive B-scans is computed and provided as input to the CNN. To solve the problem of accumulative error in iterative frame stream processing, we deploy an overall rotation estimation between reference orientation and actual OCT image orientation. We train the network with synthetic OCT videos by intentionally adding rotational distortion into real OCT images. As part of this article we discuss the proposed method in two different scanning modes: the first is a conventional pullback mode where the optical components move along the protection sheath, and the second is a self-designed scanning mode where the catheter is globally translated by using an external actuator. The efficiency and robustness of the proposed method are evaluated with synthetic scans as well as real scans under two scanning modes