Study on Magnetic Control Systems of Micro-Robots

Magnetic control systems of micro-robots have recently blossomed as one of the most thrilling areas in the field of medical treatment. For the sake of learning how to apply relevant technologies in medical services, we systematically review pioneering works published in the past and divide magnetic control systems into three categories: stationary electromagnet control systems, permanent magnet control systems and mobile electromagnet control systems. Based on this, we ulteriorly analyze and illustrate their respective strengths and weaknesses. Furthermore, aiming at surmounting the instability of magnetic control system, we utilize SolidWorks2020 software to partially modify the SAMM system to make its final overall thickness attain 111 mm, which is capable to control and observe the motion of the micro-robot under the microscope system in an even better fashion. Ultimately, we emphasize the challenges and open problems that urgently need to be settled, and summarize the direction of development in this field, which plays a momentous role in the wide and safe application of magnetic control systems of micro-robots in clinic

VR-Caps: A Virtual Environment for Capsule Endoscopy

Current capsule endoscopes and next-generation robotic capsules for diagnosis and treatment of gastrointestinal diseases are complex cyber-physical platforms that must orchestrate complex software and hardware functions. The desired tasks for these systems include visual localization, depth estimation, 3D mapping, disease detection and segmentation, automated navigation, active control, path realization and optional therapeutic modules such as targeted drug delivery and biopsy sampling. Data-driven algorithms promise to enable many advanced functionalities for capsule endoscopes, but real-world data is challenging to obtain. Physically-realistic simulations providing synthetic data have emerged as a solution to the development of data-driven algorithms. In this work, we present a comprehensive simulation platform for capsule endoscopy operations and introduce VR-Caps, a virtual active capsule environment that simulates a range of normal and abnormal tissue conditions (e.g., inflated, dry, wet etc.) and varied organ types, capsule endoscope designs (e.g., mono, stereo, dual and 360{\deg}camera), and the type, number, strength, and placement of internal and external magnetic sources that enable active locomotion. VR-Caps makes it possible to both independently or jointly develop, optimize, and test medical imaging and analysis software for the current and next-generation endoscopic capsule systems. To validate this approach, we train state-of-the-art deep neural networks to accomplish various medical image analysis tasks using simulated data from VR-Caps and evaluate the performance of these models on real medical data. Results demonstrate the usefulness and effectiveness of the proposed virtual platform in developing algorithms that quantify fractional coverage, camera trajectory, 3D map reconstruction, and disease classification.Comment: 18 pages, 14 figure

The astronaut and the banana peel: An EVA retriever scenario

To prepare for the problem of accidents in Space Station activities, the Extravehicular Activity Retriever (EVAR) robot is being constructed, whose purpose is to retrieve astronauts and tools that float free of the Space Station. Advanced Decision Systems is at the beginning of a project to develop research software capable of guiding EVAR through the retrieval process. This involves addressing problems in machine vision, dexterous manipulation, real time construction of programs via speech input, and reactive execution of plans despite the mishaps and unexpected conditions that arise in uncontrolled domains. The problem analysis phase of this work is presented. An EVAR scenario is used to elucidate major domain and technical problems. An overview of the technical approach to prototyping an EVAR system is also presented

NASA space station automation: AI-based technology review

Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures

Design And Construction Of A Robotic Vehicle To Assist During Planetary Surface Operations

In the near future, astronauts will explore new planetary surfaces in the Solar System. To enable peak performance, these astronauts will need to utilize all of the tools at their disposal. It is proposed that one such tool is a planetary surface rover designed specifically to assist the astronauts during their Extra-Vehicular-Activities (EVA’s). This rover is designed and built to operate in concert with existing analog planetary surface infrastructure at the University of North Dakota (UND). This rover will be remotely controlled by an astronaut located on the planetary surface, enabling real-time operation and obstacle avoidance. The rover will act primarily as a relay for audio and video communications between the astronauts in the field and the Inflatable Lunar Habitat (ILH), or another planetary outpost. This rover will be designed to enable storage for tools and samples, freeing the astronauts from the tedious and physically demanding task of carrying items for long distances encumbered by an EVA suit. This thesis will describe the design of the rover and the rationale for each design decision. Upon completion of the rover, this thesis will report on the real-world performance of the rover, the effectiveness of the subsystems, and the lessons learned as a result of initial testing. Using the rover and the information obtained from this thesis, future astronaut-rover interaction studies will be conducted that will be important to the future of human planetary exploration

Doctor of Philosophy

dissertationClosed-loop control of wireless capsule endoscopes is an active area of research because it would drastically improve screening of the gastrointestinal tract. Traditional endoscopic procedures are unable to view the entire gastrointestinal tract and current commercial wireless capsule endoscopes are limited in their effectiveness due to their passive nature. This dissertation advances the field of active capsule endoscopy by developing methods to localize the full six-degree-of-freedom (6-DOF) pose of a screw-type magnetic capsule while it is being propelled through a lumen (such as the small intestines) using an external rotating magnetic dipole. The same external magnetic dipole is utilized for both propulsion and localization. Hardware was designed and constructed to enable testing of the magnetic localization and propulsion methods, including a robotic end-effector used as the external actuator magnet, and a prototype capsule embedded with Hall-effect sensors. Due to the use of a rotating magnetic field for propulsion, at any given time, the capsule can be in one of three regimes: synchronously rotating with the applied field, in "step-out" where it is free to move but the external field is rotating too quickly for the capsule to remain synchronously rotating, or completely stationary. We show that it is only necessary to distinguish whether or not the capsule is synchronously rotating (i.e., a single localization method can be used for a capsule in either the step-out or stationary regimes). Two magnetic localization methods are developed. The first uses nonlinear least squares to estimate the capsule's pose when it has no (or approximately no) net motion (e.g., to find the initial capsule pose or when it is stuck in an intestinal fold). The second method estimates the 6-DOF capsule pose as it synchronously rotates with the applied magnetic field using a square-root variant of the Unscented Kalman filter. A simple process model is adopted that restricts the capsule's movement to translation along and rotation about its principle axis. The capsule is actively propelled forward or backward, but it is not actively steered, rather, steering is provided by the lumen. The propulsion parameters that transform magnetic force and torque to the capsule's spatial velocity and angular velocity are estimated with an additional square-root Unscented Kalman filter to enable the capsule to navigate heterogeneous environments such as the small intestines. An optimized localization-propulsion system is described using the two localization algorithms and prior work in screw-type magnetic capsule propulsion with a single rotating dipole field. The capsule's regime is determined and the corresponding localization method is employed. Based on the capsule's estimated pose and the current estimates of its propulsion parameters, the actuator magnet's pose relative to the capsule is optimized to maximize the capsule's forward propulsion. Using this system, our prototype magnetic capsule successfully completed U-shaped and S-shaped trajectories in fresh bovine intestines with an average forward velocity of 5.5mm/s and 3.5 mm/s, respectively. At this rate it would take approximately 18-30 minutes to traverse the 6 meters of a typical human small intestine