Robotic Applications At Kennedy Space Center

McDonnell Douglas recently performed a study, Ref [1], to find effective application of robots and their associated technology at the Kennedy Space Center (KSC). Specifically, this study was directed towards the newly planned Space Station Processing Facility (SSPF). Because the Operations and Checkout (O&C) building has a similar charter to that of the SSPF, the O&C was carefully checked for potential robotic applications. Eleven applications were discovered and a trade study developed to rate these applications. Twenty more applications external to the SSPF were found during additional studies. These robotic tasks fall into three major categories including: teleoperated robots for hazardous tasks, mobile robots -for repetitive tasks and feedback compensated robots for refurbishment and inspection tasks. This paper will highlight some of the requirements for these tasks and others external to the SSPF. Additionally, the resources available at KSC will be discussed

Human factors in space telepresence

The problems of interfacing a human with a teleoperation system, for work in space are discussed. Much of the information presented here is the result of experience gained by the M.I.T. Space Systems Laboratory during the past two years of work on the ARAMIS (Automation, Robotics, and Machine Intelligence Systems) project. Many factors impact the design of the man-machine interface for a teleoperator. The effects of each are described in turn. An annotated bibliography gives the key references that were used. No conclusions are presented as a best design, since much depends on the particular application desired, and the relevant technology is swiftly changing

Estimating and understanding motion : from diagnostic to robotic surgery

Estimating and understanding motion from an image sequence is a central topic in computer vision. The high interest in this topic is because we are living in a world where many events that occur in the environment are dynamic. This makes motion estimation and understanding a natural component and a key factor in a widespread of applications including object recognition , 3D shape reconstruction, autonomous navigation and medica! diagnosis. Particularly, we focus on the medical domain in which understanding the human body for clinical purposes requires retrieving the organs' complex motion patterns, which is in general a hard problem when using only image data. In this thesis, we cope with this problem by posing the question - How to achieve a realistic motion estimation to offer a better clinical understanding? We focus this thesis on answering this question by using a variational formulation as a basis to understand one of the most complex motions in the human's body, the heart motion, through three different applications: (i) cardiac motion estimation for diagnostic, (ii) force estimation and (iii) motion prediction, both for robotic surgery. Firstly, we focus on a central topic in cardiac imaging that is the estimation of the cardiac motion. The main aim is to offer objective and understandable measures to physicians for helping them in the diagnostic of cardiovascular diseases. We employ ultrafast ultrasound data and tools for imaging motion drawn from diverse areas such as low-rank analysis and variational deformation to perform a realistic cardiac motion estimation. The significance is that by taking low-rank data with carefully chosen penalization, synergies in this complex variational problem can be created. We demonstrate how our proposed solution deals with complex deformations through careful numerical experiments using realistic and simulated data. We then move from diagnostic to robotic surgeries where surgeons perform delicate procedures remotely through robotic manipulators without directly interacting with the patients. As a result, they lack force feedback, which is an important primary sense for increasing surgeon-patient transparency and avoiding injuries and high mental workload. To solve this problem, we follow the conservation principies of continuum mechanics in which it is clear that the change in shape of an elastic object is directly proportional to the force applied. Thus, we create a variational framework to acquire the deformation that the tissues undergo due to an applied force. Then, this information is used in a learning system to find the nonlinear relationship between the given data and the applied force. We carried out experiments with in-vivo and ex-vivo data and combined statistical, graphical and perceptual analyses to demonstrate the strength of our solution. Finally, we explore robotic cardiac surgery, which allows carrying out complex procedures including Off-Pump Coronary Artery Bypass Grafting (OPCABG). This procedure avoids the associated complications of using Cardiopulmonary Bypass (CPB) since the heart is not arrested while performing the surgery on a beating heart. Thus, surgeons have to deal with a dynamic target that compromisetheir dexterity and the surgery's precision. To compensate the heart motion, we propase a solution composed of three elements: an energy function to estimate the 3D heart motion, a specular highlight detection strategy and a prediction approach for increasing the robustness of the solution. We conduct evaluation of our solution using phantom and realistic datasets. We conclude the thesis by reporting our findings on these three applications and highlight the dependency between motion estimation and motion understanding at any dynamic event, particularly in clinical scenarios.L’estimació i comprensió del moviment dins d’una seqüència d’imatges és un tema central en la visió per ordinador, el que genera un gran interès perquè vivim en un entorn ple d’esdeveniments dinàmics. Per aquest motiu és considerat com un component natural i factor clau dins d’un ampli ventall d’aplicacions, el qual inclou el reconeixement d’objectes, la reconstrucció de formes tridimensionals, la navegació autònoma i el diagnòstic de malalties. En particular, ens situem en l’àmbit mèdic en el qual la comprensió del cos humà, amb finalitats clíniques, requereix l’obtenció de patrons complexos de moviment dels òrgans. Aquesta és, en general, una tasca difícil quan s’utilitzen només dades de tipus visual. En aquesta tesi afrontem el problema plantejant-nos la pregunta - Com es pot aconseguir una estimació realista del moviment amb l’objectiu d’oferir una millor comprensió clínica? La tesi se centra en la resposta mitjançant l’ús d’una formulació variacional com a base per entendre un dels moviments més complexos del cos humà, el del cor, a través de tres aplicacions: (i) estimació del moviment cardíac per al diagnòstic, (ii) estimació de forces i (iii) predicció del moviment, orientant-se les dues últimes en cirurgia robòtica. En primer lloc, ens centrem en un tema principal en la imatge cardíaca, que és l’estimació del moviment cardíac. L’objectiu principal és oferir als metges mesures objectives i comprensibles per ajudar-los en el diagnòstic de les malalties cardiovasculars. Fem servir dades d’ultrasons ultraràpids i eines per al moviment d’imatges procedents de diverses àrees, com ara l’anàlisi de baix rang i la deformació variacional, per fer una estimació realista del moviment cardíac. La importància rau en que, en prendre les dades de baix rang amb una penalització acurada, es poden crear sinergies en aquest problema variacional complex. Mitjançant acurats experiments numèrics, amb dades realístiques i simulades, hem demostrat com les nostres propostes solucionen deformacions complexes. Després passem del diagnòstic a la cirurgia robòtica, on els cirurgians realitzen procediments delicats remotament, a través de manipuladors robòtics, sense interactuar directament amb els pacients. Com a conseqüència, no tenen la percepció de la força com a resposta, que és un sentit primari important per augmentar la transparència entre el cirurgià i el pacient, per evitar lesions i per reduir la càrrega de treball mental. Resolem aquest problema seguint els principis de conservació de la mecànica del medi continu, en els quals està clar que el canvi en la forma d’un objecte elàstic és directament proporcional a la força aplicada. Per això hem creat un marc variacional que adquireix la deformació que pateixen els teixits per l’aplicació d’una força. Aquesta informació s’utilitza en un sistema d’aprenentatge, per trobar la relació no lineal entre les dades donades i la força aplicada. Hem dut a terme experiments amb dades in-vivo i ex-vivo i hem combinat l’anàlisi estadístic, gràfic i de percepció que demostren la robustesa de la nostra solució. Finalment, explorem la cirurgia cardíaca robòtica, la qual cosa permet realitzar procediments complexos, incloent la cirurgia coronària sense bomba (off-pump coronary artery bypass grafting o OPCAB). Aquest procediment evita les complicacions associades a l’ús de circulació extracorpòria (Cardiopulmonary Bypass o CPB), ja que el cor no s’atura mentre es realitza la cirurgia. Això comporta que els cirurgians han de tractar amb un objectiu dinàmic que compromet la seva destresa i la precisió de la cirurgia. Per compensar el moviment del cor, proposem una solució composta de tres elements: un funcional d’energia per estimar el moviment tridimensional del cor, una estratègia de detecció de les reflexions especulars i una aproximació basada en mètodes de predicció, per tal d’augmentar la robustesa de la solució. L’avaluació de la nostra solució s’ha dut a terme mitjançant conjunts de dades sintètiques i realistes. La tesi conclou informant dels nostres resultats en aquestes tres aplicacions i posant de relleu la dependència entre l’estimació i la comprensió del moviment en qualsevol esdeveniment dinàmic, especialment en escenaris clínics.Postprint (published version

Position / force control of systems subjected to communicaton delays and interruptions in bilateral teleoperation

Thesis (Master)--Izmir Institute of Technology, Mechanical Engineering, Izmir, 2012Includes bibliographical references (leaves: 65-68)Text in English; Abstract: Turkish and Englishix, 76 leavesTeleoperation technology allows to remotely operate robotic (slave) systems located in hazardous, risky and distant environments. The human operator sends commands through the controller (master) system to execute the tasks from a distance. The operator is provided with necessary (visual, audio or haptic) feedback to accomplish the mission remotely. In bilateral teleoperation, continuous feedback from the remote environment is generated. Thus, the operator can handle the task as if the operator is in the remote environment relying on the relevant feedback. Since teleoperation deals with systems controlled from a distance, time delays and package losses in transmission of information are present. These communication failures affect the human perception and system stability, and thus, the ability of operator to handle the task successfully. The objective of this thesis is to investigate and develop a control algorithm, which utilizes model mediated teleoperation integrating parallel position/force controllers, to compensate for the instability issues and excessive forcing applied to the environment arising from communication failures. Model mediation technique is extended for three-degrees-of-freedom teleoperation and a parallel position/force controller, impedance controller, is integrated in the control algorithm. The proposed control method is experimentally tested by using Matlab Simulink blocksets for real-time experimentation in which haptic desktop devices, Novint Falcon and Phantom Desktop are configured as master and slave subsystems of the bilateral teleoperation. The results of these tests indicate that the stability and passivity of proposed bilateral teleoperation systems are preserved during constant and variable time delays and data losses while the position and force tracking test results provide acceptable performance with bounded errors

High Frequency Acceleration Feedback Significantly Increases the Realism of Haptically Rendered Textured Surfaces

Almost every physical interaction generates high frequency vibrations, especially if one of the objects is a rigid tool. Previous haptics research has hinted that the inclusion or exclusion of these signals plays a key role in the realism of haptically rendered surface textures, but this connection has not been formally investigated until now. This paper presents a human subject study that compares the performance of a variety of surface rendering algorithms for a master-slave teleoperation system; each controller provides the user with a different combination of position and acceleration feedback, and subjects compared the renderings with direct tool-mediated exploration of the real surface. We use analysis of variance to examine quantitative performance metrics and qualitative realism ratings across subjects. The results of this study show that algorithms that include high-frequency acceleration feedback in combination with position feedback achieve significantly higher realism ratings than traditional position feedback alone. Furthermore, we present a frequency-domain metric for quantifying a controller\u27s acceleration feedback performance; given a constant surface stiffness, the median of this metric across subjects was found to have a significant positive correlation with median realism rating

Sensorless Haptic Force Feedback for Telemanipulation using two identical Delta Robots

Bilateral teleoperation allows users to interact with objects in remote environments by providing the operator with haptic feedback. In this thesis two control scheme have been implemented in order to guarantee stability and transparency to the system: a position-position control scheme with gravity and passivity compensation and a bilateral force sensorless acceleration control implemented with Kalman filters and disturbance observers. Both methods were tested using two identical Delta robot