Robot Assisted Object Manipulation for Minimally Invasive Surgery

Robotic systems have an increasingly important role in facilitating minimally invasive surgical treatments. In robot-assisted minimally invasive surgery, surgeons remotely control instruments from a console to perform operations inside the patient. However, despite the advanced technological status of surgical robots, fully autonomous systems, with decision-making capabilities, are not yet available. In 2017, a structure to classify the research efforts toward autonomy achievable with surgical robots was proposed by Yang et al. Six different levels were identified: no autonomy, robot assistance, task autonomy, conditional autonomy, high autonomy, and full autonomy. All the commercially available platforms in robot-assisted surgery is still in level 0 (no autonomy). Despite increasing the level of autonomy remains an open challenge, its adoption could potentially introduce multiple benefits, such as decreasing surgeons’ workload and fatigue and pursuing a consistent quality of procedures. Ultimately, allowing the surgeons to interpret the ample and intelligent information from the system will enhance the surgical outcome and positively reflect both on patients and society. Three main aspects are required to introduce automation into surgery: the surgical robot must move with high precision, have motion planning capabilities and understand the surgical scene. Besides these main factors, depending on the type of surgery, there could be other aspects that might play a fundamental role, to name some compliance, stiffness, etc. This thesis addresses three technological challenges encountered when trying to achieve the aforementioned goals, in the specific case of robot-object interaction. First, how to overcome the inaccuracy of cable-driven systems when executing fine and precise movements. Second, planning different tasks in dynamically changing environments. Lastly, how the understanding of a surgical scene can be used to solve more than one manipulation task. To address the first challenge, a control scheme relying on accurate calibration is implemented to execute the pick-up of a surgical needle. Regarding the planning of surgical tasks, two approaches are explored: one is learning from demonstration to pick and place a surgical object, and the second is using a gradient-based approach to trigger a smoother object repositioning phase during intraoperative procedures. Finally, to improve scene understanding, this thesis focuses on developing a simulation environment where multiple tasks can be learned based on the surgical scene and then transferred to the real robot. Experiments proved that automation of the pick and place task of different surgical objects is possible. The robot was successfully able to autonomously pick up a suturing needle, position a surgical device for intraoperative ultrasound scanning and manipulate soft tissue for intraoperative organ retraction. Despite automation of surgical subtasks has been demonstrated in this work, several challenges remain open, such as the capabilities of the generated algorithm to generalise over different environment conditions and different patients

Robotic Crop Interaction in Agriculture for Soft Fruit Harvesting

Autonomous tree crop harvesting has been a seemingly attainable, but elusive, robotics goal for the past several decades. Limiting grower reliance on uncertain seasonal labour is an economic driver of this, but the ability of robotic systems to treat each plant individually also has environmental benefits, such as reduced emissions and fertiliser use. Over the same time period, effective grasping and manipulation (G&M) solutions to warehouse product handling, and more general robotic interaction, have been demonstrated. Despite research progress in general robotic interaction and harvesting of some specific crop types, a commercially successful robotic harvester has yet to be demonstrated. Most crop varieties, including soft-skinned fruit, have not yet been addressed. Soft fruit, such as plums, present problems for many of the techniques employed for their more robust relatives and require special focus when developing autonomous harvesters. Adapting existing robotics tools and techniques to new fruit types, including soft skinned varieties, is not well explored. This thesis aims to bridge that gap by examining the challenges of autonomous crop interaction for the harvesting of soft fruit. Aspects which are known to be challenging include mixed obstacle planning with both hard and soft obstacles present, poor outdoor sensing conditions, and the lack of proven picking motion strategies. Positioning an actuator for harvesting requires solving these problems and others specific to soft skinned fruit. Doing so effectively means addressing these in the sensing, planning and actuation areas of a robotic system. Such areas are also highly interdependent for grasping and manipulation tasks, so solutions need to be developed at the system level. In this thesis, soft robotics actuators, with simplifying assumptions about hard obstacle planes, are used to solve mixed obstacle planning. Persistent target tracking and filtering is used to overcome challenging object detection conditions, while multiple stages of object detection are applied to refine these initial position estimates. Several picking motions are developed and tested for plums, with varying degrees of effectiveness. These various techniques are integrated into a prototype system which is validated in lab testing and extensive field trials on a commercial plum crop. Key contributions of this thesis include I. The examination of grasping & manipulation tools, algorithms, techniques and challenges for harvesting soft skinned fruit II. Design, development and field-trial evaluation of a harvester prototype to validate these concepts in practice, with specific design studies of the gripper type, object detector architecture and picking motion for this III. Investigation of specific G&M module improvements including: o Application of the autocovariance least squares (ALS) method to noise covariance matrix estimation for visual servoing tasks, where both simulated and real experiments demonstrated a 30% improvement in state estimation error using this technique. o Theory and experimentation showing that a single range measurement is sufficient for disambiguating scene scale in monocular depth estimation for some datasets. o Preliminary investigations of stochastic object completion and sampling for grasping, active perception for visual servoing based harvesting, and multi-stage fruit localisation from RGB-Depth data. Several field trials were carried out with the plum harvesting prototype. Testing on an unmodified commercial plum crop, in all weather conditions, showed promising results with a harvest success rate of 42%. While a significant gap between prototype performance and commercial viability remains, the use of soft robotics with carefully chosen sensing and planning approaches allows for robust grasping & manipulation under challenging conditions, with both hard and soft obstacles

A survey of single and multi-UAV aerial manipulation

Aerial manipulation has direct application prospects in environment, construction, forestry, agriculture, search, and rescue. It can be used to pick and place objects and hence can be used for transportation of goods. Aerial manipulation can be used to perform operations in environments inaccessible or unsafe for human workers. This paper is a survey of recent research in aerial manipulation. The aerial manipulation research has diverse aspects, which include the designing of aerial manipulation platforms, manipulators, grippers, the control of aerial platform and manipulators, the interaction of aerial manipulator with the environment, through forces and torque. In particular, the review paper presents the survey of the airborne platforms that can be used for aerial manipulation including the new aerial platforms with aerial manipulation capability. We also classified the aerial grippers and aerial manipulators based on their designs and characteristics. The recent contributions regarding the control of the aerial manipulator platform is also discussed. The environment interaction of aerial manipulators is also surveyed which includes, different strategies used for end-effectors interaction with the environment, application of force, application of torque and visual servoing. A recent and growing interest of researchers about the multi-UAV collaborative aerial manipulation was also noticed and hence different strategies for collaborative aerial manipulation are also surveyed, discussed and critically analyzed. Some key challenges regarding outdoor aerial manipulation and energy constraints in aerial manipulation are also discussed

Development of an Intelligent Robotic Manipulator

The presence of hazards to human health in chemical process plant and nuclear waste stores leads to the use of robots and more specifically manipulators in unmanned spaces. Rapid and accurate performance of robotic arm movement and positioning, coupled with a reliable manipulator gripping mechanism for variable orientation and a range of deformable and/or geometric and coloured products, will lead to smarter/intelligent operation of high precision equipment. The aim of the research is to design a more effective robot arm manipulator for use in a glovebox environment utilising control kinematics together with image processing / object recognition algorithms and in particular the work is aimed at improving the movement of the robot arm in the case of unresolved kinematics, seeking improved speed and performance of object recognition along with improved sensitivity of the manipulator gripper mechanism A virtual robot arm and associated workspace was designed within the LabView 2009 environment and prototype gripper arms were designed and analysed within the Solidworks 2009 environment. Visual information was acquired by barrel cameras. Field research determines the location of identically shaped objects, and the object recognition algorithms establish the difference between them. A touch/feel device installed within the gripper arm housing ensures that the applied force is adequate to securely grasp the object without damage, but also to adapt to any slippage whilst the manipulator moves within the robot workspace. The research demonstrates that complex operations can be achieved without the expense of specialised parts/components; and that implementation of control algorithms can compensate for any ambiguous signals or fault conditions that occur through the operation of the manipulator. The results show that system performance is determined by the trade-off between speed and accuracy. The designed system can be further utilised for control of multi-functional robots connected within a production line. The Graphic User Interface illustrated within the thesis can be customised by the supervisor to suit operational needs

RGBD Camera Pose Estimation Techniques, Slip Detection, and Occluded Object Search Strategies for Deformable Linear Object Features in Autonomous Robotic Space Task Execution

This thesis studies Robotic handling of Deformable Linear Objects (DLO). Many habitats used for space exploration include panels with multiple wires and connections which can be easily reconfigured by humans but very difficult to be handled autonomously by robotic systems due to the flexible nature of the wires. In some situations, the wires can come loose and get separated from their connections resulting in malfunctioning of some onboard systems. This thesis develops methods for autonomous handling of flexible wires (deformable linear objects) involving the unplugging and re-plugging or stowing of one end of the wire from a connection point. An anomaly situation may arise when the end of a gripped DLO slips away from the robotic end effector into the environment while being maneuvered, entering the object into anunknown state. The objective of the research presented herein was to use purely visual sensing to detect this DLO slip locating the loose connector end, estimating its pose, and autonomously developing a motion plan for retrieval and delivery of the connector end to its originally intended destination. Three pose estimation methods are implemented: employing fiducial markers, RGBD image processing, and machine learning algorithms to generate the pose of the end of the DLO being manipulated. Experiments are performed using two cooperating robotic arms that show identification rates of 48.1%, 100.0%, and 77.8% and arm retrieval grasp rates of 48.1%, 74.1%, and 64.0% respectively among 27 trials. The identification rate varied based on the level of occlusion of the DLO end within the workspace. Slip detection is accomplished by comparing this estimated position’s distance to the manipulating arm’s end effector against a threshold quantifying a slip, producing a success rate of 77.2% from 18 slip trials. In the event that the loose connector settles out of the camera’s view, a spiral search pattern was designed to maneuver the secondary camera for further workspace inspection, with a search identification rate of 91.7% in 36 trials. The effectiveness of the overall system as a solution for anomaly detection and resolution is exhibited through three demonstrations with varying environmental configurations

Vision-based Robotic Grasping in Simulation using Deep Reinforcement Learning

This thesis will investigate different robotic manipulation and grasping approaches. It will present an overview of robotic simulation environments, and offer an evaluation of PyBullet, CoppeliaSim, and Gazebo, comparing various features. The thesis further presents a background for current approaches to robotic manipulation and grasping by describing how the robotic movement and grasping can be organized. State-of-the-Art approaches for learning robotic grasping, both using supervised methods and reinforcement learning methods are presented. Two set of experiments will be conducted in PyBullet, illustrating how Deep Reinforcement Learning methods could be applied to train a 7 degrees of freedom robotic arm to grasp objects

Toward Image-Guided Automated Suture Grasping Under Complex Environments: A Learning-Enabled and Optimization-Based Holistic Framework

To realize a higher-level autonomy of surgical knot tying in minimally invasive surgery (MIS), automated suture grasping, which bridges the suture stitching and looping procedures, is an important yet challenging task needs to be achieved. This paper presents a holistic framework with image-guided and automation techniques to robotize this operation even under complex environments. The whole task is initialized by suture segmentation, in which we propose a novel semi-supervised learning architecture featured with a suture-aware loss to pertinently learn its slender information using both annotated and unannotated data. With successful segmentation in stereo-camera, we develop a Sampling-based Sliding Pairing (SSP) algorithm to online optimize the suture's 3D shape. By jointly studying the robotic configuration and the suture's spatial characteristics, a target function is introduced to find the optimal grasping pose of the surgical tool with Remote Center of Motion (RCM) constraints. To compensate for inherent errors and practical uncertainties, a unified grasping strategy with a novel vision-based mechanism is introduced to autonomously accomplish this grasping task. Our framework is extensively evaluated from learning-based segmentation, 3D reconstruction, and image-guided grasping on the da Vinci Research Kit (dVRK) platform, where we achieve high performances and successful rates in perceptions and robotic manipulations. These results prove the feasibility of our approach in automating the suture grasping task, and this work fills the gap between automated surgical stitching and looping, stepping towards a higher-level of task autonomy in surgical knot tying

Compliant aerial manipulation.

The aerial manipulation is a research field which proposes the integration of robotic manipulators in aerial platforms, typically multirotors – widely known as “drones” – or autonomous helicopters. The development of this technology is motivated by the convenience to reduce the time, cost and risk associated to the execution of certain operations or tasks in high altitude areas or difficult access workspaces. Some illustrative application examples are the detection and insulation of leaks in pipe structures in chemical plants, repairing the corrosion in the blades of wind turbines, the maintenance of power lines, or the installation and retrieval of sensor devices in polluted areas. Although nowadays it is possible to find a wide variety of commercial multirotor platforms with payloads from a few gramps up to several kilograms, and flight times around thirty minutes, the development of an aerial manipulator is still a technological challenge due to the strong requirements relative to the design of the manipulator in terms of very low weight, low inertia, dexterity, mechanical robustness and control. The main contribution of this thesis is the design, development and experimental validation of several prototypes of lightweight (<2 kg) and compliant manipulators to be integrated in multirotor platforms, including human-size dual arm systems, compliant joint arms equipped with human-like finger modules for grasping, and long reach aerial manipulators. Since it is expected that the aerial manipulator is capable to execute inspection and maintenance tasks in a similar way a human operator would do, this thesis proposes a bioinspired design approach, trying to replicate the human arm in terms of size, kinematics, mass distribution, and compliance. This last feature is actually one of the key concepts developed and exploited in this work. Introducing a flexible element such as springs or elastomers between the servos and the links extends the capabilities of the manipulator, allowing the estimation and control of the torque/force, the detection of impacts and overloads, or the localization of obstacles by contact. It also improves safety and efficiency of the manipulator, especially during the operation on flight or in grabbing situations, where the impacts and contact forces may damage the manipulator or destabilize the aerial platform. Unlike most industrial manipulators, where force-torque control is possible at control rates above 1 kHz, the servo actuators typically employed in the development of aerial manipulators present important technological limitations: no torque feedback nor control, only position (and in some models, speed) references, low update rates (<100 Hz), and communication delays. However, these devices are still the best solution due to their high torque to weight ratio, low cost, compact design, and easy assembly and integration. In order to cope with these limitations, the compliant joint arms presented here estimate and control the wrenches from the deflection of the spring-lever transmission mechanism introduced in the joints, measured at joint level with encoders or potentiometers, or in the Cartesian space employing vision sensors. Note that in the developed prototypes, the maximum joint deflection is around 25 degrees, which corresponds to a deviation in the position of the end effector around 20 cm for a human-size arm. The capabilities and functionalities of the manipulators have been evaluated in fixed base test-bench firstly, and then in outdoor flight tests, integrating the arms in different commercial hexarotor platforms. Frequency characterization, position/force/impedance control, bimanual grasping, arm teleoperation, payload mass estimation, or contact-based obstacle localization are some of the experiments presented in this thesis that validate the developed prototypes.La manipulación aérea es un campo de investigación que propone la integración de manipuladores robóticos in plataformas aéreas, típicamente multirotores – comúnmente conocidos como “drones” – o helicópteros autónomos. El desarrollo de esta tecnología está motivada por la conveniencia de reducir el tiempo, coste y riesgo asociado a la ejecución de ciertas operaciones o tareas en áreas de gran altura o espacios de trabajo de difícil acceso. Algunos ejemplos ilustrativos de aplicaciones son la detección y aislamiento de fugas en estructura de tuberías en plantas químicas, la reparación de la corrosión en las palas de aerogeneradores, el mantenimiento de líneas eléctricas, o la instalación y recuperación de sensores en zonas contaminadas. Aunque hoy en día es posible encontrar una amplia variedad de plataformas multirotor comerciales con cargas de pago desde unos pocos gramos hasta varios kilogramos, y tiempo de vuelo entorno a treinta minutos, el desarrollo de los manipuladores aéreos es todavía un desafío tecnológico debido a los exigentes requisitos relativos al diseño del manipulador en términos de muy bajo peso, baja inercia, destreza, robustez mecánica y control. La contribución principal de esta tesis es el diseño, desarrollo y validación experimental de varios prototipos de manipuladores de bajo peso (<2 kg) con capacidad de acomodación (“compliant”) para su integración en plataformas aéreas multirotor, incluyendo sistemas bi-brazo de tamaño humano, brazos robóticos de articulaciones flexibles con dedos antropomórficos para agarre, y manipuladores aéreos de largo alcance. Puesto que se prevé que el manipulador aéreo sea capaz de ejecutar tareas de inspección y mantenimiento de forma similar a como lo haría un operador humano, esta tesis propone un enfoque de diseño bio-inspirado, tratando de replicar el brazo humano en cuanto a tamaño, cinemática, distribución de masas y flexibilidad. Esta característica es de hecho uno de los conceptos clave desarrollados y utilizados en este trabajo. Al introducir un elemento elástico como los muelles o elastómeros entre el los actuadores y los enlaces se aumenta las capacidades del manipulador, permitiendo la estimación y control de las fuerzas y pares, la detección de impactos y sobrecargas, o la localización de obstáculos por contacto. Además mejora la seguridad y eficiencia del manipulador, especialmente durante las operaciones en vuelo, donde los impactos y fuerzas de contacto pueden dañar el manipulador o desestabilizar la plataforma aérea. A diferencia de la mayoría de manipuladores industriales, donde el control de fuerzas y pares es posible a tasas por encima de 1 kHz, los servo motores típicamente utilizados en el desarrollo de manipuladores aéreos presentan importantes limitaciones tecnológicas: no hay realimentación ni control de torque, sólo admiten referencias de posición (o bien de velocidad), y presentan retrasos de comunicación. Sin embargo, estos dispositivos son todavía la mejor solución debido al alto ratio de torque a peso, por su bajo peso, diseño compacto y facilidad de ensamblado e integración. Para suplir estas limitaciones, los brazos robóticos flexibles presentados aquí permiten estimar y controlar las fuerzas a partir de la deflexión del mecanismo de muelle-palanca introducido en las articulaciones, medida a nivel articular mediante potenciómetros o codificadores, o en espacio Cartesiano mediante sensores de visión. Tómese como referencia que en los prototipos desarrollados la máxima deflexión articular es de unos 25 grados, lo que corresponde a una desviación de posición en torno a 20 cm en el efector final para un brazo de tamaño humano. Las capacidades y funcionalidades de estos manipuladores se han evaluado en base fija primero, y luego en vuelos en exteriores, integrando los brazos en diferentes plataformas hexartor comerciales. Caracterización frecuencial, control de posición/fuerza/impedancia, agarre bimanual, teleoperación de brazos, estimación de carga, o la localización de obstáculos mediante contacto son algunos de los experimentos presentados en esta tesis para validar los prototipos desarrollados por el auto

TWINBOT: Autonomous Underwater Cooperative Transportation

Underwater Inspection, Maintenance, and Repair operations are nowadays performed using Remotely Operated Vehicles (ROV) deployed from dynamic-positioning vessels, having high daily operational costs. During the last twenty years, the research community has been making an effort to design new Intervention Autonomous Underwater Vehicles (I-AUV), which could, in the near future, replace the ROVs, significantly decreasing these costs. Until now, the experimental work using I-AUVs has been limited to a few single-vehicle interventions, including object search and recovery, valve turning, and hot stab operations. More complex scenarios usually require the cooperation of multiple agents, i.e., the transportation of large and heavy objects. Moreover, using small, autonomous vehicles requires consideration of their limited load capacity and limited manipulation force/torque capabilities. Following the idea of multi-agent systems, in this paper we propose a possible solution: using a group of cooperating I-AUVs, thus sharing the load and optimizing the stress exerted on the manipulators. Specifically, we tackle the problem of transporting a long pipe. The presented ideas are based on a decentralized Task-Priority kinematic control algorithm adapted for the highly limited communication bandwidth available underwater. The aforementioned pipe is transported following a sequence of poses. A path-following algorithm computes the desired velocities for the robots’ end-effectors, and the on-board controllers ensure tracking of these setpoints, taking into account the geometry of the pipe and the vehicles’ limitations. The utilized algorithms and their practical implementation are discussed in detail and validated through extensive simulations and experimental trials performed in a test tank using two 8 DOF I-AUV