640 research outputs found

    Design, analysis and kinematic control of highly redundant serial robotic arms

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    The use of robotic manipulators in industry has grown in the last decades to improve and speed up industrial processes. Industrial manipulators started to be investigated for machining tasks since they can cover larger workspaces, increasing the range of achievable operations and improving flexibility. The company Nimbl’Bot developed a new mechanism, or module, to build stiffer flexible serial modular robots for machining applications. This manipulator is a kinematic redundant robot with 21 degrees of freedom. This thesis thoroughly analysis the Nimbl’Bot robot features and is divided into three main topics. The first topic regards using a task priority kinematic redundancy resolution algorithm for the Nimbl’Bot robot tracking trajectory while optimizing its kinetostatic performances. The second topic is the kinematic redundant robot design optimization with respect to a desired application and its kinetostatic performance. For the third topic, a new workspace determination algorithm is proposed for kinematic redundant manipulators. Several simulation tests are proposed and tested on some Nimbl’Bot robot designs for each subjects

    Decentralised State Feedback Tracking Control for Large-Scale Interconnected Systems Using Sliding Mode Techniques

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    A class of large-scale interconnected systems with matched and unmatched uncertainties is studied in this thesis, with the objective of proposing a controller based on diffeomorphisms and some techniques to deal with the tracking problem of the system. The main research developed in this thesis includes: 1. Large-scale interconnected system is a complex system consisting of several semi-independent subsystems, which are typically located in distinct geographic or logical locations. In this situation, decentralised control which only collects the local information is the practical method to deal with large-scale interconnected systems. The decentralised methodology is utilised throughout this thesis, guaranteeing that systems exhibit essential robustness against uncertainty. 2. Sliding mode technique is involved in the process of controller design. By introducing a nonsingular local diffeomorphism, the large-scale system can be transformed into a system with a specific regular form, where the matched uncertainty is completely absent from the subspace spanned by the sliding mode dynamics. The sliding mode based controller is proposed in this thesis to successfully achieve high robustness of the closed-loop interconnected systems with some particular uncertainties. 3. The considered large-scale interconnected systems can always track the smooth desired signals in a finite time. Each subsystem can track its own ideal signal or all subsystems can track the same ideal signal. Both situations are discussed in this thesis and the results are mathematically proven by introducing the Lyapunov theory, even when operating under the presence of disturbances. At the end of each chapter, some simulation examples, like a coupled inverted pendulums system, a river pollution system and a high-speed train system, are presented to verify the correctness of the proposed theory. At the conclusion of this thesis, a brief summary of the research findings has been provided, along with a mention of potential future research directions in tracking control of large-scale systems, like more general boundedness of interconnections, possibilities of distributed control, collaboration with intelligent control and so on. Some mathematical theories involved and simulation code are included in the appendix section

    Characterisation and State Estimation of Magnetic Soft Continuum Robots

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    Minimally invasive surgery has become more popular as it leads to less bleeding, scarring, pain, and shorter recovery time. However, this has come with counter-intuitive devices and steep surgeon learning curves. Magnetically actuated Soft Continuum Robots (SCR) have the potential to replace these devices, providing high dexterity together with the ability to conform to complex environments and safe human interactions without the cognitive burden for the clinician. Despite considerable progress in the past decade in their development, several challenges still plague SCR hindering their full realisation. This thesis aims at improving magnetically actuated SCR by addressing some of these challenges, such as material characterisation and modelling, and sensing feedback and localisation. Material characterisation for SCR is essential for understanding their behaviour and designing effective modelling and simulation strategies. In this work, the material properties of commonly employed materials in magnetically actuated SCR, such as elastic modulus, hyper-elastic model parameters, and magnetic moment were determined. Additionally, the effect these parameters have on modelling and simulating these devices was investigated. Due to the nature of magnetic actuation, localisation is of utmost importance to ensure accurate control and delivery of functionality. As such, two localisation strategies for magnetically actuated SCR were developed, one capable of estimating the full 6 degrees of freedom (DOFs) pose without any prior pose information, and another capable of accurately tracking the full 6-DOFs in real-time with positional errors lower than 4~mm. These will contribute to the development of autonomous navigation and closed-loop control of magnetically actuated SCR

    Evaluating EEG–EMG Fusion-Based Classification as a Method for Improving Control of Wearable Robotic Devices for Upper-Limb Rehabilitation

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    Musculoskeletal disorders are the biggest cause of disability worldwide, and wearable mechatronic rehabilitation devices have been proposed for treatment. However, before widespread adoption, improvements in user control and system adaptability are required. User intention should be detected intuitively, and user-induced changes in system dynamics should be unobtrusively identified and corrected. Developments often focus on model-dependent nonlinear control theory, which is challenging to implement for wearable devices. One alternative is to incorporate bioelectrical signal-based machine learning into the system, allowing for simpler controller designs to be augmented by supplemental brain (electroencephalography/EEG) and muscle (electromyography/EMG) information. To extract user intention better, sensor fusion techniques have been proposed to combine EEG and EMG; however, further development is required to enhance the capabilities of EEG–EMG fusion beyond basic motion classification. To this end, the goals of this thesis were to investigate expanded methods of EEG–EMG fusion and to develop a novel control system based on the incorporation of EEG–EMG fusion classifiers. A dataset of EEG and EMG signals were collected during dynamic elbow flexion–extension motions and used to develop EEG–EMG fusion models to classify task weight, as well as motion intention. A variety of fusion methods were investigated, such as a Weighted Average decision-level fusion (83.01 ± 6.04% accuracy) and Convolutional Neural Network-based input-level fusion (81.57 ± 7.11% accuracy), demonstrating that EEG–EMG fusion can classify more indirect tasks. A novel control system, referred to as a Task Weight Selective Controller (TWSC), was implemented using a Gain Scheduling-based approach, dictated by external load estimations from an EEG–EMG fusion classifier. To improve system stability, classifier prediction debouncing was also proposed to reduce misclassifications through filtering. Performance of the TWSC was evaluated using a developed upper-limb brace simulator. Due to simulator limitations, no significant difference in error was observed between the TWSC and PID control. However, results did demonstrate the feasibility of prediction debouncing, showing it provided smoother device motion. Continued development of the TWSC, and EEG–EMG fusion techniques will ultimately result in wearable devices that are able to adapt to changing loads more effectively, serving to improve the user experience during operation

    Constrained-Differential-Kinematics-Decomposition-Based NMPC for Online Manipulator Control with Low Computational Costs

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    Flexibility combined with the ability to consider external constraints comprises the main advantages of nonlinear model predictive control (NMPC). Applied as a motion controller, NMPC enables applications in varying and disturbed environments, but requires time-consuming computations. Hence, given the full nonlinear multi-DOF robot model, a delay-free execution providing short control horizons at appropriate prediction horizons for accurate motions is not applicable in common use. This contribution introduces an approach that analyzes and decomposes the differential kinematics similar to the inverse kinematics method to assign Cartesian boundary conditions to specific systems of equations during the model building, reducing the online computational costs. The resulting fully constrained NMPC realizes the translational obstacle avoidance during trajectory tracking using a reduced model considering both joint and Cartesian constraints coupled with a Jacobian transposed controller performing the end-effector’s orientation correction. Apart from a safe distance from the obstacles, the presented approach does not lead to any limitations of the reachable workspace, and all degrees of freedom (DOFs) of the robot are used. The simulative evaluation in Gazebo using the Stäubli TX2-90 commanded of ROS on a standard computer emphasizes the significantly lower online computational costs, accuracy analysis, and extended adaptability in obstacle avoidance, providing additional flexibility. An interpretation of the new concept is discussed for further use and extensions

    Limited Information Shared Control and its Applications to Large Vehicle Manipulators

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    Diese Dissertation beschäftigt sich mit der kooperativen Regelung einer mobilen Arbeitsmaschine, welche aus einem Nutzfahrzeug und einem oder mehreren hydraulischen Manipulatoren besteht. Solche Maschinen werden für Aufgaben in der Straßenunterhaltungsaufgaben eingesetzt. Die Arbeitsumgebung des Manipulators ist unstrukturiert, was die Bestimmung einer Referenztrajektorie erschwert oder unmöglich macht. Deshalb wird in dieser Arbeit ein Ansatz vorgeschlagen, welcher nur das Fahrzeug automatisiert, während der menschliche Bediener ein Teil des Systems bleibt und den Manipulator steuert. Eine solche Teilautomatisierung des Gesamtsystems führt zu einer speziellen Klasse von Mensch-Maschine-Interaktionen, welche in der Literatur noch nicht untersucht wurde: Eine kooperative Regelung zwischen zwei Teilsystemen, bei der die Automatisierung keine Informationen von dem vom Menschen gesteuerten Teilsystem hat. Deswegen wird in dieser Arbeit ein systematischer Ansatz der kooperativen Regelung mit begrenzter Information vorgestellt, der den menschlichen Bediener unterstützen kann, ohne die Referenzen oder die Systemzustände des Manipulators zu messen. Außerdem wird ein systematisches Entwurfskonzept für die kooperative Regelung mit begrenzter Information vorgestellt. Für diese Entwurfsmethode werden zwei neue Unterklassen der sogenannten Potenzialspiele eingeführt, die eine systematische Berechnung der Parameter der entwickelten kooperativen Regelung ohne manuelle Abstimmung ermöglichen. Schließlich wird das entwickelte Konzept der kooperativen Regelung am Beispiel einer großen mobilen Arbeitsmaschine angewandt, um seine Vorteile zu ermitteln und zu bewerten. Nach der Analyse in Simulationen wird die praktische Anwendbarkeit der Methode in drei Experimenten mit menschlichen Probanden an einem Simulator untersucht. Die Ergebnisse zeigen die Überlegenheit des entwickelten kooperativen Regelungskonzepts gegenüber der manuellen Steuerung und der nicht-kooperativen Steuerung hinsichtlich sowohl der objektiven Performanz als auch der subjektiven Bewertung der Probanden. Somit zeigt diese Dissertation, dass die kooperative Regelung mobiler Arbeitsmaschinen mit den entwickelten theoretischen Konzepten sowohl hilfreich als auch praktisch anwendbar ist

    Surgical Subtask Automation for Intraluminal Procedures using Deep Reinforcement Learning

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    Intraluminal procedures have opened up a new sub-field of minimally invasive surgery that use flexible instruments to navigate through complex luminal structures of the body, resulting in reduced invasiveness and improved patient benefits. One of the major challenges in this field is the accurate and precise control of the instrument inside the human body. Robotics has emerged as a promising solution to this problem. However, to achieve successful robotic intraluminal interventions, the control of the instrument needs to be automated to a large extent. The thesis first examines the state-of-the-art in intraluminal surgical robotics and identifies the key challenges in this field, which include the need for safe and effective tool manipulation, and the ability to adapt to unexpected changes in the luminal environment. To address these challenges, the thesis proposes several levels of autonomy that enable the robotic system to perform individual subtasks autonomously, while still allowing the surgeon to retain overall control of the procedure. The approach facilitates the development of specialized algorithms such as Deep Reinforcement Learning (DRL) for subtasks like navigation and tissue manipulation to produce robust surgical gestures. Additionally, the thesis proposes a safety framework that provides formal guarantees to prevent risky actions. The presented approaches are evaluated through a series of experiments using simulation and robotic platforms. The experiments demonstrate that subtask automation can improve the accuracy and efficiency of tool positioning and tissue manipulation, while also reducing the cognitive load on the surgeon. The results of this research have the potential to improve the reliability and safety of intraluminal surgical interventions, ultimately leading to better outcomes for patients and surgeons

    Mechatronic design solution for planar overconstrained cable-driven parallel robot

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    Cable-driven parallel robots (CDPRs) combine the successful features of parallel manipulators with the benefits of cable transmissions. The payload is divided among light extendable cables, resulting in an energy-efficient system that can achieve high end-effector acceleration over a huge workspace. A CDPR is formed by a set of actuation units, and a mobile platform, working as an end-effector (EE). The cables, driven by the actuation units, are guided inside the robot workspace using a guidance system and then connected to the mobile platform. The variation of cable lengths is responsible for the EE displacement throughout the robot workspace. These features result in easily reconfigurable systems where the workspace can be modified by relocating the actuation and guidance units. Nevertheless, the use of CDPRs in industrial environments is still limited, due to the drawbacks of employing flexible cables. Indeed, cables impose unilateral constraints that can only exert tensile forces and, consequently, the EE cannot withstand any arbitrary external action. To enhance the robot’s controllability, CDPRs can be overconstrained by employing a number of cables higher than the degrees of freedom of the EE. This allows cables to pull one against the other and to keep the overall system controllable over a wide range of externally applied loads. In this thesis, an eight-cable, planar, overconstrained CDPR is designed: the robot has the deployable and reconfigurable features required by the task. In particular, this CDPR has its actuation units installed into the EE mobile platform, and the frame anchor points can be rearranged to obtain a discrete reconfiguration. The cable arrangement, location of anchor points and mechanical design have been studied, by implementing a hybrid optimisation procedure. The genetic algorithm is combined with a local minimum optimiser, maximizing the CDPR volume index and deriving a mechanical design for the prototype
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