Navigation Control of an Automated Guided Underwater Robot using Neural Network Technique

In recent years, under water robots play an important role in various under water operations. There is an increase in research in this area because of the application of autonomous underwater robots in several issues like exploring under water environment and resource, doing scientific and military tasks under water. We need good maneuvering capabilities and a well precision for moving in a specified track in these applications. However, control of these under water bots become very difficult due to the highly non-linear and dynamic characteristics of the underwater world. The logical answer to this problem is the application of non-linear controllers. As neural networks (NNs) are characterized by flexibility and an aptitude for dealing with non-linear problems, they are envisaged to be beneficial when used on underwater robots. In this research our artificial intelligence system is based on neural network model for navigation of an Automated Underwater robot in unpredictable and imprecise environment. Thus the back propagation algorithm has been used for the steering analysis of the underwater robot when it is encountered by a left, right and front as well as top obstacle. After training the neural network the neural network pattern was used in the controller of the underwater robot. The simulation of underwater robot under various obstacle conditions are shown using MATLAB

Active Particles Bound by Information Flows

Self-organization is the generation of order out of local interactions in non-equilibrium [1]. It is deeply connected to all fields of science from physics, chemistry to biology where functional living structures self-assemble[2] and constantly evolve[3] all based on physical interactions. The emergence of collective animal behavior[4], of society or language are the results of self-organization processes as well though they involve abstract interactions arising from sensory inputs, information processing, storage and feedback[5-7]. Resulting collective behaviors are found for example in crowds of people, flocks of birds, schools of fish or swarms of bacteria[8,9]. Here we introduce such information based interactions to the behavior of active microparticles. A real time feedback of active particle positions controls the propulsion direction these active particles. The emerging structures are bound by dissipation and reveal frustrated geometries due to confinement to two dimensions. They diffuse like passive clusters of colloids, but possess internal dynamical degrees of freedom that are determined by the feed- back and the noise in the system. As the information processing in the feedback loops can be designed almost arbitrarily, new perspectives for self-organization studies involving coupled feedback systems with separate timescales, machine learning and swarm intelligence arise.Comment: 13 pages, 4 figure

A numerical study of fin and jet propulsions involving fluid-structure interactions

Fish swimming is elegant and efficient, which inspires humans to learn from them to design high-performance artificial underwater vehicles. Research on aquatic locomotion has made extensive progress towards a better understanding of how aquatic animals control their flexible body and fin for propulsion. Although the structural flexibility and deformation of the body and fin are believed to be important features to achieve optimal swimming performance, studies on high-fidelity deformable body and fin with complex material behavior, such as non-uniform stiffness distributions, are rare. In this thesis, a fully coupled three-dimensional high-fidelity fluid-structure interaction (FSI) solver is developed to investigate the flow field evolution and propulsion performance of caudal fin and jet propulsion involving body and/or fin deformation. Within this FSI solver, the fluid is resolved by solving unsteady and viscous Navier-Stokes equations based on the finite volume method with a multi-block grid system. The solid dynamics are solved by a nonlinear finite element method. The coupling between the two solvers is achieved in a partitioned approach in which convergence check and sub-iteration are implemented to ensure numerical stability and accuracy. Validations are conducted by comparing the simulation results of classical benchmarks with previous data in the literature, and good agreements between them are obtained. The developed FSI solver is then applied to study the bio-inspired fin and jet propulsion involving body deformation. Specifically, the effect of non-uniform stiffness distributions of fish body and/or fin, key features of fish swimming which have been excluded in most previous studies, on the propulsive performance is first investigated. Simulation results of a sunfish-like caudal fin model and a tuna-inspired swimmer model both show that larger thrust and propulsion efficiency can be achieved by a non-uniform stiffness distribution (e.g., increased by 11.2% and 9.9%, respectively, for the sunfish-like model) compared with a uniform stiffness profile. Despite the improved propulsive e performance, a bionic variable fish body stiffness does not yield fish-like midline kinematics observed in real fish, suggesting that fish movement involves significant active control that cannot be replicated purely by passive deformations. Subsequent studies focus on the jet propulsion inspired by squid locomotion using the developed numerical solver. Simulation results of a two-dimensional inflation-deflation jet propulsion system, whose inflation is actuated by an added external force that mimics the muscle constriction of the mantle and deflation is caused by the release of elastic energy of the structure, suggest larger mean thrust production and higher efficiency in high Reynolds number scenarios compared with the cases in laminar flow. A unique symmetry-breaking instability in turbulent flow is found to stem from irregular internal body vortices, which cause symmetry breaking in the wake. Besides, a three-dimensional squid-like jet propulsion system in the presence of background flow is studied by prescribing the body deformation and jet velocity profiles. The effect of the background flow on the leading vortex ring formation and jet propulsion is investigated, and the thrust sources of the overall pulsed jet are revealed as well. Finally, FSI analysis on motion control of a self-propelled flexible swimmer in front of a cylinder utilizing proportional-derivative (PD) control is conducted. The amplitude of the actuation force, which is applied to the swimmer to bend it to produce thrust, is dynamically tuned by a feedback PD controller to instruct the swimmer to swim the desired distance from an initial position to a target location and then hold the station there. Despite the same swimming distance, a swimmer whose departure location is closer to the cylinder requires less energy consumption to reach the target and hold the position there.Fish swimming is elegant and efficient, which inspires humans to learn from them to design high-performance artificial underwater vehicles. Research on aquatic locomotion has made extensive progress towards a better understanding of how aquatic animals control their flexible body and fin for propulsion. Although the structural flexibility and deformation of the body and fin are believed to be important features to achieve optimal swimming performance, studies on high-fidelity deformable body and fin with complex material behavior, such as non-uniform stiffness distributions, are rare. In this thesis, a fully coupled three-dimensional high-fidelity fluid-structure interaction (FSI) solver is developed to investigate the flow field evolution and propulsion performance of caudal fin and jet propulsion involving body and/or fin deformation. Within this FSI solver, the fluid is resolved by solving unsteady and viscous Navier-Stokes equations based on the finite volume method with a multi-block grid system. The solid dynamics are solved by a nonlinear finite element method. The coupling between the two solvers is achieved in a partitioned approach in which convergence check and sub-iteration are implemented to ensure numerical stability and accuracy. Validations are conducted by comparing the simulation results of classical benchmarks with previous data in the literature, and good agreements between them are obtained. The developed FSI solver is then applied to study the bio-inspired fin and jet propulsion involving body deformation. Specifically, the effect of non-uniform stiffness distributions of fish body and/or fin, key features of fish swimming which have been excluded in most previous studies, on the propulsive performance is first investigated. Simulation results of a sunfish-like caudal fin model and a tuna-inspired swimmer model both show that larger thrust and propulsion efficiency can be achieved by a non-uniform stiffness distribution (e.g., increased by 11.2% and 9.9%, respectively, for the sunfish-like model) compared with a uniform stiffness profile. Despite the improved propulsive e performance, a bionic variable fish body stiffness does not yield fish-like midline kinematics observed in real fish, suggesting that fish movement involves significant active control that cannot be replicated purely by passive deformations. Subsequent studies focus on the jet propulsion inspired by squid locomotion using the developed numerical solver. Simulation results of a two-dimensional inflation-deflation jet propulsion system, whose inflation is actuated by an added external force that mimics the muscle constriction of the mantle and deflation is caused by the release of elastic energy of the structure, suggest larger mean thrust production and higher efficiency in high Reynolds number scenarios compared with the cases in laminar flow. A unique symmetry-breaking instability in turbulent flow is found to stem from irregular internal body vortices, which cause symmetry breaking in the wake. Besides, a three-dimensional squid-like jet propulsion system in the presence of background flow is studied by prescribing the body deformation and jet velocity profiles. The effect of the background flow on the leading vortex ring formation and jet propulsion is investigated, and the thrust sources of the overall pulsed jet are revealed as well. Finally, FSI analysis on motion control of a self-propelled flexible swimmer in front of a cylinder utilizing proportional-derivative (PD) control is conducted. The amplitude of the actuation force, which is applied to the swimmer to bend it to produce thrust, is dynamically tuned by a feedback PD controller to instruct the swimmer to swim the desired distance from an initial position to a target location and then hold the station there. Despite the same swimming distance, a swimmer whose departure location is closer to the cylinder requires less energy consumption to reach the target and hold the position there

Motion representation with spiking neural networks for grasping and manipulation

Die Natur bedient sich Millionen von Jahren der Evolution, um adaptive physikalische Systeme mit effizienten Steuerungsstrategien zu erzeugen. Im Gegensatz zur konventionellen Robotik plant der Mensch nicht einfach eine Bewegung und führt sie aus, sondern es gibt eine Kombination aus mehreren Regelkreisen, die zusammenarbeiten, um den Arm zu bewegen und ein Objekt mit der Hand zu greifen. Mit der Forschung an humanoiden und biologisch inspirierten Robotern werden komplexe kinematische Strukturen und komplizierte Aktor- und Sensorsysteme entwickelt. Diese Systeme sind schwierig zu steuern und zu programmieren, und die klassischen Methoden der Robotik können deren Stärken nicht immer optimal ausnutzen. Die neurowissenschaftliche Forschung hat große Fortschritte beim Verständnis der verschiedenen Gehirnregionen und ihrer entsprechenden Funktionen gemacht. Dennoch basieren die meisten Modelle auf groß angelegten Simulationen, die sich auf die Reproduktion der Konnektivität und der statistischen neuronalen Aktivität konzentrieren. Dies öffnet eine Lücke bei der Anwendung verschiedener Paradigmen, um Gehirnmechanismen und Lernprinzipien zu validieren und Funktionsmodelle zur Steuerung von Robotern zu entwickeln. Ein vielversprechendes Paradigma ist die ereignis-basierte Berechnung mit SNNs. SNNs fokussieren sich auf die biologischen Aspekte von Neuronen und replizieren deren Arbeitsweise. Sie sind für spike- basierte Kommunikation ausgelegt und ermöglichen die Erforschung von Mechanismen des Gehirns für das Lernen mittels neuronaler Plastizität. Spike-basierte Kommunikation nutzt hoch parallelisierten Hardware-Optimierungen mittels neuromorpher Chips, die einen geringen Energieverbrauch und schnelle lokale Operationen ermöglichen. In dieser Arbeit werden verschiedene SNNs zur Durchführung von Bewegungss- teuerung für Manipulations- und Greifaufgaben mit einem Roboterarm und einer anthropomorphen Hand vorgestellt. Diese basieren auf biologisch inspirierten funktionalen Modellen des menschlichen Gehirns. Ein Motor-Primitiv wird auf parametrische Weise mit einem Aktivierungsparameter und einer Abbildungsfunktion auf die Roboterkinematik übertragen. Die Topologie des SNNs spiegelt die kinematische Struktur des Roboters wider. Die Steuerung des Roboters erfolgt über das Joint Position Interface. Um komplexe Bewegungen und Verhaltensweisen modellieren zu können, werden die Primitive in verschiedenen Schichten einer Hierarchie angeordnet. Dies ermöglicht die Kombination und Parametrisierung der Primitiven und die Wiederverwendung von einfachen Primitiven für verschiedene Bewegungen. Es gibt verschiedene Aktivierungsmechanismen für den Parameter, der ein Motorprimitiv steuert — willkürliche, rhythmische und reflexartige. Außerdem bestehen verschiedene Möglichkeiten neue Motorprimitive entweder online oder offline zu lernen. Die Bewegung kann entweder als Funktion modelliert oder durch Imitation der menschlichen Ausführung gelernt werden. Die SNNs können in andere Steuerungssysteme integriert oder mit anderen SNNs kombiniert werden. Die Berechnung der inversen Kinematik oder die Validierung von Konfigurationen für die Planung ist nicht erforderlich, da der Motorprimitivraum nur durchführbare Bewegungen hat und keine ungültigen Konfigurationen enthält. Für die Evaluierung wurden folgende Szenarien betrachtet, das Zeigen auf verschiedene Ziele, das Verfolgen einer Trajektorie, das Ausführen von rhythmischen oder sich wiederholenden Bewegungen, das Ausführen von Reflexen und das Greifen von einfachen Objekten. Zusätzlich werden die Modelle des Arms und der Hand kombiniert und erweitert, um die mehrbeinige Fortbewegung als Anwendungsfall der Steuerungsarchitektur mit Motorprimitiven zu modellieren. Als Anwendungen für einen Arm (3 DoFs) wurden die Erzeugung von Zeigebewegungen und das perzeptionsgetriebene Erreichen von Zielen modelliert. Zur Erzeugung von Zeigebewegun- gen wurde ein Basisprimitiv, das auf den Mittelpunkt einer Ebene zeigt, offline mit vier Korrekturprimitiven kombiniert, die eine neue Trajektorie erzeugen. Für das wahrnehmungsgesteuerte Erreichen eines Ziels werden drei Primitive online kombiniert unter Verwendung eines Zielsignals. Als Anwendungen für eine Fünf-Finger-Hand (9 DoFs) wurden individuelle Finger-aktivierungen und Soft-Grasping mit nachgiebiger Steuerung modelliert. Die Greif- bewegungen werden mit Motor-Primitiven in einer Hierarchie modelliert, wobei die Finger-Primitive die Synergien zwischen den Gelenken und die Hand-Primitive die unterschiedlichen Affordanzen zur Koordination der Finger darstellen. Für jeden Finger werden zwei Reflexe hinzugefügt, zum Aktivieren oder Stoppen der Bewegung bei Kontakt und zum Aktivieren der nachgiebigen Steuerung. Dieser Ansatz bietet enorme Flexibilität, da Motorprimitive wiederverwendet, parametrisiert und auf unterschiedliche Weise kombiniert werden können. Neue Primitive können definiert oder gelernt werden. Ein wichtiger Aspekt dieser Arbeit ist, dass im Gegensatz zu Deep Learning und End-to-End-Lernmethoden, keine umfangreichen Datensätze benötigt werden, um neue Bewegungen zu lernen. Durch die Verwendung von Motorprimitiven kann der gleiche Modellierungsansatz für verschiedene Roboter verwendet werden, indem die Abbildung der Primitive auf die Roboterkinematik neu definiert wird. Die Experimente zeigen, dass durch Motor- primitive die Motorsteuerung für die Manipulation, das Greifen und die Lokomotion vereinfacht werden kann. SNNs für Robotikanwendungen ist immer noch ein Diskussionspunkt. Es gibt keinen State-of-the-Art-Lernalgorithmus, es gibt kein Framework ähnlich dem für Deep Learning, und die Parametrisierung von SNNs ist eine Kunst. Nichtsdestotrotz können Robotikanwendungen - wie Manipulation und Greifen - Benchmarks und realistische Szenarien liefern, um neurowissenschaftliche Modelle zu validieren. Außerdem kann die Robotik die Möglichkeiten der ereignis- basierten Berechnung mit SNNs und neuromorpher Hardware nutzen. Die physikalis- che Nachbildung eines biologischen Systems, das vollständig mit SNNs implementiert und auf echten Robotern evaluiert wurde, kann neue Erkenntnisse darüber liefern, wie der Mensch die Motorsteuerung und Sensorverarbeitung durchführt und wie diese in der Robotik angewendet werden können. Modellfreie Bewegungssteuerungen, inspiriert von den Mechanismen des menschlichen Gehirns, können die Programmierung von Robotern verbessern, indem sie die Steuerung adaptiver und flexibler machen

A Numerical Study of Fish Adaption Behaviors by Deep Reinforcement Learning and Immersed Boundary–Lattice Boltzmann Method

The aim of this thesis is to study the adaption behaviors of self-propelled fish in complex environments. In order to do so, a numerical framework is first developed. In this framework, fish swimming in a viscous incompressible flow is simulated with the immersed boundary--lattice Boltzmann method (IB--LBM). Furthermore, a deep recurrent Q-network (DRQN) is incorporated with the IB--LBM to train the fish model to adapt its motion to optimally achieve a specific task, such as prey capture, rheotaxis and Kármán gaiting. Compared to existing models for fish, this work incorporates the fish position, velocity and acceleration into the state space in the DRQN; and it considers the amplitude and frequency action space as well as the historical effects. This framework makes use of the high computational efficiency of the IB--LBM which is of crucial importance for the effective coupling with learning algorithms. Test cases including point-to-point swimming in quiescent flow and position holding both in a uniform stream and a Kármán vortex street have been conducted to show the effectiveness of the proposed framework. With the proposed method, the effect of vision, superficial neuromast (SN) and canal neuromast (CN) in position holding swimming in a uniform flow are then investigated. It is found that the fish is able to hold position with all those sensory methods. The control with vision is most accurate while the control with CN information is least accurate. In addition, the combination of vision, SN and CN will not improve the control with only vision, but the combination of SN and CN outperforms SN or CN alone. The effect of the undulation frequency on fish's behavior in a Kármán vortex street is finally investigated. Result shows that the swimming is most stable and efficient when the fish is synchronizing its tail-beat frequency with the vortex shedding frequency. Higher undulation frequency will decrease the hydrodynamic efficiency, and lower undulation frequency will decrease swimming stability. The effect of the scale of the vortex street on fish behavior is also investigated. Smaller vortex street makes the swimming more stable and less efficient, while larger vortex street makes the swimming unstable but hydrodynamically efficient

Part clamping and fixture geometric adaptability for reconfigurable assembly systems.

Masters of Science in Mechanical Engineering. University of KwaZulu-Natal. Durban, 2017.The Fourth Industrial Revolution is leading towards cyber-physical systems which justified research efforts in pursuing efficient production systems incorporating flexible grippers. Due to the complexity of assembly processes, reconfigurable assembly systems have received considerable attention in recent years. The demand for the intricate task and complicated operations, demands the need for efficient robotic manipulators that are required to manoeuvre and grasp objects effectively. Investigations were performed to understand the requirements of efficient gripping systems and existing gripping methods. A biologically inspired robotic gripper was investigated to establish conformity properties for the performance of a robotic gripper system. The Fin Ray Effect® was selected as a possible approach to improve effective gripping and reduce slippage of component handling with regards to pick and place procedures of assembly processes. As a result, the study established the optimization of self-adjusting end-effectors. The gripper system design was simulated and empirically tested. The impact of gripping surface compliance and geometric conformity was investigated. The gripper system design focused on the response of load applied to the conformity mechanism called the Fin Ray Effect®. The appendages were simulated to determine the deflection properties and stress distribution through a finite element analysis. The simulation proved that the configuration of rib structures of the appendages affected the conformity to an applied force representing an object in contact. The system was tested in real time operation and required a control system to produce an active performance of the system. A mass loading test was performed on the gripper system. The repeatability and mass handling range was determined. A dynamic operation was tested on the gripper to determine force versus time properties throughout the grasping movement for a pick and place procedure. The fluctuating forces generated through experimentation was related to the Lagrangian model describing forces experienced by a moving object. The research promoted scientific contribution to the investigation, analysis, and design of intelligent gripping systems that can potentially be implemented in the operational processes of on-demand production lines for reconfigurable assembly systems