59 research outputs found
Iterative Solutions to the Inverse Geometric Problem for Manipulators with no Closed Form Solution
A set of new iterative solutions to the inverse geometric problem is presented. The approach is general and does not depend on intersecting axes or calculation of the Jacobian. The solution can be applied to any manipulator and is well suited for manipulators for which convergence is poor for conventional Jacobian-based iterative algorithms. For kinematically redundant manipulators, weights can be applied to each joint to introduce stiffness and for collision avoidance. The algorithm uses the unit quaternion to represent the position of each joint and calculates analytically the optimal position of the joint when only the respective joint is considered. This sub-problem is computationally very efficient due to the analytical solution. Several algorithms based on the solution of this sub-problem are presented. For difficult problems, for which the initial condition is far from a solution or the geometry of the manipulator makes the solution hard to reach, it is shown that the algorithm finds a solution fairly close to the solution in only a few iterations
General Solutions to Functional and Kinematic Redundancy
A systematic and general approach to represent functional redundancy is presented. It is shown how this approach allows the freedom provided by functional redundancy to be integrated into the inverse geometric problem for real-time applications and how it can be utilised to improve performance. A set of new iterative solutions to the inverse geometric problem, well suited for kinematically redundant manipulators, is also presented
Nonlinear Control Strategies for Outdoor Aerial Manipulators
In this thesis, the design, validation and implementation of nonlinear control strategies for aerial manipulators
{i.e. aerial robots equipped with manipulators{ is studied, with special emphasis on the internal coupling of the
system and its resilience against external disturbances. For the rst, di erent decentralised control strategies
{i.e. using di erent control typologies for each one of the subsystems{ that indirectly take into account this
coupling have been analysed. As a result, a nonlinear strategy composed of two controllers is proposed. A higher
priority is given to the manipulation accuracy, relaxing the platform tracking, and hence obtaining a solution
improving the manipulation capabilities with the surrounding environment. To validate these results, thorough
stability and robustness analyses are provided, both theoretically and in simulation.
On the other hand, a signi cant e ort has been devoted to improving the response and applicability of
robot manipulators used in
ight via control. In particular, the design of controllers for lightweight
exible
manipulators {that reduce the consequences of incidents involving unforeseen contacts{ is analysed. Although
their inherent nature perfectly ts for aerial manipulation applications, the added
exibility produces unwanted
behaviours, such as second-order modes and uncertainties. To cope with them, an adaptable position nonlinear
control strategy is proposed. To validate this contribution, the stability of the approach is studied in theory
and its capabilities are proven in several experimental scenarios. In these, the robustness of the solution against
unforeseen impacts and contact with uncharacterised interfaces is demonstrated.
Subsequently, this strategy has been enriched with {multiaxis{ force control capabilities thanks to the
inclusion of an outer control loop modifying the manipulator reference. Accordingly, this additional applicationfocused
capability is added to the controlled system without loosing the modulated response of the inner-loop
position strategy. It is also worth noting that, thanks to the cascade-like nature of the modi cation, the transition
between position and force control modes is inherently smooth and automatic. The stability of this expanded
strategy has been theoretically analysed and the results validated in a set of experimental scenarios.
To validate the rst nonlinear approach with realistic outdoor simulations before its implementation, a
computational
uid dynamics analysis has been performed to obtain an explicit model of the aerodynamic
forces and torques applied to the blunt-body of the aerial platform in
ight. The results of this study have been
compared to the most common alternative nowadays, being highlighted that the proposed model signi cantly
surpasses this option in terms of accuracy. Moreover, it is worth underscoring that this characterisation could
be also employed in the future to develop control solutions with enhanced rejection capabilities against wind
conditions.
Finally, as the focus of this thesis is on the use of novel control strategies on real aerial manipulation outdoors
to improve their accuracy while performing complex tasks, a modular autopilot solution to be able to implement
them has been also developed. This general-purpose autopilot allows the implementation of new algorithms,
and facilitates their theory-to-experimentation transition. Taking into account this perspective, the proposed
tool employs the simple and widely-known MAS interface and the highly reliable PX4 autopilot as backup, thus
providing a redundant approach to handle unexpected incidents in
ight.En esta tesis se ha estudiado el diseño, validación e implementación de estrategias de control
no lineales para robots manipuladores aéreos –esto es, robots aéreos equipados con un sistema
de manipulación robótica–, dándose especial énfasis a las interacciones internas del sistema y a
su resiliencia frente a efectos externos. Para lo primero, se han analizado diferentes estrategias
de control descentralizado –es decir, que usan tipologías de control diferentes para cada uno de
los subsistemas–, pero que tienen indirectamente en consideración la interacción entre manipulación
y vuelo. Como resultado de esta línea, se propone una estretegia de control conformada
por dos controladores. Estos se coordinan de tal forma que se le da prioridad a la manipulación
sobre el seguimiento de posiciones del vehículo, produciéndose un sistema de control que mejora
la precisión de las interacciones entre el sistema manipulador y el entorno. Para validar estos resultados,
se ha analizado su estabilidad y robustez tanto teóricamente como mediante simulaciones
numéricas.
Por otro lado, se ha buscado mejorar la respuesta y aplicabilidad de los manipuladores que se
usan en vuelo mediante su control. Dentro de esta tendencia, la tesis se ha centrado en el diseño
de controladores para manipuladores ligeros flexibles, ya que estos permiten reducir el peso del
sistema completo y reducen el riesgo de incidentes debidos a contactos inesperados. Sin embargo,
la flexibilidad de estos produce comportamientos indeseados durante la operación, como la aparición
de modos de segundo orden y cierta incentidumbre en su comportamiento. Para reducir su
impacto en la precisión de las tareas de manipulación, se ha desarrollado un controlador no lineal
adaptable. Para validar estos resultados, se ha analizado la estabilidad del sistema teóricamente y se
han desarrollado una serie de experimentos. En ellos, se ha comprobado su robustez ante impactos
inesperados y contactos con elementos no caracterizados.
Posteriormente, esta estrategia para manipuladores flexibles ha sido ampliada al añadir un bucle
externo que posibilita el control en fuerzas en varias direcciones. Esto permite, mediante un único
controlador, mantener la suave respuesta de la estrategia. Además cabe destacar que, al contar esta
estrategia con un diseño en cascade, la transición entre los segmentos de desplazamiento del brazo
y de aplicación de fuerzas es fluida y automática. La estabilidad de esta estrategia ampliada ha sido
analizada teóricamente y los resultados han sido validados experimentalmente.
Para validar la primera estrategia mediante simulaciones que representen fielmente las condiciones
en exteriores antes de su implementación, ha sido necesario realizar un estudio mediante
mecánica de fluidos computacional para obtener un modelo explícito de las fuerzas y momentos
aerodinámicos a los que se efrenta la plataforma en vuelo. Los resultados de este estudio han
sido comparados con la alternativa más empleada actualmente, mostrándose que los avances del
método propuesto son sustanciales. Asimismo, es importante destacar que esta caracterización podría
también usarse en el futuro para desarrollar controladores con una respuesta mejorada ante
perturbaciones aerodinámicas, como en el caso de volar con viento. Finalmente, al ser esta una tesis centrada en las estrategias de control novedosas en sistemas
reales para la mejora de su rendimiento en misiones complejas, se ha desarrollado un autopiloto
modular fácilmente modificable para implementarlas. Este permite validar experimentalmente
nuevos algoritmos y facilita la transición entre teoría y práctica. Para ello, esta herramienta se
basa en una interfaz sencilla ampliamente conocida por los investigadores de robótica, Simulink®,
y cuenta con un autopiloto de respaldo, PX4, para enfrentarse a los incidentes inesperados que
pudieran surgir en vuelo
On Increasing the Automation Level of Heavy-Duty Hydraulic Manipulators with Condition Monitoring of the Hydraulic System and Energy-Optimised Redundancy Resolution
Hydraulic manipulators on mobile machines are predominantly used for excavation and lifting applications at construction sites and for heavy-duty material handling in the forest industry due to their superior power-density and rugged nature. These manipulators are conventionally open-loop controlled by human operators who are sufficiently skilled to operate the machines. However, in the footsteps of pioneering original equipment manufacturers (OEMs) and to keep up with the intensifying demand for innovation, more and more mobile machine OEMs have a major interest in significantly increasing the automation level of their hydraulic manipulators and improving the operation of manipulators. In this thesis, robotic software-based functionalities in the form of modelbased condition monitoring and energy-optimal redundancy resolution which facilitate increased automation level of hydraulic manipulators are proposed.A condition monitoring system generally consists of software modules and sensors which co-operate harmonically and monitor the hydraulic system’s health in real-time based on an indirect measure of this system’s health. The premise is that when this condition monitoring system recognises that the system’s health has deteriorated past a given threshold (in other words, when a minor fault is detected, such as a slowly increasing internal leakage of the hydraulic cylinder), the condition monitoring module issues an alarm to warn the system operator of the malfunction, and the module could ideally diagnose the fault cause. In addition, when faced with severe faults, such as an external leakage or an abruptly increasing internal leakage in the hydraulic system, an alarm from the condition monitoring system ensures that the machine is quickly halted to prevent any further damage to the machine or its surroundings.The basic requirement in the design of such a condition monitoring system is to make sure that this system is robust and fault-sensitive. These properties are difficult to achieve in complex mobile hydraulic systems on hydraulic manipulators due to the modelling uncertainties affecting these systems. The modelling uncertainties affecting mobile hydraulic systems are specific compared with many other types of systems and are large because of the hydraulic system complexities, nonlinearities, discontinuities and inherently time-varying parameters. A feasible solution to this modelling uncertainty problem would be to either attenuate the effect of modelling errors on the performance of model-based condition monitoring or to develop improved non-model-based methods with increased fault-sensitivity. In this research work, the former model-based approach is taken. Adaptation of the model residual thresholds based on system operating points and reliable, load-independent system models are proposed as integral parts of the condition monitoring solution to the modelling uncertainty problem. These proposed solutions make the realisation of condition monitoring solutions more difficult on heavy-duty hydraulic manipulators compared with fixed-load manipulators, for example. These solutions are covered in detail in a subset of the research publications appended to this thesis.There is wide-spread interest from hydraulic manipulator OEMs in increasing the automation level of their hydraulic manipulators. Most often, this interest is related to semi-automation of repetitive work cycles to improve work productivity and operator workload circumstances. This robotic semi-automated approach involves resolving the kinematic redundancy of hydraulic manipulators to obtain motion references for the joint controller to enable desirable closed-loop controlled motions. Because conventional redundancy resolutions are usually sub-optimal at the hydraulic system level, a hydraulic energy-optimised, global redundancy resolution is proposed in this thesis for the first time. Kinematic redundancy is resolved energy optimally from the standpoint of the hydraulic system along a prescribed path for a typical 3-degrees-of-freedom (3-DOF) and 4-DOF hydraulic manipulator. Joint motions are also constrained based on the actuators’ position, velocity and acceleration bounds in hydraulic manipulators in the proposed solution. This kinematic redundancy resolution topic is discussed in the last two research papers. Overall, both designed manipulator features, condition monitoring and energy-optimised redundancy resolution, are believed to be essential for increasing the automation of hydraulic manipulators
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Evolutionary approaches to robot path planning
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The ultimate goal in robotics is to create machines which are more independent and rely less on humans to guide them in their operation. There are many sub-systems which may be present in such a robot, one of which is path planning — the ability to determine a sequence of positions or configurations
between an initial and goal position within a particular obstacle cluttered workspace.
Many classical path planning techniques have been developed, but these tend to have drawbacks such as their computational requirements; the suitability of the plans they produce for a particular application; or how well they are able to generalise to unseen problems. In recent years, evolutionary based problem solving techniques have seen a rise in popularity, possibly coinciding with the improvement in the computational power afforded researches by successful developments in hardware.
These techniques adopt some of the features of natural evolution and mimic them in a computer. The increase in the number of publications in the areas of Genetic Algorithms (GA) and Genetic Programming (GP) demonstrate the success achieved when applying these techniques to ever more problem areas.
This dissertation presents research conducted to determine whether there is a place for Evolutionary Approaches, and specifically GA and GP, in the development of future path planning techniques
Free-flyer path planning in the proximity to large space structures
The development of the modem space stations into large, highly complex orbital structures such as the International Space Station (ISS), has brought about a requirement for free-flying vehicles to perform various inspection and maintenance task on the exterior of the station. Concentrating on the ISS-Inspector vehicle, this thesis investigates the trajectory and mission planning required for a small free-flying vehicle operating in close proximity to the ISS. Two complimentary methods are presented to permit safe manoeuvring around the ISS. Ellipse of Safety trajectories enforce long-term passive safety requirements in the presence of differential air drag during the fly-around phases of the mission, used to transfer between the docking port and observation points. Short-range, close proximity manoeuvring is permitted through the use of Potential Field Guidance methods, enhanced through Velocity Selection strategies to provide passively safe trajectories where possible. Finally, a mission planning tool is presented to permit the integrated planning of ISS-Inspector missions, with automated scheduling and trajectory selection, designed to optimise the use of available manoeuvring methods to maximise overall mission safety. This facilitates the rapid planning and prototyping of Inspector missions from within a single tool, which is available both to operators on the ground and the crew onboard the ISS
The application of neural networks in active suspension
This thesis considers the application of neural networks to automotive suspension
systems. In particular their ability to learn non-linear feedback control
relationships. The speed of processing, once trained, means that neural networks
open up new opportunities and allow increased complexity in the control
strategies employed.
The suitability of neural networks for this task is demonstrated here using multilayer
perceptron, (MLP) feed forward neural networks applied to a quarter vehicle
simulation model. Initially neural networks are trained from a training data set
created using a non-linear optimal control strategy, the complexity of which
prohibits its direct use. They are shown to be successful in learning the
relationship between the current system states and the optimal control. [Continues.
Advances in humanoid control and perception
One day there will be humanoid robots among us doing our boring, time-consuming, or dangerous tasks. They might cook a delicious meal for us or do the groceries. For this to become reality, many advances need to be made to the artificial intelligence of humanoid robots. The ever-increasing available computational processing power opens new doors for such advances. In this thesis we develop novel algorithms for humanoid control and vision that harness this power. We apply these methods on an iCub humanoid upper-body with 41 degrees of freedom. For control, we develop Natural Gradient Inverse Kinematics (NGIK), a sampling-based optimiser that applies natural evolution strategies to perform inverse kinematics. The resulting algorithm makes very few assumptions and gives much more freedom in definable constraints than its Jacobian-based counterparts. A special graph-building procedure is introduced to build Task-Relevant Roadmaps (TRM) by iteratively applying NGIK and storing the results. TRMs form searchable graphs of kinematic configurations on which a wide range of task-relevant humanoid movements can be planned. Through coordinating several instances of NGIK, a fast parallelised version of the TRM building algorithm is developed. To contrast the offline TRM algorithms, we also develop Natural Gradient Control which directly uses the optimisation pass in NGIK as an online control signal. For vision, we develop dynamic vision algorithms that form cyclic information flows that affect their own processing. Deep Attention Selective Networks (dasNet) implement feedback in convolutional neural networks through a gating mechanism that is steered by a policy. Through this feedback, dasNet can focus on different features in the image in light of previously gathered information and improve classification, with state-of-the- art results at the time of publication. Then, we develop PyraMiD-LSTM, which processes 3D volumetric data by employing a novel convolutional Long Short-Term Memory network (C-LSTM) to compute pyramidal contexts for every voxel, and combine them to perform segmentation. This resulted in state-of-the-art performance on a segmentation benchmark. The work on control and vision is integrated into an application on the iCub robot. A Fast-Weight PyraMiD-LSTM is developed that dynamically generates weights for a C-LSTM layer given actions of the robot. An explorative policy using NGC generates a stream of data, which the Fast-Weight PyraMiD-LSTM has to predict. The resulting integrated system learns to model the effects of head and hand movements and their effects on future visual input. To our knowledge, this is the first effective visual prediction system on an iCub
Learning control policies from constrained motion
Many everyday human skills can be framed in terms of performing some task subject
to constraints imposed by the task or the environment. Constraints are usually
unobservable and frequently change between contexts.
In this thesis, we explore the problem of learning control policies from data containing
variable, dynamic and non-linear constraints on motion. We show that an effective
approach for doing this is to learn the unconstrained policy in a way that is
consistent with the constraints.
We propose several novel algorithms for extracting these policies from movement
data, where observations are recorded under different constraints. Furthermore, we
show that, by doing so, we are able to learn representations of movement that generalise
over constraints and can predict behaviour under new constraints.
In our experiments, we test the algorithms on systems of varying size and complexity,
and show that the novel approaches give significant improvements in performance
compared with standard policy learning approaches that are naive to the effect of constraints.
Finally, we illustrate the utility of the approaches for learning from human
motion capture data and transferring behaviour to several robotic platforms
Planning and control of robotic manipulation actions for extreme environments
A large societal and economic need arises for advanced robotic capabilities, where we need to perform complex human-like tasks such as tool-use, in environments that are hazardous for human workers. This thesis addresses a collection of problems, which arise when robotic manipulators must perform complex tasks in cluttered and constrained environments. The work is illustrated by example scenarios of robotic tool use, grasping and manipulating, motivated by the challenges of dismantling operations in the extreme environments of nuclear decommissioning
Contrary to popular assumptions, legacy nuclear facilities (which can date back three-quarters of a century in the UK) can be highly unstructured and uncertain environments, with insufficient a-priori information available for e.g. conventional pre-programming of robot tasks. Meanwhile, situational awareness and direct teleoperation can be extremely difficult for human operators working in a safe zone that is physically remote from the robot. This engenders a need for significant autonomous capabilities. Robots must use vision and sensory systems to perceive their environment, plan and execute complex actions on complex objects in cluttered and constrained environments. Significant radiation, of different types and intensities, provides further challenges in terms of sensor noise. Perception uncertainty can also result from e.g. vision systems observing shiny featureless metal structures. Robotic actions therefore need to be: i) planned in ways that are robust to uncertainties; and ii) controlled in ways which enable the robust reaction to disturbances.
In particular, we investigate motion planning and control in tasks where the robot must: maintain contact while moving over arbitrarily shaped surfaces with end-effector tools; exert forces and withstand perturbations during forceful contact actions; while also avoiding collisions with obstacles; avoiding singularity configurations; and increasing robustness by maximising manipulability during task execution. Furthermore, we consider the issues of robust planning and control with respect to uncertain information, derived from noisy sensors in challenging environments.
We explore the Riemannian geometry and robot's manipulability to yield path planners that produce paths for both fixed-based and floating-based robots, whose tools always stay in contact with the object's surface. Our planners overcome disturbances in the perception and account for robot/environment interactions that may demand unexpected forces. The task execution is entrusted to a hybrid force/motion controller whose motion space behaves with compliance to accommodate unexpected stiffness changes throughout the contact.
We examine the problem of grasping a tool for performing a task. Firstly, we introduce a method for selecting the grasp candidate onto an object yielding collision-free motion for the robot in the post-grasp movements. Furthermore, we study the case of a dual-arm robot performing full-force tasks on an object and slippage on the grasping is allowed. We account for the slippage throughout the task execution using a novel controller based on the sliding mode controllers
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