993 research outputs found
Probabilistic Inference for Model Based Control
Robotic systems are essential for enhancing productivity, automation, and performing hazardous tasks. Addressing the unpredictability of physical systems, this thesis advances robotic planning and control under uncertainty, introducing learning-based methods for managing uncertain parameters and adapting to changing environments in real-time.
Our first contribution is a framework using Bayesian statistics for likelihood-free inference of model parameters. This allows employing complex simulators for designing efficient, robust controllers. The method, integrating the unscented transform with a variant of information theoretical model predictive control, shows better performance in trajectory evaluation compared to Monte Carlo sampling, easing the computational load in various control and robotics tasks.
Next, we reframe robotic planning and control as a Bayesian inference problem, focusing on the posterior distribution of actions and model parameters. An implicit variational inference algorithm, performing Stein Variational Gradient Descent, estimates distributions over model parameters and control inputs in real-time. This Bayesian approach effectively handles complex multi-modal posterior distributions, vital for dynamic and realistic robot navigation.
Finally, we tackle diversity in high-dimensional spaces. Our approach mitigates underestimation of uncertainty in posterior distributions, which leads to locally optimal solutions. Using the theory of rough paths, we develop an algorithm for parallel trajectory optimisation, enhancing solution diversity and avoiding mode collapse. This method extends our variational inference approach for trajectory estimation, employing diversity-enhancing kernels and leveraging path signature representation of trajectories. Empirical tests, ranging from 2-D navigation to robotic manipulators in cluttered environments, affirm our method's efficiency, outperforming existing alternatives
Gaussian Control Barrier Functions : A Gaussian Process based Approach to Safety for Robots
In recent years, the need for safety of autonomous and intelligent robots has increased. Today, as robots are being increasingly deployed in closer proximity to humans, there is an exigency for safety since human lives may be at risk, e.g., self-driving vehicles or surgical robots. The objective of this thesis is to present a safety framework for dynamical systems that leverages tools from control theory and machine learning. More formally, the thesis presents a data-driven framework for designing safety function candidates which ensure properties of forward invariance. The potential benefits of the results presented in this thesis are expected to help applications such as safe exploration, collision avoidance problems, manipulation tasks, and planning, to name some.
We utilize Gaussian processes (GP) to place a prior on the desired safety function candidate, which is to be utilized as a control barrier function (CBF). The resultant formulation is called Gaussian CBFs and they reside in a reproducing kernel Hilbert space. A key concept behind Gaussian CBFs is the incorporation of both safety belief as well as safety uncertainty, which former barrier function formulations did not consider. This is achieved by using robust posterior estimates from a GP where the posterior mean and variance serve as surrogates for the safety belief and uncertainty respectively. We synthesize safe controllers by framing a convex optimization problem where the kernel-based representation of GPs allows computing the derivatives in closed-form analytically.
Finally, in addition to the theoretical and algorithmic frameworks in this thesis, we rigorously test our methods in hardware on a quadrotor platform. The platform used is a Crazyflie 2.1 which is a versatile palm-sized quadrotor. We provide our insights and detailed discussions on the hardware implementations which will be useful for large-scale deployment of the techniques presented in this dissertation.Ph.D
Set-based state estimation and fault diagnosis using constrained zonotopes and applications
This doctoral thesis develops new methods for set-based state estimation and
active fault diagnosis (AFD) of (i) nonlinear discrete-time systems, (ii)
discrete-time nonlinear systems whose trajectories satisfy nonlinear equality
constraints (called invariants), (iii) linear descriptor systems, and (iv)
joint state and parameter estimation of nonlinear descriptor systems. Set-based
estimation aims to compute tight enclosures of the possible system states in
each time step subject to unknown-but-bounded uncertainties. To address this
issue, the present doctoral thesis proposes new methods for efficiently
propagating constrained zonotopes (CZs) through nonlinear mappings. Besides,
this thesis improves the standard prediction-update framework for systems with
invariants using new algorithms for refining CZs based on nonlinear
constraints. In addition, this thesis introduces a new approach for set-based
AFD of a class of nonlinear discrete-time systems. An affine parametrization of
the reachable sets is obtained for the design of an optimal input for set-based
AFD. In addition, this thesis presents new methods based on CZs for set-valued
state estimation and AFD of linear descriptor systems. Linear static
constraints on the state variables can be directly incorporated into CZs.
Moreover, this thesis proposes a new representation for unbounded sets based on
zonotopes, which allows to develop methods for state estimation and AFD also of
unstable linear descriptor systems, without the knowledge of an enclosure of
all the trajectories of the system. This thesis also develops a new method for
set-based joint state and parameter estimation of nonlinear descriptor systems
using CZs in a unified framework. Lastly, this manuscript applies the proposed
set-based state estimation and AFD methods using CZs to unmanned aerial
vehicles, water distribution networks, and a lithium-ion cell.Comment: My PhD Thesis from Federal University of Minas Gerais, Brazil. Most
of the research work has already been published in DOIs
10.1109/CDC.2018.8618678, 10.23919/ECC.2018.8550353,
10.1016/j.automatica.2019.108614, 10.1016/j.ifacol.2020.12.2484,
10.1016/j.ifacol.2021.08.308, 10.1016/j.automatica.2021.109638,
10.1109/TCST.2021.3130534, 10.1016/j.automatica.2022.11042
Autonomisten metsÀkoneiden koneaistijÀrjestelmÀt
A prerequisite for increasing the autonomy of forest machinery is to provide robots with digital situational awareness, including a representation of the surrounding environment and the robot's own state in it. Therefore, this article-based dissertation proposes perception systems for autonomous or semi-autonomous forest machinery as a summary of seven publications. The work consists of several perception methods using machine vision, lidar, inertial sensors, and positioning sensors. The sensors are used together by means of probabilistic sensor fusion. Semi-autonomy is interpreted as a useful intermediary step, situated between current mechanized solutions and full autonomy, to assist the operator.
In this work, the perception of the robot's self is achieved through estimation of its orientation and position in the world, the posture of its crane, and the pose of the attached tool. The view around the forest machine is produced with a rotating lidar, which provides approximately equal-density 3D measurements in all directions. Furthermore, a machine vision camera is used for detecting young trees among other vegetation, and sensor fusion of an actuated lidar and machine vision camera is utilized for detection and classification of tree species. In addition, in an operator-controlled semi-autonomous system, the operator requires a functional view of the data around the robot. To achieve this, the thesis proposes the use of an augmented reality interface, which requires measuring the pose of the operator's head-mounted display in the forest machine cabin. Here, this work adopts a sensor fusion solution for a head-mounted camera and inertial sensors.
In order to increase the level of automation and productivity of forest machines, the work focuses on scientifically novel solutions that are also adaptable for industrial use in forest machinery. Therefore, all the proposed perception methods seek to address a real existing problem within current forest machinery. All the proposed solutions are implemented in a prototype forest machine and field tested in a forest. The proposed methods include posture measurement of a forestry crane, positioning of a freely hanging forestry crane attachment, attitude estimation of an all-terrain vehicle, positioning a head mounted camera in a forest machine cabin, detection of young trees for point cleaning, classification of tree species, and measurement of surrounding tree stems and the ground surface underneath.MetsÀkoneiden autonomia-asteen kasvattaminen edellyttÀÀ, ettÀ robotilla on digitaalinen tilannetieto sekÀ ympÀristöstÀ ettÀ robotin omasta toiminnasta. TÀmÀn saavuttamiseksi työssÀ on kehitetty autonomisen tai puoliautonomisen metsÀkoneen koneaistijÀrjestelmiÀ, jotka hyödyntÀvÀt konenÀkö-, laserkeilaus- ja inertia-antureita sekÀ paikannusantureita. Työ liittÀÀ yhteen seitsemÀssÀ artikkelissa toteutetut havainnointimenetelmÀt, joissa useiden anturien mittauksia yhdistetÀÀn sensorifuusiomenetelmillÀ. TyössÀ puoliautonomialla tarkoitetaan hyödyllisiÀ kuljettajaa avustavia vÀlivaiheita nykyisten mekanisoitujen ratkaisujen ja tÀyden autonomian vÀlillÀ.
TyössÀ esitettÀvissÀ autonomisen metsÀkoneen koneaistijÀrjestelmissÀ koneen omaa toimintaa havainnoidaan estimoimalla koneen asentoa ja sijaintia, nosturin asentoa sekÀ siihen liitetyn työkalun asentoa suhteessa ympÀristöön. YleisnÀkymÀ metsÀkoneen ympÀrille toteutetaan pyörivÀllÀ laserkeilaimella, joka tuottaa lÀhes vakiotiheyksisiÀ 3D-mittauksia jokasuuntaisesti koneen ympÀristöstÀ. Nuoret puut tunnistetaan muun kasvillisuuden joukosta kÀyttÀen konenÀkökameraa. LisÀksi puiden tunnistamisessa ja puulajien luokittelussa kÀytetÀÀn konenÀkökameraa ja laserkeilainta yhdessÀ sensorifuusioratkaisun avulla. LisÀksi kuljettajan ohjaamassa puoliautonomisessa jÀrjestelmÀssÀ kuljettaja tarvitsee toimivan tavan ymmÀrtÀÀ koneen tuottaman mallin ympÀristöstÀ. TyössÀ tÀmÀ ehdotetaan toteutettavaksi lisÀtyn todellisuuden kÀyttöliittymÀn avulla, joka edellyttÀÀ metsÀkoneen ohjaamossa istuvan kuljettajan lisÀtyn todellisuuden lasien paikan ja asennon mittaamista. TyössÀ se toteutetaan kypÀrÀÀn asennetun kameran ja inertia-anturien sensorifuusiona.
Jotta metsÀkoneiden automatisaatiotasoa ja tuottavuutta voidaan lisÀtÀ, työssÀ keskitytÀÀn uusiin tieteellisiin ratkaisuihin, jotka soveltuvat teolliseen kÀyttöön metsÀkoneissa. Kaikki esitetyt koneaistijÀrjestelmÀt pyrkivÀt vastaamaan todelliseen olemassa olevaan tarpeeseen nykyisten metsÀkoneiden kÀytössÀ. Siksi kaikki menetelmÀt on implementoitu prototyyppimetsÀkoneisiin ja tulokset on testattu metsÀympÀristössÀ. TyössÀ esitetyt menetelmÀt mahdollistavat metsÀkoneen nosturin, vapaasti riippuvan työkalun ja ajoneuvon asennon estimoinnin, lisÀtyn todellisuuden lasien asennon mittaamisen metsÀkoneen ohjaamossa, nuorten puiden havaitsemisen reikÀperkauksessa, ympÀröivien puiden puulajien tunnistuksen, sekÀ puun runkojen ja maanpinnan mittauksen
Concurrent design and motion planning in robotics using differentiable optimal control
Robot design optimization (what the robot is) and motion planning (how the robot
moves) are two problems that are connected. Robots are limited by their design in
terms of what motions they can execute â for instance a robot with a heavy base has
less payload capacity compared to the same robot with a lighter base. On the other
hand, the motions that the robot executes guide which design is best for the task.
Concurrent design (co-design) is the process of performing robot design and motion
planning together. Although traditionally co-design has been viewed as an offline
process that can take hours or days, we view interactive co-design tools as the next
step as they enable quick prototyping and evaluation of designs across different tasks
and environments.
In this thesis we adopt a gradient-based approach to co-design. Our baseline
approach embeds the motion planning into bi-level optimization and uses gradient
information via finite differences from the lower motion planning level to optimize
the design in the upper level. Our approach uses the full rigid-body dynamics of the
robot and allows for arbitrary upper-level design constraints, which is key for finding
physically realizable designs. Our approach is also between 1.8 and 8.4 times faster on
a quadruped trotting and jumping co-design task as compared to the popular genetic
algorithm covariance matrix adaptation evolutionary strategy (CMA-ES). We further
demonstrate the speed of our approach by building an interactive co-design tool that
allows for optimization over uneven terrain with varying height.
Furthermore, we propose an algorithm to analytically take the derivative of nonlinear
optimal control problems via differential dynamic programming (DDP). Analytical
derivatives are a step towards addressing the scalability and accuracy issues of finite
differences. We further compared with a simultaneous approach for co-design that
optimizes both motion and design in one nonlinear program. On a co-design task for
the Kinova robotic arm we observed a 54-times improvement in computational speed.
We additionally carry out hardware validation experiments on the quadruped robot
Solo. We designed longer lower legs for the robot, which minimize the peak torque
used during trotting. Although we always observed an improvement in peak torque,
it was less than in simulation (7.609% versus 28.271%). We discuss some of the sim-toreal
issues including the structural stability of joints and slipping of feet that need to
be considered and how they can be addressed using our framework.
In the second part of this thesis we propose solutions to some open problems
in motion planning. Firstly, in our co-design approach we assumed fixed contact
locations and timings. Ideally we would like the motion planner to choose the contacts
instead. We solve a related, but simpler problem, which is the control of satellite
thrusters, which are similar to robot feet but do not have the constraint of having to be
in contact with the ground to exert force on the robot. We introduce a sparse, L1 cost
on control inputs (thrusters) and implement optimization via DDP-style solvers. We
use full rigid-body dynamics and achieve bang-bang control via optimization, which
is a difficult problem due to the discrete switching nature of the thrusters.
Lastly, we present a method for planning and control of a hybrid, wheel-legged
robot. This is a difficult problem, as the robot needs to always actively balance on
the wheel even when not driving or jumping forward. We propose the variablelength
wheeled inverted pendulum (VL-WIP) template model that captures only the
necessary dynamic interactions between wheels and base. We embedded this into
a model-predictive controller (MPC) and demonstrated highly dynamic behaviors,
including swinging-up and jumping over a gap.
Both of these motion planning problems expand the ability of our motion planning
tools to new domains, which is an integral part also of the co-design algorithms, as
co-design aims to optimize both design, and motion, together
Design and Implementation of an IMU Sensor System to Estimate a Hockey Puckâs Peak Velocity
The rapid advancement in sensor technology can revolutionize how sports dynamics are understood and analyzed. This thesis focuses on designing and implementing an Inertial Measurement Unit (IMU) sensor system to be deployed within a hockey puck to estimate its peak velocity.
The research involved the intricate design of a sensor system comprising an accelerometer, two gyroscopes, and a magnetometer. Moreover, puck preparation was carried out to secure the sensor and battery within the puck to ensure functionality and durability. Furthermore, a data acquisition system is developed to receive, save, and plot data transmitted via Bluetooth Low Energy (BLE) protocol.
Three distinct methods for estimating the puck's peak velocity from the sensor data are compared. It is discovered that the method based on an extended Kalman filter and utilizing data from all three sensor types exhibits superior accuracy. This method is subsequently validated under various hockey shot conditions, reinforcing its practical applicability. Moreover, the relationship between velocity estimation error versus true velocity is investigated.
Primarily designed for research studies, this work offers a foundational understanding of hockey puck dynamics, despite the sensor system not being tailored for real-game scenarios. The insights gained have substantial implications for further sports analytics and player training. Furthermore, the results outline a promising pathway for future sports engineering and wearable technology investigations
Expectations and expertise in artificial intelligence: specialist views and historical perspectives on conceptualisation, promise, and funding
Artificial intelligenceâs (AI) distinctiveness as a technoscientific field that imitates the ability to think went through a resurgence of interest post-2010, attracting a flood of scientific and popular expectations as to its utopian or dystopian transformative consequences. This thesis offers observations about the formation and dynamics of expectations based on documentary material from the previous periods of perceived AI hype (1960-1975 and 1980-1990, including in-between periods of perceived dormancy), and 25 interviews with UK-based AI specialists, directly involved with its development, who commented on the issues during the crucial period of uncertainty (2017-2019) and intense negotiation through which AI gained momentum prior to its regulation and relatively stabilised new rounds of long-term investment (2020-2021). This examination applies and contributes to longitudinal studies in the sociology of expectations (SoE) and studies of experience and expertise (SEE) frameworks, proposing a historical sociology of expertise and expectations framework. The research questions, focusing on the interplay between hype mobilisation and governance, are: (1) What is the relationship between AI practical development and the broader expectational environment, in terms of funding and conceptualisation of AI? (2) To what extent does informal and non-developer assessment of expectations influence formal articulations of foresight? (3) What can historical examinations of AIâs conceptual and promissory settings tell about the current rebranding of AI?
The following contributions are made: (1) I extend SEE by paying greater attention to the interplay between technoscientific experts and wider collective arenas of discourse amongst non-specialists and showing how AIâs contemporary research cultures are overwhelmingly influenced by the hype environment but also contribute to it. This further highlights the interaction between competing rationales focusing on exploratory, curiosity-driven scientific research against exploitation-oriented strategies at formal and informal levels. (2) I suggest benefits of examining promissory environments in AI and related technoscientific fields longitudinally, treating contemporary expectations as historical products of sociotechnical trajectories through an authoritative historical reading of AIâs shifting conceptualisation and attached expectations as a response to availability of funding and broader national imaginaries. This comes with the benefit of better perceiving technological hype as migrating from social group to social group instead of fading through reductionist cycles of disillusionment; either by rebranding of technical operations, or by the investigation of a given field by non-technical practitioners. It also sensitises to critically examine broader social expectations as factors for shifts in perception about theoretical/basic science research transforming into applied technological fields. Finally, (3) I offer a model for understanding the significance of interplay between conceptualisations, promising, and motivations across groups within competing dynamics of collective and individual expectations and diverse sources of expertise
Study and Development of Mechatronic Devices and Machine Learning Schemes for Industrial Applications
Obiettivo del presente progetto di dottorato Ăš lo studio e sviluppo di sistemi meccatronici e di modelli machine learning per macchine operatrici e celle robotizzate al fine di incrementarne le prestazioni operative e gestionali. Le pressanti esigenze del mercato hanno imposto lavorazioni con livelli di accuratezza sempre piĂč elevati, tempi di risposta e di produzione ridotti e a costi contenuti. In questo contesto nasce il progetto di dottorato, focalizzato su applicazioni di lavorazioni meccaniche (e.g. fresatura), che includono sistemi complessi quali, ad esempio, macchine a 5 assi e, tipicamente, robot industriali, il cui utilizzo varia a seconda dellâimpiego. Oltre alle specifiche problematiche delle lavorazioni, si deve anche considerare lâinterazione macchina-robot per permettere unâefficiente capacitĂ e gestione dellâintero impianto. La complessitĂ di questo scenario puĂČ evidenziare sia specifiche problematiche inerenti alle lavorazioni (e.g. vibrazioni) sia inefficienze piĂč generali che riguardano lâimpianto produttivo (e.g. asservimento delle macchine con robot, consumo energetico). Vista la vastitĂ della tematica, il progetto si Ăš suddiviso in due parti, lo studio e sviluppo di due specifici dispositivi meccatronici, basati sullâimpiego di attuatori piezoelettrici, che puntano principalmente alla compensazione di vibrazioni indotte dal processo di lavorazione, e lâintegrazione di robot per lâasservimento di macchine utensili in celle robotizzate, impiegando modelli di machine learning per definire le traiettorie ed i punti di raggiungibilitĂ del robot, al fine di migliorarne lâaccuratezza del posizionamento del pezzo in diverse condizioni. In conclusione, la presente tesi vuole proporre soluzioni meccatroniche e di machine learning per incrementare le prestazioni di macchine e sistemi robotizzati convenzionali. I sistemi studiati possono essere integrati in celle robotizzate, focalizzandosi sia su problematiche specifiche delle lavorazioni in macchine operatrici sia su problematiche a livello di impianto robot-macchina. Le ricerche hanno riguardato unâapprofondita valutazione dello stato dellâarte, la definizione dei modelli teorici, la progettazione funzionale e lâidentificazione delle criticitĂ del design dei prototipi, la realizzazione delle simulazioni e delle prove sperimentali e lâanalisi dei risultati.The aim of this Ph.D. project is the study and development of mechatronic systems and machine learning models for machine tools and robotic applications to improve their performances. The industrial demands have imposed an ever-increasing accuracy and efficiency requirement whilst constraining the cost. In this context, this project focuses on machining processes (e.g. milling) that include complex systems such as 5-axes machine tool and industrial robots, employed for various applications. Beside the issues related to the machining process itself, the interaction between the machining centre and the robot must be considered for the complete industrial plantâs improvement. This scenarioÂŽs complexity depicts both specific machining problematics (e.g. vibrations) and more general issues related to the complete plant, such as machine tending with an industrial robot and energy consumption. Regarding the immensity of this area, this project is divided in two parts, the study and development of two mechatronic devices, based on piezoelectric stack actuators, for the active vibration control during the machining process, and the robot machine tending within the robotic cell, employing machine learning schemes for the trajectory definition and robot reachability to improve the corresponding positioning accuracy. In conclusion, this thesis aims to provide a set of solutions, based on mechatronic devices and machine learning schemes, to improve the conventional machining centre and the robotic systems performances. The studied systems can be integrated within a robotic cell, focusing on issues related to the specific machining process and to the interaction between robot-machining centre. This research required a thorough study of the state-of-the-art, the formulation of theoretical models, the functional design development, the identification of the critical aspects in the prototype designs, the simulation and experimental campaigns, and the analysis of the obtained results
Automatic Control and Routing of Marine Vessels
Due to the intensive development of the global economy, many problems are constantly emerging connected to the safety of shipsâ motion in the context of increasing marine traffic. These problems seem to be especially significant for the further development of marine transportation services, with the need to considerably increase their efficiency and reliability. One of the most commonly used approaches to ensuring safety and efficiency is the wide implementation of various automated systems for guidance and control, including such popular systems as marine autopilots, dynamic positioning systems, speed control systems, automatic routing installations, etc. This Special Issue focuses on various problems related to the analysis, design, modelling, and operation of the aforementioned systems. It covers such actual problems as tracking control, path following control, ship weather routing, course keeping control, control of autonomous underwater vehicles, ship collision avoidance. These problems are investigated using methods such as neural networks, sliding mode control, genetic algorithms, L2-gain approach, optimal damping concept, fuzzy logic and others. This Special Issue is intended to present and discuss significant contemporary problems in the areas of automatic control and the routing of marine vessels
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