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Bazzi, Louay Mohamad Jamil 1974. "Robust algorithms for model-based object recognition and localization." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/9440.
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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.Includes bibliographical references (p. 86-87).
We consider the problem of model-based object recognition and localization in the presence of noise, spurious features, and occlusion. We address the case where the model is allowed to be transformed by elements in a given space of allowable transformations. Known algorithms for the problem either treat noise very accurately in an unacceptable worst case running time, or may have unreliable output when noise is allowed. We introduce the idea of tolerance which measures the robustness of a recognition and localization method when noise is allowed. We present a collection of algorithms for the problem, each achieving a different degree of tolerance. The main result is a localization algorithm that achieves any desired tolerance in a relatively low order worst case asymptotic running time. The time constant of the algorithm depends on the ratio of the noise bound over the given tolerance bound. The solution we provide is general enough to handle different cases of allowable transformations, such as planar affine transformations, and scaled rigid motions in arbitrary dimensions.
by Louay Mohamad Jamil Bazzi.
S.M.
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Bax, Ingo. "Hierarchical feed forward models for robust object recognition." [S.l.] : [s.n.], 2007. http://deposit.ddb.de/cgi-bin/dokserv?idn=984822666.
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Schaich, Rainer Manuel. "Robust model predictive control." Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:94e75a62-a801-47e1-8cb8-668e8309d477.
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This thesis deals with the topic of min-max formulations of robust model predictive control problems. The sets involved in guaranteeing robust feasibility of the min-max program in the presence of state constraints are of particular interest, and expanding the applicability of well understood solvers of linearly constrained quadratic min-max programs is the main focus. To this end, a generalisation for the set of uncertainty is considered: instead of fixed bounds on the uncertainty, state- and input-dependent bounds are used. To deal with state- and input dependent constraint sets a framework for a particular class of set-valued maps is utilised, namely parametrically convex set-valued maps. Relevant properties and operations are developed to accommodate parametrically convex set-valued maps in the context of robust model predictive control. A quintessential part of this work is the study of fundamental properties of piecewise polyhedral set-valued maps which are parametrically convex, we show that one particular property is that their combinatorial structure is constant. The study of polytopic maps with a rigid combinatorial structure allows the use of an optimisation based approach of robustifying constrained control problems with probabilistic constraints. Auxiliary polytopic constraint sets, used to replace probabilistic constraints by deterministic ones, can be optimised to minimise the conservatism introduced while guaranteeing constraint satisfaction of the original probabilistic constraint. We furthermore study the behaviour of the maximal robust positive invariant set for the case of scaled uncertainty and show that this set is continuously polytopic up to a critical scaling factor, which we can approximate a-priori with an arbitrary degree of accuracy. Relevant theoretical statements are developed, discussed and illustrated with examples.
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Cheng, Qifeng. "Robust & stochastic model predictive control." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:89da4934-9de7-4142-958e-513065189518.
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In the thesis, two different model predictive control (MPC) strategies are investigated for linear systems with uncertainty in the presence of constraints: namely robust MPC and stochastic MPC. Firstly, a Youla Parameter is integrated into an efficient robust MPC algorithm. It is demonstrated that even in the constrained cases, the use of the Youla Parameter can desensitize the costs to the effect of uncertainty while not affecting the nominal performance, and hence it strengthens the robustness of the MPC strategy. Since the controller u = K x + c can offer many advantages and is used across the thesis, the work provides two solutions to the problem when the unconstrained nominal LQ-optimal feedback K cannot stabilise the whole class of system models. The work develops two stochastic tube approaches to account for probabilistic constraints. By using a semi closed-loop paradigm, the nominal and the error dynamics are analyzed separately, and this makes it possible to compute the tube scalings offline. First, ellipsoidal tubes are considered. The evolution for the tube scalings is simplified to be affine and using Markov Chain model, the probabilistic tube scalings can be calculated to tighten the constraints on the nominal. The online algorithm can be formulated into a quadratic programming (QP) problem and the MPC strategy is closed-loop stable. Following that, a direct way to compute the tube scalings is studied. It makes use of the information on the distribution of the uncertainty explicitly. The tubes do not take a particular shape but are defined implicitly by tightened constraints. This stochastic MPC strategy leads to a non-conservative performance in the sense that the probability of constraint violation can be as large as is allowed. It also ensures the recursive feasibility and closed-loop stability, and is extended to the output feedback case.
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Gastebois, Jérémy. "Contribution à la commande temps réel des robots marcheurs. Application aux stratégies d'évitement des chutes." Thesis, Poitiers, 2017. http://www.theses.fr/2017POIT2315/document.
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Les grands robots marcheurs sont des systèmes mécatroniques poly-articulés complexes qui cristallisent la volonté des humains de conférer leurs capacités à des artefacts, l’une d’entre elle étant la locomotion bipède, et plus particulièrement la conservation de l’équilibre face à des perturbations extérieures. Cette thèse propose un stabilisateur postural ainsi que sa mise en œuvre sur le système locomoteur BIP 2000.Ce robot anthropomorphique possède quinze degrés de libertés actionnés par moteurs électriques et a reçu un nouvel automate ainsi que des variateurs industriels lors de la mise à jour réalisée dans le cadre de ces travaux. Un contrôleur a été conçu et implémenté en suivant les principes de la programmation orientée objet afin de fournir une modularité qui s’inspire de la symétrie naturelle des humanoïdes. Cet aspect a conduit à l’élaboration d’un ensemble d’outils mathématiques permettant de calculer l’ensemble des modèles d’un robot composé de sous-robots dont on connaîtrait déjà les modèles. Le contrôleur permet notamment à la machine de suivre des trajectoires calculées hors ligne par des algorithmes de génération de marches dynamiques ainsi que de tester le stabilisateur postural.Ce dernier consiste en un contrôle en position du robot physique par la consigne d’un robot virtuel de modèle dégradé, commandé en effort, soumis à des champs électrostatiques contraignant sa configuration articulaire. Les tests effectués ont permis de montrer la faisabilité de la méthodeBig walking robots are complex multi-joints mechanical systems which crystallize the human will to confer their capabilities on artefacts, one of them being the bipedal locomotion and more especially the balance keeping against external disturbances. This thesis proposes a balance stabilizer under operating conditions displayed on the locomotor system BIP 2000.This anthropomorphic robot has got fifteen electrically actuated degree of freedom and an Industrial controller. A new software has been developed with an object-oriented programming approach in order to propose the modularity required by the emulated and natural human symmetry. This consideration leads to the development of a mathematical tool allowing the computation of every modelling of a serial robot which is the sum of multiple sub robots with already known modelling. The implemented software also enables the robot to run offline generated dynamic walking trajectories and to test the balance stabilizer.We explore in this thesis the feasibility of controlling the center of gravity of a multibody robotic system with electrostatic fields acting on its virtual counterpart in order to guarantee its balance. Experimental results confirm the potential of the proposed approach
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Reynaga, Barba Valeria. "Detecting Changes During the Manipulation of an Object Jointly Held by Humans and RobotsDetektera skillnader under manipulationen av ett objekt som gemensamt hålls av människor och robotar." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-174027.
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In the last decades research and development in the field of robotics has grown rapidly. This growth has resulted in the emergence of service robots that need to be able to physically interact with humans for different applications. One of these applications involves robots and humans cooperating in handling an object together. In such cases, there is usually an initial arrangement of how the robot and the humans hold the object and the arrangement stays the same throughout the manipulation task. Real-world scenarios often require that the initial arrangement changes throughout the task, therefore, it is important that the robot is able to recognize these changes and act accordingly. We consider a setting where a robot holds a large flat object with one or two humans. The aim of this research project is to detect the change in the number of agents grasping the object using only force and torque information measured at the robot's wrist. The proposed solution involves defining a transition sequence of four steps that the humans should perform to go from the initial scenario to the final one. The force and torque information is used to estimate the grasping point of the agents with a Kalman filter. While the humans are going from one scenario to the other, the estimated point changes according to the step of the transition the humans are in. These changes are used to track the steps in the sequence using a hidden Markov model (HMM). Tracking the steps in the sequence means knowing how many agents are grasping the object. To evaluate the method, humans that were not involved in the training of the HMM were asked to perform two tasks: a) perform the previously defined sequence as is, and b) perform a deviation of the sequence. The results of the method show that it is possible to detect the change between one human and two humans holding the object using only force and torque information.
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Munoz, Carpintero Diego Alejandro. "Strategies in robust and stochastic model predictive control." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:2f6bce71-f91f-4d5a-998f-295eff5b089a.
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The presence of uncertainty in model predictive control (MPC) has been accounted for using two types of approaches: robust MPC (RMPC) and stochastic MPC (SMPC). Ideal RMPC and SMPC formulations consider closed-loop optimal control problems whose exact solution, via dynamic programming, is intractable for most systems. Much effort then has been devoted to find good compromises between the degree of optimality and computational tractability. This thesis expands on this effort and presents robust and stochastic MPC strategies with reduced online computational requirements where the conservativeness incurred is made as small as conveniently possible. Two RMPC strategies are proposed for linear systems under additive uncertainty. They are based on a recently proposed approach which uses a triangular prediction structure and a non-linear control policy. One strategy considers a transference of part of the computation of the control policy to an offline stage. The other strategy considers a modification of the prediction structure so that it has a striped structure and the disturbance compensation extends throughout an infinite horizon. An RMPC strategy for linear systems with additive and multiplicative uncertainty is also presented. It considers polytopic dynamics that are designed so as to maximize the volume of an invariant ellipsoid, and are used in a dual-mode prediction scheme where constraint satisfaction is ensured by an approach based on a variation of Farkas' Lemma. Finally, two SMPC strategies for linear systems with additive uncertainty are presented, which use an affine-in-the-disturbances control policy with a striped structure. One strategy considers an offline sequential design of the gains of the control policy, while these are variables in the online optimization in the other. Control theoretic properties, such as recursive feasibility and stability, are studied for all the proposed strategies. Numerical comparisons show that the proposed algorithms can provide a convenient compromise in terms of computational demands and control authority.
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Spoida, Peter. "Robust pricing and hedging beyond one marginal." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:0315824b-52f7-4e44-9ac6-0a688c49762c.
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The robust pricing and hedging approach in Mathematical Finance, pioneered by Hobson (1998), makes statements about non-traded derivative contracts by imposing very little assumptions about the underlying financial model but directly using information contained in traded options, typically call or put option prices. These prices are informative about marginal distributions of the asset. Mathematically, the theory of Skorokhod embeddings provides one possibility to approach robust problems. In this thesis we consider mostly robust pricing and hedging problems of Lookback options (options written on the terminal maximum of an asset) and Convex Vanilla Options (options written on the terminal value of an asset) and extend the analysis which is predominately found in the literature on robust problems by two features: Firstly, options with multiple maturities are available for trading (mathematically this corresponds to multiple marginal constraints) and secondly, restrictions on the total realized variance of asset trajectories are imposed. Probabilistically, in both cases, we develop new optimal solutions to the Skorokhod embedding problem. More precisely, in Part I we start by constructing an iterated Azema-Yor type embedding (a solution to the n-marginal Skorokhod embedding problem, see Chapter 2). Subsequently, its implications are presented in Chapter 3. From a Mathematical Finance perspective we obtain explicitly the optimal superhedging strategy for Barrier/Lookback options. From a probability theory perspective, we find the maximum maximum of a martingale which is constrained by finitely many intermediate marginal laws. Further, as a by-product, we discover a new class of martingale inequalities for the terminal maximum of a cadlag submartingale, see Chapter 4. These inequalities enable us to re-derive the sharp versions of Doob's inequalities. In Chapter 5 a different problem is solved. Motivated by the fact that in some markets both Vanilla and Barrier options with multiple maturities are traded, we characterize the set of market models in this case. In Part II we incorporate the restriction that the total realized variance of every asset trajectory is bounded by a constant. This has been previously suggested by Mykland (2000). We further assume that finitely many put options with one fixed maturity are traded. After introducing the general framework in Chapter 6, we analyse the associated robust pricing and hedging problem for convex Vanilla and Lookback options in Chapters 7 and 8. Robust pricing is achieved through construction of appropriate Root solutions to the Skorokhod embedding problem. Robust hedging and pathwise duality are obtained by a careful development of dynamic pathwise superhedging strategies. Further, we characterize existence of market models with a suitable notion of arbitrage.
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Lee, Sharen Woon Yee. "Bayesian methods for the construction of robust chronologies." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:49c30401-9442-441f-b6b5-1539817e2c95.
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Bayesian modelling is a widely used, powerful approach for reducing absolute dating uncertainties in archaeological research. It is important that the methods used in chronology building are robust and reflect substantial prior knowledge. This thesis focuses on the development and evaluation of two novel, prior models: the trapezoidal phase model; and the Poisson process deposition model. Firstly, the limitations of the trapezoidal phase model were investigated by testing the model assumptions using simulations. It was found that a simple trapezoidal phase model does not reflect substantial prior knowledge and the addition of a non-informative element to the prior was proposed. An alternative parameterisation was also presented, to extend its use to a contiguous phase scenario. This method transforms the commonly-used abrupt transition model to allow for gradual changes. The second phase of this research evaluates the use of Bayesian model averaging in the Poisson process deposition model. The use of model averaging extends the application of the Poisson process model to remove the subjectivity involved in model selection. The last part of this thesis applies these models to different case studies, including attempts at resolving the Iron Age chronological debate in Israel, at determining the age of an important Quaternary tephra, at refining a cave chronology, and at more accurately modelling the mid-Holocene elm decline in the British Isles. The Bayesian methods discussed in this thesis are widely applicable in modelling situations where the associated prior assumptions are appropriate. Therefore, they are not limited to the case studies addressed in this thesis, nor are they limited to analysing radiocarbon chronologies.
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Fleming, James. "Robust and stochastic MPC of uncertain-parameter systems." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:c19ff07c-0756-45f6-977b-9d54a5214310.
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Constraint handling is difficult in model predictive control (MPC) of linear differential inclusions (LDIs) and linear parameter varying (LPV) systems. The designer is faced with a choice of using conservative bounds that may give poor performance, or accurate ones that require heavy online computation. This thesis presents a framework to achieve a more flexible trade-off between these two extremes by using a state tube, a sequence of parametrised polyhedra that is guaranteed to contain the future state. To define controllers using a tube, one must ensure that the polyhedra are a sub-set of the region defined by constraints. Necessary and sufficient conditions for these subset relations follow from duality theory, and it is possible to apply these conditions to constrain predicted system states and inputs with only a little conservatism. This leads to a general method of MPC design for uncertain-parameter systems. The resulting controllers have strong theoretical properties, can be implemented using standard algorithms and outperform existing techniques. Crucially, the online optimisation used in the controller is a convex problem with a number of constraints and variables that increases only linearly with the length of the prediction horizon. This holds true for both LDI and LPV systems. For the latter it is possible to optimise over a class of gain-scheduled control policies to improve performance, with a similar linear increase in problem size. The framework extends to stochastic LDIs with chance constraints, for which there are efficient suboptimal methods using online sampling. Sample approximations of chance constraint-admissible sets are generally not positively invariant, which motivates the novel concept of âsample-admissible' sets with this property to ensure recursive feasibility when using sampling methods. The thesis concludes by introducing a simple, convex alternative to chance-constrained MPC that applies a robust bound to the time average of constraint violations in closed-loop.
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Buerger, Johannes Albert. "Fast model predictive control." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:6e296415-f02c-4bc2-b171-3bee80fc081a.
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This thesis develops efficient optimization methods for Model Predictive Control (MPC) to enable its application to constrained systems with fast and uncertain dynamics. The key contribution is an active set method which exploits the parametric nature of the sequential optimization problem and is obtained from a dynamic programming formulation of the MPC problem. This method is first applied to the nominal linear MPC problem and is successively extended to linear systems with additive uncertainty and input constraints or state/input constraints. The thesis discusses both offline (projection-based) and online (active set) methods for the solution of controllability problems for linear systems with additive uncertainty. The active set method uses first-order necessary conditions for optimality to construct parametric programming regions for a particular given active set locally along a line of search in the space of feasible initial conditions. Along this line of search the homotopy of optimal solutions is exploited: a known solution at some given plant state is continuously deformed into the solution at the actual measured current plant state by performing the required active set changes whenever a boundary of a parametric programming region is crossed during the line search operation. The sequence of solutions for the finite horizon optimal control problem is therefore obtained locally for the given plant state. This method overcomes the main limitation of parametric programming methods that have been applied in the MPC context which usually require the offline precomputation of all possible regions. In contrast to this the proposed approach is an online method with very low computational demands which efficiently exploits the parametric nature of the solution and returns exact local DP solutions. The final chapter of this thesis discusses an application of robust tube-based MPC to the nonlinear MPC problem based on successive linearization.
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Evans, Martin A. "Multiplicative robust and stochastic MPC with application to wind turbine control." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:0ad9b878-00f3-4cfa-a683-148765e3ae39.
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A robust model predictive control algorithm is presented that explicitly handles multiplicative, or parametric, uncertainty in linear discrete models over a finite horizon. The uncertainty in the predicted future states and inputs is bounded by polytopes. The computational cost of running the controller is reduced by calculating matrices offline that provide a means to construct outer approximations to robust constraints to be applied online. The robust algorithm is extended to problems of uncertain models with an allowed probability of violation of constraints. The probabilistic degrees of satisfaction are approximated by one-step ahead sampling, with a greedy solution to the resulting mixed integer problem. An algorithm is given to enlarge a robustly invariant terminal set to exploit the probabilistic constraints. Exponential basis functions are used to create a Robust MPC algorithm for which the predictions are defined over the infinite horizon. The control degrees of freedom are weights that define the bounds on the state and input uncertainty when multiplied by the basis functions. The controller handles multiplicative and additive uncertainty. Robust MPC is applied to the problem of wind turbine control. Rotor speed and tower oscillations are controlled by a low sample rate robust predictive controller. The prediction model has multiplicative and additive uncertainty due to the uncertainty in short-term future wind speeds and in model linearisation. Robust MPC is compared to nominal MPC by means of a high-fidelity numerical simulation of a wind turbine under the two controllers in a wide range of simulated wind conditions.
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Sanchez, Loza Jose Manuel. "Shape sensing of deformable objects for robot manipulation." Thesis, Université Clermont Auvergne (2017-2020), 2019. http://www.theses.fr/2019CLFAC012/document.
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Les objets déformables sont omniprésents dans notre vie quotidienne. Chaque jour, nous manipulons des vêtements dans des configurations innombrables pour nous habiller, nouons les lacets de nos chaussures, cueillons des fruits et des légumes sans les endommager pour notre consommation et plions les reçus dans nos portefeuilles. Toutes ces tâches impliquent de manipuler des objets déformables et peuvent être exécutées sans problème par une personne. Toutefois, les robots n'ont pas encore atteint le même niveau de dextérité. Contrairement aux objets rigides, que les robots sont maintenant capables de manipuler avec des performances proches de celles des humains; les objets déformables doivent être contrôlés non seulement pour les positionner, mais aussi pour définir leur forme. Cette contrainte supplémentaire, relative au contrôle de la forme d’un objet, rend les techniques utilisées pour les objets rigides inapplicables aux objets déformables. En outre, le comportement des objets déformables diffère largement entre eux, par exemple: la forme d’un câble et des vêtements est considérablement affectée par la gravité, alors que celle-ci n’affecte pas la configuration d’autres objets déformables tels que des produits alimentaires. Ainsi, différentes approches ont été proposées pour des classes spécifiques d’objets déformables.Dans cette thèse, nous cherchons à remédier à ces lacunes en proposant une approche modulaire pour détecter la forme d'un objet pendant qu'il est manipulé par un robot. La modularité de cette approche s’inspire d’un paradigme de programmation qui s’applique de plus en plus au développement de logiciels en robotique et vise à apporter des solutions plus générales en séparant les fonctionnalités en composants. Ces composants peuvent ensuite être interchangés en fonction de la tâche ou de l'objet concerné. Cette stratégie est un moyen modulaire de suivre la forme d'objets déformables.Pour valider la stratégie proposée, nous avons implémenté trois applications différentes. Deux applications portaient exclusivement sur l'estimation de la déformation de l'objet à l'aide de données tactiles ou de données issues d’un capteur d’effort. La troisième application consistait à contrôler la déformation d'un objet. Une évaluation de la stratégie proposée, réalisée sur un ensemble d'objets élastiques pour les trois applications, montre des résultats prometteurs pour une approche qui n'utilise pas d'informations visuelles et qui pourrait donc être améliorée de manière significative par l'ajout de cette modalitéDeformable objects are ubiquitous in our daily lives. On a given day, we manipulate clothes into uncountable configurations to dress ourselves, tie the shoelaces on our shoes, pick up fruits and vegetables without damaging them for our consumption and fold receipts into our wallets. All these tasks involve manipulating deformable objects and can be performed by an able person without any trouble, however robots have yet to reach the same level of dexterity. Unlike rigid objects, where robots are now capable of handling objects with close to human performance in some tasks; deformable objects must be controlled not only to account for their pose but also their shape. This extra constraint, to control an object's shape, renders techniques used for rigid objects mainly inapplicable to deformable objects. Furthermore, the behavior of deformable objects widely differs among them, e.g. the shape of a cable and clothes are significantly affected by gravity while it might not affect the configuration of other deformable objects such as food products. Thus, different approaches have been designed for specific classes of deformable objects.In this thesis we seek to address these shortcomings by proposing a modular approach to sense the shape of an object while it is manipulated by a robot. The modularity of the approach is inspired by a programming paradigm that has been increasingly been applied to software development in robotics and aims to achieve more general solutions by separating functionalities into components. These components can then be interchanged based on the specific task or object at hand. This provides a modular way to sense the shape of deformable objects.To validate the proposed pipeline, we implemented three different applications. Two applications focused exclusively on estimating the object's deformation using either tactile or force data, and the third application consisted in controlling the deformation of an object. An evaluation of the pipeline, performed on a set of elastic objects for all three applications, shows promising results for an approach that makes no use of visual information and hence, it could greatly be improved by the addition of this modality
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Antonello, Morris. "Semantic models of scenes and objects for service and industrial robotics." Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3422425.
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What may seem straightforward for the human perception system is still challenging for robots. Automatically segmenting the elements with highest relevance or salience, i.e. the semantics, is non-trivial given the high level of variability in the world and the limits of vision sensors. This stands up when multiple ambiguous sources of information are available, which is the case when dealing with moving robots. This thesis leverages on the availability of contextual cues and multiple points of view to make the segmentation task easier. Four robotic applications will be presented, two designed for service robotics and two for an industrial context. Semantic models of indoor environments will be built enriching geometric reconstructions with semantic information about objects, structural elements and humans. Our approach leverages on the importance of context, the availability of multiple source of information, as well as multiple view points showing with extensive experiments on several datasets that these are all crucial elements to boost state-of-the-art performances.
Furthermore, moving to applications with robots analyzing object surfaces instead of their surroundings, semantic models of Carbon Fiber Reinforced Polymers will be built augmenting geometric models with accurate measurements of superficial fiber orientations, and inner defects invisible to the human-eye. We succeeded in reaching an industrial grade accuracy making these models useful for autonomous quality inspection and process optimization. In all applications, special attention will be paid towards fast methods suitable for real robots like the two prototypes presented in this thesis.Il sistema percettivo umano si presta alla risoluzione di compiti che possono sembrare banali, ma che al contrario si rivelano essere delle sfide per i robot. La segmentazione automatica degli elementi di maggiore rilevanza o salienza, vale a dire la semantica, ne è un esempio in quanto è soggetta ai limiti dei sensori di visione e all’elevato grado di variabilità del mondo. In particolar modo ne abbiamo esperienza quando sono presenti più fonti di informazione, spesso ambigue, come nel caso di un robot in movimento. Questa tesi dimostra come si possa sfruttare la disponibilità di indizi contestuali e punti di vista diversi per rendere più facile l’attività di segmentazione. A dimostrazione verranno presentate quattro applicazioni robotiche, due progettate per la robotica di servizio e due per un contesto industriale. Verranno costruiti modelli semantici di scene domestiche arricchendo le ricostruzioni geometriche con delle informazioni semantiche che comprendono oggetti, elementi strutturali ed esseri umani. Il nostro approccio sfrutta il contesto, la molteplicità di fonti di informazioni e dei punti di vista, servendosi di esperimenti esaustivi condotti su diversi dataset per dimostrare come questi siano elementi cruciali per aumentare le prestazioni del robot. Inoltre, considerando scenari con robot che analizzano oggetti anziché esplorare l’ambiente, verranno costruiti modelli semantici di polimeri rinforzati in fibra di carbonio arricchendo i modelli geometrici con le misurazioni precise sull’orientazione delle fibre e i difetti interni non visibili all’occhio umano. Siamo riusciti a raggiungere una precisione di livello industriale rendendo questi modelli utili per il controllo qualità automatico e l’ottimizzazione dei processi. In tutte le applicazioni, un’attenzione particolare sarà dedicata ai metodi più veloci, adatti a robot reali come i due prototipi presentati in questa tesi.
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Ray, Zachary J. "Hand Orientation Feedback for Grasped Object Slip Prevention with a Prosthetic Hand." University of Akron / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=akron1461181998.
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Voils, Danny. "Scale Invariant Object Recognition Using Cortical Computational Models and a Robotic Platform." PDXScholar, 2012. https://pdxscholar.library.pdx.edu/open_access_etds/632.
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This paper proposes an end-to-end, scale invariant, visual object recognition system, composed of computational components that mimic the cortex in the brain. The system uses a two stage process. The first stage is a filter that extracts scale invariant features from the visual field. The second stage uses inference based spacio-temporal analysis of these features to identify objects in the visual field. The proposed model combines Numenta's Hierarchical Temporal Memory (HTM), with HMAX developed by MIT's Brain and Cognitive Science Department. While these two biologically inspired paradigms are based on what is known about the visual cortex, HTM and HMAX tackle the overall object recognition problem from different directions. Image pyramid based methods like HMAX make explicit use of scale, but have no sense of time. HTM, on the other hand, only indirectly tackles scale, but makes explicit use of time. By combining HTM and HMAX, both scale and time are addressed. In this paper, I show that HTM and HMAX can be combined to make a com- plete cortex inspired object recognition model that explicitly uses both scale and time to recognize objects in temporal sequences of images. Additionally, through experimentation, I examine several variations of HMAX and its
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Rusaw, Shawn. "Sensor-based motion planning via nonsmooth analysis." Thesis, University of Oxford, 2002. http://ora.ox.ac.uk/objects/uuid:46fa490d-c4ca-45ad-9cd5-b1f11920863d.
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In this thesis we present a novel approach to sensor-based motion planning developed using the mathematical tools provided by the field of nonsmooth analysis. The work is based on a broad body of background material developed using the tools of differential topology (smooth analysis), that is limited to simple cases like a point or circular robot. Nonsmooth analysis is required to extend this background work to the case of a polygonal robot moving amidst polygonal obstacles. We present a detailed nonsmooth analysis of the distance function for arbitrary configuration spaces and use this analysis to develop a planner for a rotating and translating polygonal mobile robot. Using the tools of nonsmooth analysis, we then describe a one-dimensional nonsmooth roadmap of the robot's freespace called the Nonsmooth Critical Set + Nonsmooth Generalised Voronoi Graph (NCRIT+NGVG) where the robot is equidistant to a number of obstacles, in a critical configuration or passing between two obstacles. We then use the related field of nonsmooth control theory to develop several provably stable control laws for following and exploring the nonsmooth roadmap. Finally, we implement a motion planner in simulation and for a real polygonal mobile robot, thus verifying the utility and practicality of the nonsmooth roadmap.
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Khusheef, Ahmed S. "Investigation on the mobile robot navigation in an unknown environment." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2013. https://ro.ecu.edu.au/theses/537.
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Mobile robots could be used to search, find, and relocate objects in many types of manufacturing operations and environments. In this scenario, the target objects might reside with equal probability at any location in the environment and, therefore, the robot must navigate and search the whole area autonomously, and be equipped with specific sensors to detect objects. Novel challenges exist in developing a control system, which helps a mobile robot achieve such tasks, including constructing enhanced systems for navigation, and vision-based object recognition. The latter is important for undertaking the exploration task that requires an optimal object recognition technique.
In this thesis, these challenges, for an indoor environment, were divided into three sub-problems. In the first, the navigation task involved discovering an appropriate exploration path for the entire environment, with minimal sensing requirements. The Bug algorithm strategies were adapted for modelling the environment and implementing the exploration path. The second was a visual-search process, which consisted of employing appropriate image-processing techniques, and choosing a suitable viewpoint field for the camera. This study placed more emphasis on colour segmentation, template matching and Speeded-Up Robust Features (SURF) for object detection. The third problem was the relocating process, which involved using a robot’s gripper to grasp the detected, desired object and then move it to the assigned, final location. This also included approaching both the target and the delivery site, using a visual tracking technique.
All codes were developed using C++ and C programming, and some libraries that included OpenCV and OpenSURF were utilized for image processing. Each control system function was tested both separately, and then in combination as a whole control program. The system performance was evaluated using two types of mobile robots: legged and wheeled. In this study, it was necessary to develop a wheeled search robot with a high performance processor. The experimental results demonstrated that the methodology used for the search robots was highly efficient provided the processor was adequate. It was concluded that it is possible to implement a navigation system within a minimum number of sensors if they are located and used effectively on the robot’s body. The main challenge within a visual-search process is that the environmental conditions are difficult to control, because the search robot executes its tasks in dynamic environments. The additional challenges of scaling these small robots up to useful industrial capabilities were also explored.
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Chen, Zengshi. "Dynamics and control of collision of multi-link humanoid robots with a rigid or elastic object." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1158442034.
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Schallert, Christian [Verfasser], Robert [Akademischer Betreuer] Luckner, Robert [Gutachter] Luckner, and Martin [Gutachter] Otter. "Integrated safety and reliability analysis methods for aircraft system development using multi-domain object-oriented models / Christian Schallert ; Gutachter: Robert Luckner, Martin Otter ; Betreuer: Robert Luckner." Berlin : Technische Universität Berlin, 2016. http://d-nb.info/1156181852/34.
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Souroulla, Timotheos. "Distributed Intelligence for Multi-Robot Environment : Model Compression for Mobile Devices with Constrained Computing Resources." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302151.
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Human-Robot Collaboration (HRC), where both humans and robots work in the same environment simultaneously, is an emerging field and has increased massively during the past decade. For this collaboration to be feasible and safe, robots need to perform a proper safety analysis to avoid hazardous situations. This safety analysis procedure involves complex computer vision tasks that require a lot of processing power. Therefore, robots with constrained computing resources cannot execute these tasks without any delays, thus for executing these tasks they rely on edge infrastructures, such as remote computational resources accessible over wireless communication. In some cases though, the edge may be unavailable, or connection to it may not be possible. In such cases, robots still have to navigate themselves around the environment, while maintaining high levels of safety. This thesis project focuses on reducing the complexity and the total number of parameters of pre-trained computer vision models by using model compression techniques, such as pruning and knowledge distillation. These model compression techniques have strong theoretical and practical foundations, but work on their combination is limited, therefore it is investigated in this work. The results of this thesis project show that in the test cases, up to 90% of the total number of parameters of a computer vision model can be removed without any considerable reduction in the model’s accuracy.Människa och robot samarbete (förkortat HRC från engelskans Human-Robot Collaboration), där både människor och robotar arbetar samtidigt i samma miljö, är ett växande forskningsområde och har ökat dramatiskt över de senaste decenniet. För att detta samarbetet ska vara möjligt och säkert behöver robotarna genomgå en ordentlig säkerhetsanalys så att farliga situationer kan undvikas. Denna säkerhetsanalys inkluderar komplexa Computer Vision uppgifter som kräver mycket processorkraft. Därför kan inte robotar med begränsad processorkraft utföra dessa beräkningar utan fördröjning, utan måste istället förlita sig på utomstående infrastruktur för att exekvera dem. Vid vissa tillfällen kan dock denna utomstående infrastruktur inte finnas på plats eller vara svår att koppla upp sig till. Även vid dessa tillfällen måste robotar fortfarande kunna navigera sig själva genom en lokal, och samtidigt upprätthålla hög grad av säkerhet. Detta projekt fokuserar på att reducera komplexiteten och det totala antalet parametrar av för-tränade Computer Vision-modeller genom att använda modellkompressionstekniker så som: Beskärning och kunskapsdestilering. Dessa modellkompressionstekniker har starka teoretiska grunder och praktiska belägg, men mängden arbeten kring deras kombinerade effekt är begränsad, därför är just det undersökt i detta arbetet. Resultaten av det här projektet visar att up till 90% av det totala antalet parametrar hos en Computer Vision-modell kan tas bort utan någon noterbar försämring av modellens säkerhet.
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Khokar, Karan Hariharan. "Human Intention Recognition Based Assisted Telerobotic Grasping of Objects in an Unstructured Environment." Scholar Commons, 2013. http://scholarcommons.usf.edu/etd/4909.
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In this dissertation work, a methodology is proposed to enable a robot to identify an object to be grasped and its intended grasp configuration while a human is teleoperating a robot towards the desired object. Based on the detected object and grasp configuration, the human is assisted in the teleoperation task. The environment is unstructured and consists of a number of objects, each with various possible grasp configurations. The identification of the object and the grasp configuration is carried out in real time, by recognizing the intention of the human motion. Simultaneously, the human user is assisted to preshape over the desired grasp configuration. This is done by scaling the components of the remote arm end-effector motion that lead to the desired grasp configuration and simultaneously attenuating the components that are in perpendicular directions. The complete process occurs while manipulating the master device and without having to interact with another interface.
Intention recognition from motion is carried out by using Hidden Markov Model (HMM) theory. First, the objects are classified based on their shapes. Then, the grasp configurations are preselected for each object class. The selection of grasp configurations is based on the human knowledge of robust grasps for the various shapes. Next, an HMM for each object class is trained by having a skilled teleoperator perform repeated preshape trials over each grasp configuration of the object class in consideration. The grasp configurations are modeled as the states of each HMM whereas the projections of translation and orientation vectors, over each reference vector, are modeled as observations. The reference vectors are the ideal translation and rotation trajectories that lead the remote arm end-effector towards a grasp configuration. During an actual grasping task performed by a novice or a skilled user, the trained model is used to detect their intention. The output probability of the HMM associated with each object in the environment is computed as the user is teleoperating towards the desired object. The object that is associated with the HMM which has the highest output probability, is taken as the desired object. The most likely Viterbi state sequence of the selected HMM gives the desired grasp configuration. Since an HMM is associated with every object, objects can be shuffled around, added or removed from the environment without the need to retrain the models. In other words, the HMM for each object class needs to be trained only once by a skilled teleoperator.
The intention recognition algorithm was validated by having novice users, as well as the skilled teleoperator, grasp objects with different grasp configurations from a dishwasher rack. Each object had various possible grasp configurations. The proposed algorithm was able to successfully detect the operator's intention and identify the object and the grasp configuration of interest. This methodology of grasping was also compared with unassisted mode and maximum-projection mode. In the unassisted mode, the operator teleoperated the arm without any assistance or intention recognition. In the maximum-projection mode, the maximum projection of the motion vectors was used to determine the intended object and the grasp configuration of interest. Six healthy and one wheelchair-bound individuals, each executed twelve pick-and-place trials in intention-based assisted mode and unassisted mode. In these trials, they picked up utensils from the dishwasher and laid them on a table located next to it. The relative positions and orientations of the utensils were changed at the end of every third trial. It was observed that the subjects were able to pick-and-place the objects 51% faster and with less number of movements, using the proposed method compared to the unassisted method. They found it much easier to execute the task using the proposed method and experienced less mental and overall workloads. Two able-bodied subjects also executed three preshape trials over three objects in intention-based assisted and maximum projection mode. For one of the subjects, the objects were shuffled at the end of the six trials and she was asked to carry out three more preshape trials in the two modes. This time, however, the subject was made to change their intention when she was about to preshape to the grasp configurations. It was observed that intention recognition was consistently accurate through the trajectory in the intention-based assisted method except at a few points. However, in the maximum-projection method the intention recognition was consistently inaccurate and fluctuated. This often caused to subject to be assisted in the wring directions and led to extreme frustration. The intention-based assisted method was faster and had less hand movements. The accuracy of the intention based method did not change when the objects were shuffled. It was also shown that the model for intention recognition can be trained by a skilled teleoperator and be used by a novice user to efficiently execute a grasping task in teleoperation.
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Li, Zhongmou. "Theoretical developments and experimental evaluation of a novel collaborative multi-drones grasping and manipulation system Zof large objects." Thesis, Ecole centrale de Nantes, 2021. http://www.theses.fr/2021ECDN0019.
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Cette thèse propose un nouveau concept de robot de manipulation aérienne appelé Flying Gripper. Ce robot est un manipulateur aérien, destiné à la saisie, la manipulation et le transport de grands objets de manière autonome. Le robot Flying Gripper est composé de quatre quadrotors, de quatre doigts auto-adaptatifs et d'un châssis. Les principaux apports de ces travaux sont: (1) un concept mécanique original reposant sur l'utilisation de plusieurs quadrotors et tirant parti de la rotation en lacet des quadrotors pour actionner un mécanisme de préhension auto-adaptatif et intrinsèquement sûr (2) une méthode pour analyser des torseurs disponibles en tenant compte des contraintes d'égalité et d'inégalité imposées par les limites d'actionnement, les butées mécaniques et les relations d'équilibre; (3) une commande prédictive permettant de manipuler l'objet saisie avec une masse, des inerties et un centre de masse inconnus; (4) un algorithme d'allocation de contrôle dynamique pour la distribution de l'effort de contrôle, de manière à optimiser l'efficacité énergétique et à assurer la continuité de la commande, en considérant les limites mécaniques du robot.Des simulations numériques et des tests expérimentaux ont été effectués pour valider les performances du contrôleurThis thesis proposes a new concept of aerial manipulation robot named Flying Gripper that is intended to perform grasping, manipulating, and transporting of large objects autonomously. The Flying Gripper robot is composed of four quadrotors, four self-adaptive fingers and a body structure. The main contributions of these works are: (1) an original mechanical concept using multiple quadrotors to obtain full manipulability in SE(3) and taking advantage of their yaw rotations to actuate a self-adaptive and intrinsically safe grasping mechanism; (2) a wrench capability analysis method taking into account the equality and inequality constraints imposed by actuation limits, mechanical stops and equilibrium relations; (3) a model predictive controller to deal with unknown mass, inertia and center of mass due to the grasped object; (4) a Dynamic Control Allocation algorithm to distribute the control output in a way that guarantees the continuity of actuator's velocity, improves the energy efficiency and satisfies the robot mechanical limits.Numerical simulations and experimental tests have been carried out to validate the controller performances
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Zamzow, Scottie L. "Ambassador of American airpower : Major General Robert Olds /." Maxwell AFB, Ala. : School of Advanced Air and Space Studies, 2008. https://www.afresearch.org/skins/rims/display.aspx?moduleid=be0e99f3-fc56-4ccb-8dfe-670c0822a153&mode=user&action=downloadpaper&objectid=e01c5779-0a3b-4ea3-999e-a35a94fd5600&rs=PublishedSearch.
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Staub, Nicolas. "Models, algorithms and architectures for cooperative manipulation with aerial and ground robots." Thesis, Toulouse 3, 2018. http://www.theses.fr/2018TOU30169/document.
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Les dernières années ont vu le développement de recherches portant sur l'interaction physique entre les robots aériens et leur environnement, accompagné de l'apparition de nombreux nouveaux systèmes mécaniques et approches de régulation. La communauté centrée autour de la robotique aérienne observe actuellement un déplacement de paradigmes des approches classiques de guidage, de navigation et de régulation vers des tâches moins triviales, telle le développement de l'interaction physique entre robots aériens et leur environnement. Ceci correspond à une extension des tâches dites de manipulation, du sol vers les airs. Cette thèse contribue au domaine de la manipulation aérienne en proposant un nouveau concept appelé MAGMaS, pour " Multiple Aerial Ground Manipulator System ". Les motivations qui ont conduites à l'association de manipulateurs terrestres et aériens pour effectuer des tâches de manipulation coopérative, résident dans une volonté d'exploiter leurs particularités respectives. Les manipulateurs terrestres apportant leur importante force et les manipulateurs aériens apportant leur vaste espace de travail. La première contribution de cette thèse présente une modélisation rigoureuse des MAGMaS. Les propriétés du système ainsi que ses possibles extensions sont discutées. Les méthodes de planning, d'estimation et de régulation nécessaire à l'exploitation des MAGMaS pour des tâches de manipulation collaborative sont dérivées. Ce travail propose d'exploiter les redondances des MAGMaS grâce à un algorithme optimal d'allocation de forces entre les manipulateurs. De plus, une méthode générale d'estimation de forces pour robots aériens est introduite. Toutes les techniques et les algorithmes présentés dans cette thèse sont intégrés dans une architecture globale, utilisée à la fois pour la simulation et la validation expérimentale. Cette architecture est en outre augmentée par l'addition d'une structure de télé-présence, afin de permettre l'opération à distances des MAGMaS. L'architecture générale est validée par une démonstration de levage de barre, qui est une application représentative des potentiels usages des MAGMaS. Une autre contribution relative au développement des MAGMaS consiste en une étude exploratoire de la flexibilité dans les objets manipulés par un MAGMaS. Un modèle du phénomène vibratoire est dérivé afin de mettre en exergue ses propriétés en termes de contrôle. La dernière contribution de cette thèse consiste en une étude exploratoire sur l'usage des actionneurs à raideur variable dans les robots aériens, dotant ces systèmes d'une compliance mécanique intrinsèque et de capacité de stockage d'énergie. Les fondements théoriques sont associés à la synthèse d'un contrôleur non-linéaire. L'approche proposée est validée par le biais d'expériences reposant sur l'intégration d'un actionneur à raideur variable léger sur un robot aérienIn recent years, the subject of physical interaction for aerial robots has been a popular research area with many new mechanical designs and control approaches being proposed. The aerial robotics community is currently observing a paradigm shift from classic guidance, navigation, and control tasks towards more unusual tasks, for example requesting aerial robots to physically interact with the environment, thus extending the manipulation task from the ground into the air. This thesis contributes to the field of aerial manipulation by proposing a novel concept known has Multiple Aerial-Ground Manipulator System or MAGMaS, including what appears to be the first experimental demonstration of a MAGMaS and opening a new route of research. The motivation behind associating ground and aerial robots for cooperative manipulation is to leverage their respective particularities, ground robots bring strength while aerial robots widen the workspace of the system. The first contribution of this work introduces a meticulous system model for MAGMaS. The system model's properties and potential extensions are discussed in this work. The planning, estimation and control methods which are necessary to exploit MAGMaS in a cooperative manipulation tasks are derived. This works proposes an optimal control allocation scheme to exploit the MAGMaS redundancies and a general model-based force estimation method is presented. All of the proposed techniques reported in this thesis are integrated in a global architecture used for simulations and experimental validation. This architecture is extended by the addition of a tele-presence framework to allow remote operations of MAGMaS. The global architecture is validated by robust demonstrations of bar lifting, an application that gives an outlook of the prospective use of the proposed concept of MAGMaS. Another contribution in the development of MAGMaS consists of an exploratory study on the flexibility of manipulated loads. A vibration model is derived and exploited to showcase vibration properties in terms of control. The last contribution of this thesis consists of an exploratory study on the use of elastic joints in aerial robots, endowing these systems with mechanical compliance and energy storage capabilities. Theoretical groundings are associated with a nonlinear controller synthesis. The proposed approach is validated by experimental work which relies on the integration of a lightweight variable stiffness actuator on an aerial robot
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Napier, Ashley A. "Vision & laser for road based navigation." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:faeb2cb6-d97c-43e2-b291-1564d1388bbd.
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This thesis presents novel solutions for two fundamental problems associated with autonomous road driving. The first is accurate and persistent localisation and the second is automatic extrinsic sensor calibration. We start by describing a stereo Visual Odometry (VO) system, which forms the basis of later chapters. This sparse approach to ego-motion estimation leverages the efficacy and speed of the BRIEF descriptor to measure frame-to-frame correspondences and infer subsequent motion. The system is able to output locally metric trajectory estimates as demonstrated on many kilometres of data. We then present a robust vision only localisation system based on a two-stage approach. Firstly we gather a representative survey in ideal weather and lighting conditions. We then leverage locally accurate VO trajectories to synthesise a high resolution orthographic image strip of the road surface. This road image provides a highly descriptive and stable template against which to match subsequent traversals. During the second phase, localisation, we use the VO to provide high frequency pose updates, but correct for the drift inherent in all locally derived pose estimates with low frequency updates from a dense image matching technique. Here a live image stream is registered against synthesised views of the road image generated form the survey. We use an information theoretic measure, Mutual Information, to determine the alignment of live images and synthesised views. Using this measure we are able to successfully localise subsequent traversals of surveyed routes under even the most intense lighting changes expected in outdoor applications. We demonstrate our system localising in multiple environments with accuracy commensurate to that of an Inertial Navigation System. Finally we present a technique for automatically determining the extrinsic calibration between a camera and Light Detection And Ranging (LIDAR) sensor in natural scenes. Rather than requiring a stationary platform as with prior art, we actually exploit platform motion allowing us to aggregate data and adopt a retrospective approach to calibration. Coupled with accurate timing this retrospective approach allows for sensors with non-overlapping fields of view to be calibrated as long as at some point the observed workspaces overlap. We then show how we can improve the accuracy of our calibration estimates by treating each single shot estimate as a noisy measurement and fusing them together using a recursive Bayes filter. We evaluate the calibration algorithm in multiple environments and demonstrate millimetre precision in translation and deci-degrees in rotation.
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Jebelli, Ali. "Development of Sensors and Microcontrollers for Underwater Robots." Thesis, Université d'Ottawa / University of Ottawa, 2014. http://hdl.handle.net/10393/31283.
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Nowadays, small autonomous underwater robots are strongly preferred for remote exploration of unknown and unstructured environments. Such robots allow the exploration and monitoring of underwater environments where a long term underwater presence is required to cover a large area. Furthermore, reducing the robot size, embedding electrical board inside and reducing cost are some of the challenges designers of autonomous underwater robots are facing. As a key device for reliable operation-decision process of autonomous underwater robots, a relatively fast and cost effective controller based on Fuzzy logic and proportional-integral-derivative method is proposed in this thesis. It efficiently models nonlinear system behaviors largely present in robot operation and for which mathematical models are difficult to obtain. To evaluate its response, the fault finding test approach was applied and the response of each task of the robot depicted under different operating conditions. The robot performance while combining all control programs and including sensors was also investigated while the number of program codes and inputs were increased.
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Paulin, Rémi. "human-robot motion : an attention-based approach." Thesis, Université Grenoble Alpes (ComUE), 2018. http://www.theses.fr/2018GREAM018.
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Pour les robots mobiles autonomes conçus pour partager notre environnement, la sécurité et l'efficacité de leur trajectoire ne sont pas les seuls aspects à prendre en compte pour la planification de leur mouvement: ils doivent respecter des règles sociales afin de ne pas gêner les personnes environnantes. Dans un tel contexte social, la plupart des techniques de planification de mouvement actuelles s'appuient fortement sur le concept d'espaces sociaux; de tels espaces sociaux sont cependant difficiles à modéliser et ils sont d'une utilisation limitée dans le contexte d'interactions homme-robot où l'intrusion dans les espaces sociaux est nécessaire. Ce travail présente une nouvelle approche pour la planification de mouvements dans un contexte social qui permet de gérer des environnements complexes ainsi que des situation d’interaction homme-robot. Plus précisément, le concept d'attention est utilisé pour modéliser comment l'influence de l'environnement dans son ensemble affecte la manière dont le mouvement du robot est perçu par les personnes environnantes. Un nouveau modèle attentionnel est introduit qui estime comment nos ressources attentionnelles sont partagées entre les éléments saillants de notre environnement. Basé sur ce modèle, nous introduisons le concept de champ attentionnel. Un planificateur de mouvement est ensuite développé qui s'appuie sur le champ attentionnel afin de produire des mouvements socialement acceptables. Notre planificateur de mouvement est capable d'optimiser simultanément plusieurs objectifs tels que la sécurité, l'efficacité et le confort des mouvements. Les capacités de l'approche proposée sont illustrées sur plusieurs scénarios simulés dans lesquels le robot est assigné différentes tâches. Lorsque la tâche du robot consiste à naviguer dans l'environnement sans causer de distraction, notre approche produit des résultats prometteurs même dans des situations complexes. Aussi, lorsque la tâche consiste à attirer l'attention d'une personne en vue d'interagir avec elle, notre planificateur de mouvement est capable de choisir automatiquement une destination qui exprime au mieux son désir d'interagir, tout en produisant un mouvement sûr, efficace et confortableFor autonomous mobile robots designed to share their environment with humans, path safety and efficiency are not the only aspects guiding their motion: they must follow social rules so as not to cause discomfort to surrounding people. Most socially-aware path planners rely heavily on the concept of social spaces; however, social spaces are hard to model and they are of limited use in the context of human-robot interaction where intrusion into social spaces is necessary. In this work, a new approach for socially-aware path planning is presented that performs well in complex environments as well as in the context of human-robot interaction. Specifically, the concept of attention is used to model how the influence of the environment as a whole affects how the robot's motion is perceived by people within close proximity. A new computational model of attention is presented that estimates how our attentional resources are shared amongst the salient elements in our environment. Based on this model, the novel concept of attention field is introduced and a path planner that relies on this field is developed in order to produce socially acceptable paths. To do so, a state-of-the-art many-objective optimization algorithm is successfully applied to the path planning problem. The capacities of the proposed approach are illustrated in several case studies where the robot is assigned different tasks. Firstly, when the task is to navigate in the environment without causing distraction our approach produces promising results even in complex situations. Secondly, when the task is to attract a person's attention in view of interacting with him or her, the motion planner is able to automatically choose a destination that best conveys its desire to interact whilst keeping the motion safe, efficient and socially acceptable
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Vestin, Albin, and Gustav Strandberg. "Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms." Thesis, Linköpings universitet, Reglerteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160020.
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Today, the main research field for the automotive industry is to find solutions for active safety. In order to perceive the surrounding environment, tracking nearby traffic objects plays an important role. Validation of the tracking performance is often done in staged traffic scenarios, where additional sensors, mounted on the vehicles, are used to obtain their true positions and velocities. The difficulty of evaluating the tracking performance complicates its development. An alternative approach studied in this thesis, is to record sequences and use non-causal algorithms, such as smoothing, instead of filtering to estimate the true target states. With this method, validation data for online, causal, target tracking algorithms can be obtained for all traffic scenarios without the need of extra sensors. We investigate how non-causal algorithms affects the target tracking performance using multiple sensors and dynamic models of different complexity. This is done to evaluate real-time methods against estimates obtained from non-causal filtering. Two different measurement units, a monocular camera and a LIDAR sensor, and two dynamic models are evaluated and compared using both causal and non-causal methods. The system is tested in two single object scenarios where ground truth is available and in three multi object scenarios without ground truth. Results from the two single object scenarios shows that tracking using only a monocular camera performs poorly since it is unable to measure the distance to objects. Here, a complementary LIDAR sensor improves the tracking performance significantly. The dynamic models are shown to have a small impact on the tracking performance, while the non-causal application gives a distinct improvement when tracking objects at large distances. Since the sequence can be reversed, the non-causal estimates are propagated from more certain states when the target is closer to the ego vehicle. For multiple object tracking, we find that correct associations between measurements and tracks are crucial for improving the tracking performance with non-causal algorithms.
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Mustaki, Simon Éliakim. "Outils de pré-calibration numérique des lois de commande de systèmes de systèmes : application aux aides à la conduite et au véhicule autonome." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2019. http://www.theses.fr/2019IMTA0142/document.
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Cette thèse est dédiée à la pré-calibration des nouveaux systèmes d’aides à la conduite (ADAS). Le développement de ces systèmes est devenu aujourd’hui un axe de recherche stratégique pour les constructeurs automobiles dans le but de proposer des véhicules plus sûrs et moins énergivores. Cette thèse contribue à une vision méthodologique multi-critère, multi-modèle et multi-scénario. Elle en propose une instanciation particulière pour la pré-calibration spécifique au Lane Centering Assistance (LCA). Elle s’appuie sur des modèles dynamiques de complexité juste nécessaire du véhicule et de son environnement pour, dans le cadre du formalisme H2/H∞, formaliser et arbitrer les compromis entre performance de suivi de voie, confort des passagers et robustesse. Les critères élaborés sont définis de manière à être d’interprétation aisée, car directement liés à la physique, et facilement calculables. Ils s’appuient sur des modèles de perturbations exogènes (e.g. courbure de la route ou rafale de vent) et de véhicules multiples mais représentatifs, de manière à réduire autant que possible le pessimisme tout en embrassant l’ensemble des situations réalistes. Des simulations et des essais sur véhicules démontrent l’intérêt de l’approcheThis thesis deals with the tuning of the new Advanced Driving Assistance Systems (ADAS). The development of these systems has become nowadays a strategic line of research for the automotive industry towards the conception of safer and fuel-efficient vehicles.This thesis contributes to a multi-criterion, multi-modeland multi-scenario methodological vision of the tuning process. It is presented through a specific application of the tuning of the Lane Centering Assistance (LCA). It relies on vehicle and environment’s dynamical models of adequate complexity in the aim of formalizing and managing, in a H2/H∞ framework, the trade-off between performance, comfort and robustness. The formulated criteria are easy to compute and defined in a way to be understandable, closely linked to practical specifications. The whole methodology is driven by the research of a pertinent trade-off between realism (being as closest as possible to reality) and complexity (quick evaluation of the criterion). The efficiency and the robustness of the approach is demonstrated through high-fidelity simulations and numerous tests on real vehicles
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Huard, Benoît. "Contribution à la modélisation non-linéaire et à la commande d'un actionneur robotique intégré pour la manipulation." Thesis, Poitiers, 2013. http://www.theses.fr/2013POIT2262/document.
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La réalisation de tâches de manipulation dextres requiert une complexité aussi bien dans la conception de préhenseur robotique que dans la synthèse de leurs lois de commande. Une optimisation de la mécatronique de ces systèmes permet de répondre aux contraintes d'intégration fonctionnelle en se passant de capteurs de force terminaux. L'utilisation de mécanismes réversibles rend alors possible la détermination du positionnement du système dans l'espace libre et la détection de son interaction avec les objets manipulés, grâce aux mesures proprioceptives inhérentes aux actionneurs électriques. L'objectif de cette thèse est de parvenir synthétiser, dans le contexte articulaire (un degré-de-liberté), une commande adaptée à la manipulation en tenant compte de ces particularités mécaniques. La méthode proposée est basée sur une commande robuste par rapport aux non-linéarités structurelles dues aux effets gravitationnels et aux frottements secs d'une part et par rapport aux rigidités variables des objets manipulés. L'approche choisie nécessite la connaissance précise de la configuration du système étudié à chaque instant. Une représentation dynamique de son comportement permet de synthétiser un capteur logiciel pour l'estimation des grandeurs indispensables à la commande. Ces différentes étapes sont validées par des essais expérimentaux pour justifier la démarche choisie menant à une commande adaptée à la manipulation d'objetsThe realization of dexterous manipulation tasks requires a complexity in robotic hands design as well as in their control laws synthesis. A mecatronical optimization of these systems helps to answer for functional integration constraints by avoiding external force sensors. Back-drivable mechanics allows the free-space positioning determination of such system as far as the detection of its interaction with a manipulated object thanks to proprioceptives measures at electric actuator level. The objective of this thesis is to synthesize a control law adapted to object manipulation by taking into account these mechanical properties in a one degree-of-freedom case. The proposed method is based on a robust control according to structural non-linearities due to gravitational effects and dry frictions on the one hand and with regard to a variable rigidity of manipulated objects on the other hand. The chosen approach requires a precise knowledge of the system configuration at all time. A dynamic representation of its behavior enables a software sensor synthesis for the exteroceptives variables estimation in a control law application purpose. The different steps are experimentally validated in order to justify the chosen approach leading to object manipulation
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Cass, Todd A. "Robust 2-D Model-Based Object Recognition." 1988. http://hdl.handle.net/1721.1/6823.
Full textAbstract:
Techniques, suitable for parallel implementation, for robust 2D model-based object recognition in the presence of sensor error are studied. Models and scene data are represented as local geometric features and robust hypothesis of feature matchings and transformations is considered. Bounds on the error in the image feature geometry are assumed constraining possible matchings and transformations. Transformation sampling is introduced as a simple, robust, polynomial-time, and highly parallel method of searching the space of transformations to hypothesize feature matchings. Key to the approach is that error in image feature measurement is explicitly accounted for. A Connection Machine implementation and experiments on real images are presented.
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TOSATO, Diego. "Tensor Representations for Object Classification and Detection." Doctoral thesis, 2012. http://hdl.handle.net/11562/393739.
Full textAbstract:
A key problem in object recognition is finding a suitable object representation.
For historical and computational reasons, vector descriptions that encode particular
statistical properties of the data have been broadly applied. However, employing
tensor representation can describe the interactions of multiple factors
inherent to image formation. One of the most convenient uses for tensors is to represent
complex objects in order to build a discriminative description.
Thus thesis has several main contributions, focusing on visual data detection (e.g. of heads or pedestrians) and classification (e.g. of head or human body orientation) in still images and on machine learning techniques to analyse tensor data. These applications are among the most studied in computer vision and are typically formulated as binary or multi-class classification problems.
The applicative context of this thesis is the video surveillance, where classification and detection tasks
can be very hard, due to the scarce resolution and the noise characterising
sensor data. Therefore, the main goal in that context is to design algorithms that can
characterise different objects of interest, especially when immersed in a cluttered
background and captured at low resolution.
In the different amount of machine learning approaches, the ensemble-of-classifiers demonstrated to reach
excellent classification accuracy, good generalisation ability, and robustness of noisy data. For these
reasons, some approaches in that class have been adopted as basic machine classification
frameworks to build robust classifiers and detectors. Moreover, also
kernel machines has been exploited for classification purposes,
since they represent a natural learning framework for tensors.
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Bax, Ingo [Verfasser]. "Hierarchical feed forward models for robust object recognition / Ingo Bax." 2007. http://d-nb.info/984822666/34.
Full text
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Alter, Tao Daniel. "Robust and Efficient 3D Recognition by Alignment." 1992. http://hdl.handle.net/1721.1/6799.
Full textAbstract:
Alignment is a prevalent approach for recognizing 3D objects in 2D images. A major problem with current implementations is how to robustly handle errors that propagate from uncertainties in the locations of image features. This thesis gives a technique for bounding these errors. The technique makes use of a new solution to the problem of recovering 3D pose from three matching point pairs under weak-perspective projection. Furthermore, the error bounds are used to demonstrate that using line segments for features instead of points significantly reduces the false positive rate, to the extent that alignment can remain reliable even in cluttered scenes.
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Chen, Kuo-Wei, and 陳國瑋. "Robot Parallel Tracking and Mapping Using Online Sparse-representation Object Model." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/86186908796053422473.
Full textAbstract:
碩士淡江大學
機械與機電工程學系碩士班
100
This thesis presents an algorithm of moving object detection and tracking for robot concurrently localization, mapping and moving object tracking in dynamic environment. The major research topics include on-line object model construction as well as moving object detection and tracking. Apparent image features are detected and utilized as the training data for on-line constructing and sparse representing the object model. After the object model is constructed, the model elements are further matched with the image features obtained from the environment in order to recognize the object and search the object position in image and cartesian spaces. Furthermore, the developed algorithm is integrated with the methods of moving object detection, state estimation, and visual sensing to develop a system which is capable of tracking moving objects using moving camera. The detecting and tracking method developed in this thesis is capable of being applied in many systems such as mobile robot, wheelchair, car, and aerial robot to implement the tasks of parallel tracking and mapping in dynamic environments.
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Chen, Hsi Che, and 陳希哲. "Robot grasping System with Structure from motion and Shape Reconstruction by 3D Object Model." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/77119073180045270159.
Full textAbstract:
碩士國立臺灣師範大學
機電科技學系
101
This research’s object is used multi-view image rebuild 3D model, and then we use the 3D model detect the stable grasp position. Normally in stereo machine vision research, use binocular vision method is more popular. This method is likely our eyes, use disparity to calculate depth signal between camera and object. But this method can only use on static image, if camera is putted on robot manipulator or moving car. The past method will fail to reconstruction 3D model. So in this research we will use structure from motion to find the relationship between origin cameras to another from sequence images. And we will reconstruction 3D model. We use mono-camera get the image, and use Harris corner detector to find the keypoints. This research have 3 parts process, first extract the feature from images, and calculate the sequence images, tracking keypoints, Final we use Factorization method to get the camera and object’s relationship from 3D coordination.
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Yang, Teng-Chih, and 楊登智. "APPLICATION OF DIFFERENTIAL EVOLUTION BASED CEREBELLAR MODEL ARTICULATION CONTROLLER FOR ROBOT MANIPULATOR OBJECT TRACKING VIA SINGLE CAMERA VISION." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/71192357813773234412.
Full textAbstract:
碩士大同大學
電機工程學系(所)
101
In this thesis, the differential evolution cerebellar model articulation controller (DECMAC) which is applied to the robot manipulator object tracking via camera vision is proposed. In conventional CMAC, since there are several parameters which are required to be preset and difficult to be found, the DE is utilized to overcome the foregoing problem. Based on the discrete-type Lyapunov function, the stability of the proposed controller is guaranteed. In order to verify the performance of the DECMAC, it is applied to the robot manipulator object tracking which is based on the computer vision and CAMSHIFT method. The simulation and experimental results are implemented to demonstrate the effectiveness of the proposed the controller.
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Chang, Guoting. "Robot Motion and Task Learning with Error Recovery." Thesis, 2013. http://hdl.handle.net/10012/7627.
Full textAbstract:
The ability to learn is essential for robots to function and perform services within a dynamic human environment. Robot programming by demonstration facilitates learning through a human teacher without the need to develop new code for each task that the robot performs. In order for learning to be generalizable, the robot needs to be able to grasp the underlying structure of the task being learned. This requires appropriate knowledge abstraction and representation. The goal of this thesis is to develop a learning by imitation system that abstracts knowledge of human demonstrations of a task and represents the abstracted knowledge in a hierarchical framework. The learning by imitation system is capable of performing both action and object recognition based on video stream data at the lower level of the hierarchy, while the sequence of actions and object states observed is reconstructed at the higher level of the hierarchy in order to form a coherent representation of the task. Furthermore, error recovery capabilities are included in the learning by imitation system to improve robustness to unexpected situations during task execution. The first part of the thesis focuses on motion learning to allow the robot to both recognize the actions for task representation at the higher level of the hierarchy and to perform the actions to imitate the task. In order to efficiently learn actions, the actions are segmented into meaningful atomic units called motion primitives. These motion primitives are then modeled using dynamic movement primitives (DMPs), a dynamical system model that can robustly generate motion trajectories to arbitrary goal positions while maintaining the overall shape of the demonstrated motion trajectory. The DMPs also contain weight parameters that are reflective of the shape of the motion trajectory. These weight parameters are clustered using affinity propagation (AP), an efficient exemplar clustering algorithm, in order to determine groups of similar motion primitives and thus, performing motion recognition. The approach of DMPs combined with APs was experimentally verified on two separate motion data sets for its ability to recognize and generate motion primitives. The second part of the thesis outlines how the task representation is created and used for imitating observed tasks. This includes object and object state recognition using simple computer vision techniques as well as the automatic construction of a Petri net (PN) model to describe an observed task. Tasks are composed of a sequence of actions that have specific pre-conditions, i.e. object states required before the action can be performed, and post-conditions, i.e. object states that result from the action. The PNs inherently encode pre-conditions and post-conditions of a particular event, i.e. action, and can model tasks as a coherent sequence of actions and object states. In addition, PNs are very flexible in modeling a variety of tasks including tasks that involve both sequential and parallel components. The automatic PN creation process has been tested on both a sequential two block stacking task and a three block stacking task involving both sequential and parallel components. The PN provides a meaningful representation of the observed tasks that can be used by a robot to imitate the tasks. Lastly, error recovery capabilities are added to the learning by imitation system in order to allow the robot to readjust the sequence of actions needed during task execution. The error recovery component is able to deal with two types of errors: unexpected, but known situations and unexpected, unknown situations. In the case of unexpected, but known situations, the learning system is able to search through the PN to identify the known situation and the actions needed to complete the task. This ability is useful not only for error recovery from known situations, but also for human robot collaboration, where the human unexpectedly helps to complete part of the task. In the case of situations that are both unexpected and unknown, the robot will prompt the human demonstrator to teach how to recover from the error to a known state. By observing the error recovery procedure and automatically extending the PN with the error recovery information, the situation encountered becomes part of the known situations and the robot is able to autonomously recover from the error in the future. This error recovery approach was tested successfully on errors encountered during the three block stacking task.