284 research outputs found

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    A Taxonomy of Freehand Grasping Patterns in Virtual Reality

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    Grasping is the most natural and primary interaction paradigm people perform every day, which allows us to pick up and manipulate objects around us such as drinking a cup of coffee or writing with a pen. Grasping has been highly explored in real environments, to understand and structure the way people grasp and interact with objects by presenting categories, models and theories for grasping approach. Due to the complexity of the human hand, classifying grasping knowledge to provide meaningful insights is a challenging task, which led to researchers developing grasp taxonomies to provide guidelines for emerging grasping work (such as in anthropology, robotics and hand surgery) in a systematic way. While this body of work exists for real grasping, the nuances of grasping transfer in virtual environments is unexplored. The emerging development of robust hand tracking sensors for virtual devices now allow the development of grasp models that enable VR to simulate real grasping interactions. However, present work has not yet explored the differences and nuances that are present in virtual grasping compared to real object grasping, which means that virtual systems that create grasping models based on real grasping knowledge, might make assumptions which are yet to be proven true or untrue around the way users intuitively grasp and interact with virtual objects. To address this, this thesis presents the first user elicitation studies to explore grasping patterns directly in VR. The first study presents main similarities and differences between real and virtual object grasping, the second study furthers this by exploring how virtual object shape influences grasping patterns, the third study focuses on visual thermal cues and how this influences grasp metrics, and the fourth study focuses on understanding other object characteristics such as stability and complexity and how they influence grasps in VR. To provide structured insights on grasping interactions in VR, the results are synthesized in the first VR Taxonomy of Grasp Types, developed following current methods for developing grasping and HCI taxonomies and re-iterated to present an updated and more complete taxonomy. Results show that users appear to mimic real grasping behaviour in VR, however they also illustrate that users present issues around object size estimation and generally a lower variability in grasp types is used. The taxonomy shows that only five grasps account for the majority of grasp data in VR, which can be used for computer systems aiming to achieve natural and intuitive interactions at lower computational cost. Further, findings show that virtual object characteristics such as shape, stability and complexity as well as visual cues for temperature influence grasp metrics such as aperture, category, type, location and dimension. These changes in grasping patterns together with virtual object categorisation methods can be used to inform design decisions when developing intuitive interactions and virtual objects and environments and therefore taking a step forward in achieving natural grasping interaction in VR

    Synergy-Based Human Grasp Representations and Semi-Autonomous Control of Prosthetic Hands

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    Das sichere und stabile Greifen mit humanoiden Roboterhänden stellt eine große Herausforderung dar. Diese Dissertation befasst sich daher mit der Ableitung von Greifstrategien für Roboterhände aus der Beobachtung menschlichen Greifens. Dabei liegt der Fokus auf der Betrachtung des gesamten Greifvorgangs. Dieser umfasst zum einen die Hand- und Fingertrajektorien während des Greifprozesses und zum anderen die Kontaktpunkte sowie den Kraftverlauf zwischen Hand und Objekt vom ersten Kontakt bis zum statisch stabilen Griff. Es werden nichtlineare posturale Synergien und Kraftsynergien menschlicher Griffe vorgestellt, die die Generierung menschenähnlicher Griffposen und Griffkräfte erlauben. Weiterhin werden Synergieprimitive als adaptierbare Repräsentation menschlicher Greifbewegungen entwickelt. Die beschriebenen, vom Menschen gelernten Greifstrategien werden für die Steuerung robotischer Prothesenhände angewendet. Im Rahmen einer semi-autonomen Steuerung werden menschenähnliche Greifbewegungen situationsgerecht vorgeschlagen und vom Nutzenden der Prothese überwacht

    Bayesian Optimization for robust robotic grasping

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    Among the most complex tasks performed by humans is the manipulation of objects. In robotics, automating these tasks has applications in a variety of environments, such as the development of industrial processes or providing assistance to people with physical or motor disabilities. Using bio-inspired robotic hands is helping the emergence of increasingly robust and dexterous grasping strategies. However, the difficulty lies in adapting these strategies to the variety of tasks and objects, which can often be unknown also involving the computational overhead of identifying them and reconfiguring the grasp. The brute-force solution is to learn new grasps by trial and error. This method however is inefficient and ineffective, as it is based on pure randomness. In contrast, Bayesian optimization allows us to turn this process into active learning, where each attempt adds information to the approximation of an optimal grasp, in a manner analogous to a child learning. The present work aims to test Bayesian optimization in this context, providing some techniques to enhance its performance, and experimenting not only in simulation but also on real robots, as well as studying different grasp metrics that allow the grasp evaluation during the optimization process and how they behave when computing from a real system. For this, along this work, we implemented a realistic simulation environment using PyBullet, which emulates the real experimental environment. This work provides experimental results using the Light Weight robotic arm, designed at the German Aerospace Center (DLR), and two tridactyl robotic hands, the CLASH (DLR) and ReFlex TakkTile (Right Hand Robotic), demonstrating the usefulness of the method for performing unknown object grasping even in the presence of noise and uncertainty inherent in a real-world environment. Consequently, this work contributes with practical knowledge to the studied field and serves as a proof-of-concept for future grasp planning and robotic manipulation technology

    Data-driven robotic manipulation of cloth-like deformable objects : the present, challenges and future prospects

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    Manipulating cloth-like deformable objects (CDOs) is a long-standing problem in the robotics community. CDOs are flexible (non-rigid) objects that do not show a detectable level of compression strength while two points on the article are pushed towards each other and include objects such as ropes (1D), fabrics (2D) and bags (3D). In general, CDOs’ many degrees of freedom (DoF) introduce severe self-occlusion and complex state–action dynamics as significant obstacles to perception and manipulation systems. These challenges exacerbate existing issues of modern robotic control methods such as imitation learning (IL) and reinforcement learning (RL). This review focuses on the application details of data-driven control methods on four major task families in this domain: cloth shaping, knot tying/untying, dressing and bag manipulation. Furthermore, we identify specific inductive biases in these four domains that present challenges for more general IL and RL algorithms.Publisher PDFPeer reviewe

    Improved Deep Neural Networks for Generative Robotic Grasping

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    This thesis provides a thorough evaluation of current state-of-the-art robotic grasping methods and contributes to a subset of data-driven grasp estimation approaches, termed generative models. These models aim to directly generate grasp region proposals from a given image without the need for a separate analysis and ranking step, which can be computationally expensive. This approach allows for fully end-to-end training of a model and quick closed-loop operation of a robot arm. A number of limitations are identified within these generative models, which are identified and addressed. Contributions are proposed that directly target each stage of the training pipeline that help to form accurate grasp proposals and generalise better to unseen objects. Firstly, inspired by theories of object manipulation within the mammalian visual system, the use of multi-task learning in existing generative architectures is evaluated. This aims to improve the performance of grasping algorithms when presented with impoverished colour (RGB) data by training models to perform simultaneous tasks such as object categorisation, saliency detection, and depth reconstruction. Secondly, a novel loss function is introduced which improves overall performance by rewarding the network to focus only on learning grasps at suitable positions. This reduces overall training times and results in better performance on fewer training examples. The last contribution analyses the problems with the most common metric used for evaluating and comparing offline performance between different grasping models and algorithms. To this end, a Gaussian method of representing ground-truth labelled grasps is put forward, which optimal grasp locations tested in a simulated grasping environment. The combination of these novel additions to generative models results in improved grasp success, accuracy, and performance on common benchmark datasets compared to previous approaches. Furthermore, the efficacy of these contributions is also tested when transferred to a physical robotic arm, demonstrating the ability to effectively grasp previously unseen 3D printed objects of varying complexity and difficulty without the need for domain adaptation. Finally, the future directions are discussed for generative convolutional models within the overall field of robotic grasping

    Design, fabrication and stiffening of soft pneumatic robots

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    Although compliance allows the soft robot to be under-actuated and generalise its control, it also impacts the ability of the robot to exert forces on the environment. There is a trade-off between robots being compliant or precise and strong. Many mechanisms that change robots' stiffness on demand have been proposed, but none are perfect, usually compromising the device's compliance and restricting its motion capabilities. Keeping the above issues in mind, this thesis focuses on creating robust and reliable pneumatic actuators, that are designed to be easily manufactured with simple tools. They are optimised towards linear behaviour, which simplifies modelling and improve control strategies. The principle idea in relation to linearisation is a reinforcement strategy designed to amplify the desired, and limit the unwanted, deformation of the device. Such reinforcement can be achieved using fibres or 3D printed structures. I have shown that the linearity of the actuation is, among others, a function of the reinforcement density and shape, in that the response of dense fibre-reinforced actuators with a circular cross-section is significantly more linear than that of non-reinforced or non-circular actuators. I have explored moulding manufacturing techniques and a mixture of 3D printing and moulding. Many aspects of these techniques have been optimised for reliability, repeatability, and process simplification. I have proposed and implemented a novel moulding technique that uses disposable moulds and can easily be used by an inexperienced operator. I also tried to address the compliance-stiffness trade-off issue. As a result, I have proposed an intelligent structure that behaves differently depending on the conditions. Thanks to its properties, such a structure could be used in applications that require flexibility, but also the ability to resist external disturbances when necessary. Due to its nature, individual cells of the proposed system could be used to implement physical logic elements, resulting in embodied intelligent behaviours. As a proof-of-concept, I have demonstrated use of my actuators in several applications including prosthetic hands, octopus, and fish robots. Each of those devices benefits from a slightly different actuation system but each is based on the same core idea - fibre reinforced actuators. I have shown that the proposed design and manufacturing techniques have several advantages over the methods used so far. The manufacturing methods I developed are more reliable, repeatable, and require less manual work than the various other methods described in the literature. I have also shown that the proposed actuators can be successfully used in real-life applications. Finally, one of the most important outcomes of my research is a contribution to an orthotic device based on soft pneumatic actuators. The device has been successfully deployed, and, at the time of submission of this thesis, has been used for several months, with good results reported, by a patient

    Grasp Multiple Objects with One Hand

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    The human hand's complex kinematics allow for simultaneous grasping and manipulation of multiple objects, essential for tasks like object transfer and in-hand manipulation. Despite its importance, robotic multi-object grasping remains underexplored and presents challenges in kinematics, dynamics, and object configurations. This paper introduces MultiGrasp, a two-stage method for multi-object grasping on a tabletop with a multi-finger dexterous hand. It involves (i) generating pre-grasp proposals and (ii) executing the grasp and lifting the objects. Experimental results primarily focus on dual-object grasping and report a 44.13% success rate, showcasing adaptability to unseen object configurations and imprecise grasps. The framework also demonstrates the capability to grasp more than two objects, albeit at a reduced inference speed

    Real-Time Motion Planning for In-Hand Manipulation with a Multi-Fingered Hand

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    Dexterous manipulation of objects once held in hand remains a challenge. Such skills are, however, necessary for robotics to move beyond gripper-based manipulation and use all the dexterity offered by anthropomorphic robotic hands. One major challenge when manipulating an object within the hand is that fingers must move around the object while avoiding collision with other fingers or the object. Such collision-free paths must be computed in real-time, as the smallest deviation from the original plan can easily lead to collisions. We present a real-time approach to computing collision-free paths in a high-dimensional space. To guide the exploration, we learn an explicit representation of the free space, retrievable in real-time. We further combine this representation with closed-loop control via dynamical systems and sampling-based motion planning and show that the combination increases performance compared to alternatives, offering efficient search of feasible paths and real-time obstacle avoidance in a multi-fingered robotic hand
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