38 research outputs found

    An adaptive framework for changing-contact robot manipulation

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    Many robot manipulation tasks require the robot to make and break contact with other objects in the environment. The interaction dynamics of such tasks vary markedly before and after contact. They are also strongly influenced by the nature and physical properties of the objects involved, i.e., by factors such as type of contact, surface friction, and applied force. Many industrial assembly tasks and human manipulation tasks, e.g., peg insertion, stacking, and screwing, are instances of such `changing-contact' manipulation tasks. In such tasks, the interaction dynamics is discontinuous when the robot makes or breaks contact but smooth at other times, making it a piecewise continuous dynamical system. The discontinuities experienced by a robot during such tasks can be harmful to the robot and/or object. Designing a framework for smooth online control of changing-contact manipulation tasks is a challenging open problem. To complete any manipulation task without data-intensive pre-training, the robot has to plan a motion trajectory, and execute this trajectory accurately and smoothly. Many methods have been developed for the former part of the problem in the form of planners that compute a suitable trajectory while considering relevant motion constraints and environmental obstacles. This thesis focuses on the relatively less-explored latter (i.e., plan execution) part of the problem in the context of changing-contact manipulation tasks. It does so by developing an adaptive, task-space, hybrid control framework that enables efficient, smooth, and accurate following of any given motion trajectory in the presence of piecewise continuous interaction dynamics. The framework makes three key contributions. The first contribution of this thesis addresses the problem of controlling a robot performing continuous-contact tasks in the presence of smoothly-changing environment dynamics. Specifically, we provide a task-space control framework that incrementally models and predicts the end-effector wrenches, and uses the discrepancies between the predicted and measured values to revise the predictive (forward) model and to achieve smooth trajectory tracking by adapting the impedance parameters of a force-motion controller. The second contribution of the thesis expands our framework to handle interaction dynamics that can be discontinuous due to making and breaking of contacts or due to discrete changes in the environment. We formulate the piecewise continuous interaction dynamics of the robot as a hybrid dynamical system with previously unknown discrete dynamic modes. We propose a corresponding hybrid framework that incrementally identifies new or existing modes, and adapts the parameters of the dynamics models within each such mode to provide smooth and accurate tracking of the target motion trajectory. The third contribution of the thesis focuses on handling contact changes and reducing discontinuities in the interaction dynamics during mode transitions. Specifically, we develop a framework with a contact anticipation model that incrementally and probabilistically updates its estimates of when contact changes occur due to making or breaking contact, or changes in the properties of objects. The estimated contact positions are used to guide a transition to (and from) special `transition phase' controllers whose parameters are adapted online to minimise discontinuities (i.e., to minimise spikes in force, jerk etc) in the regions of anticipated contacts. The stated contributions and each part of the framework are grounded and evaluated in simulation and on a physical robot performing illustrative changing-contact manipulation tasks on a tabletop. We experimentally compare our framework with some baselines to demonstrate the importance of building an incremental, adaptive framework for such tasks. In particular, we compare our controller for continuous-contact tasks with representative baselines in the adaptive control literature, and demonstrate the benefits of an incrementally-updated predictive (forward) model. We also experimentally evaluate the ability of our hybrid framework to accurately identify and model the dynamics of discrete dynamic (contact) modes, and justify the need for online updates by comparing the performance of a state of the art offline methods for hybrid dynamical systems. Finally, we evaluate the ability of our framework to accurately estimate contact positions and minimise discontinuities in the interaction dynamics in motion trajectories involving multiple contact changes

    Human-Machine Interfaces using Distributed Sensing and Stimulation Systems

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    As the technology moves towards more natural human-machine interfaces (e.g. bionic limbs, teleoperation, virtual reality), it is necessary to develop a sensory feedback system in order to foster embodiment and achieve better immersion in the control system. Contemporary feedback interfaces presented in research use few sensors and stimulation units to feedback at most two discrete feedback variables (e.g. grasping force and aperture), whereas the human sense of touch relies on a distributed network of mechanoreceptors providing a wide bandwidth of information. To provide this type of feedback, it is necessary to develop a distributed sensing system that could extract a wide range of information during the interaction between the robot and the environment. In addition, a distributed feedback interface is needed to deliver such information to the user. This thesis proposes the development of a distributed sensing system (e-skin) to acquire tactile sensation, a first integration of distributed sensing system on a robotic hand, the development of a sensory feedback system that compromises the distributed sensing system and a distributed stimulation system, and finally the implementation of deep learning methods for the classification of tactile data. It\u2019s core focus addresses the development and testing of a sensory feedback system, based on the latest distributed sensing and stimulation techniques. To this end, the thesis is comprised of two introductory chapters that describe the state of art in the field, the objectives, and the used methodology and contributions; as well as six studies that tackled the development of human-machine interfaces

    Touching on elements for a non-invasive sensory feedback system for use in a prosthetic hand

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    Hand amputation results in the loss of motor and sensory functions, impacting activities of daily life and quality of life. Commercially available prosthetic hands restore the motor function but lack sensory feedback, which is crucial to receive information about the prosthesis state in real-time when interacting with the external environment. As a supplement to the missing sensory feedback, the amputee needs to rely on visual and audio cues to operate the prosthetic hand, which can be mentally demanding. This thesis revolves around finding potential solutions to contribute to an intuitive non-invasive sensory feedback system that could be cognitively less burdensome and enhance the sense of embodiment (the feeling that an artificial limb belongs to one’s own body), increasing acceptance of wearing a prosthesis.A sensory feedback system contains sensors to detect signals applied to the prosthetics. The signals are encoded via signal processing to resemble the detected sensation delivered by actuators on the skin. There is a challenge in implementing commercial sensors in a prosthetic finger. Due to the prosthetic finger’s curvature and the fact that some prosthetic hands use a covering rubber glove, the sensor response would be inaccurate. This thesis shows that a pneumatic touch sensor integrated into a rubber glove eliminates these errors. This sensor provides a consistent reading independent of the incident angle of stimulus, has a sensitivity of 0.82 kPa/N, a hysteresis error of 2.39±0.17%, and a linearity error of 2.95±0.40%.For intuitive tactile stimulation, it has been suggested that the feedback stimulus should be modality-matched with the intention to provide a sensation that can be easily associated with the real touch on the prosthetic hand, e.g., pressure on the prosthetic finger should provide pressure on the residual limb. A stimulus should also be spatially matched (e.g., position, size, and shape). Electrotactile stimulation has the ability to provide various sensations due to it having several adjustable parameters. Therefore, this type of stimulus is a good candidate for discrimination of textures. A microphone can detect texture-elicited vibrations to be processed, and by varying, e.g., the median frequency of the electrical stimulation, the signal can be presented on the skin. Participants in a study using electrotactile feedback showed a median accuracy of 85% in differentiating between four textures.During active exploration, electrotactile and vibrotactile feedback provide spatially matched modality stimulations, providing continuous feedback and providing a displaced sensation or a sensation dispatched on a larger area. Evaluating commonly used stimulation modalities using the Rubber Hand Illusion, modalities which resemble the intended sensation provide a more vivid illusion of ownership for the rubber hand.For a potentially more intuitive sensory feedback, the stimulation can be somatotopically matched, where the stimulus is experienced as being applied on a site corresponding to their missing hand. This is possible for amputees who experience referred sensation on their residual stump. However, not all amputees experience referred sensations. Nonetheless, after a structured training period, it is possible to learn to associate touch with specific fingers, and the effect persisted after two weeks. This effect was evaluated on participants with intact limbs, so it remains to evaluate this effect for amputees.In conclusion, this thesis proposes suggestions on sensory feedback systems that could be helpful in future prosthetic hands to (1) reduce their complexity and (2) enhance the sense of body ownership to enhance the overall sense of embodiment as an addition to an intuitive control system

    Human Inspired Multi-Modal Robot Touch

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    Robotics 2010

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    Without a doubt, robotics has made an incredible progress over the last decades. The vision of developing, designing and creating technical systems that help humans to achieve hard and complex tasks, has intelligently led to an incredible variety of solutions. There are barely technical fields that could exhibit more interdisciplinary interconnections like robotics. This fact is generated by highly complex challenges imposed by robotic systems, especially the requirement on intelligent and autonomous operation. This book tries to give an insight into the evolutionary process that takes place in robotics. It provides articles covering a wide range of this exciting area. The progress of technical challenges and concepts may illuminate the relationship between developments that seem to be completely different at first sight. The robotics remains an exciting scientific and engineering field. The community looks optimistically ahead and also looks forward for the future challenges and new development

    Intelligent Haptic Perception for Physical Robot Interaction

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    Doctorado en Ingeniería mecatrónica. Fecha de entrega de la Tesis doctoral: 8 de enero de 2020. Fecha de lectura de Tesis doctoral: 30 de marzo 2020.The dream of having robots living among us is coming true thanks to the recent advances in Artificial Intelligence (AI). The gap that still exists between that dream and reality will be filled by scientific research, but manifold challenges are yet to be addressed. Handling the complexity and uncertainty of real-world scenarios is still the major challenge in robotics nowadays. In this respect, novel AI methods are giving the robots the capability to learn from experience and therefore to cope with real-life situations. Moreover, we live in a physical world in which physical interactions are both vital and natural. Thus, those robots that are being developed to live among humans must perform tasks that require physical interactions. Haptic perception, conceived as the idea of feeling and processing tactile and kinesthetic sensations, is essential for making this physical interaction possible. This research is inspired by the dream of having robots among us, and therefore, addresses the challenge of developing robots with haptic perception capabilities that can operate in real-world scenarios. This PhD thesis tackles the problems related to physical robot interaction by employing machine learning techniques. Three AI solutions are proposed for different physical robot interaction challenges: i) Grasping and manipulation of humans’ limbs; ii) Tactile object recognition; iii) Control of Variable-Stiffness-Link (VSL) manipulators. The ideas behind this research work have potential robotic applications such as search and rescue, healthcare or rehabilitation. This dissertation consists of a compendium of publications comprising as the main body a compilation of previously published scientific articles. The baseline of this research is composed of a total of five papers published in prestigious peer-reviewed scientific journals and international robotics conferences

    Haptics: Science, Technology, Applications

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    This open access book constitutes the proceedings of the 12th International Conference on Human Haptic Sensing and Touch Enabled Computer Applications, EuroHaptics 2020, held in Leiden, The Netherlands, in September 2020. The 60 papers presented in this volume were carefully reviewed and selected from 111 submissions. The were organized in topical sections on haptic science, haptic technology, and haptic applications. This year's focus is on accessibility

    Performance of modified jatropha oil in combination with hexagonal boron nitride particles as a bio-based lubricant for green machining

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    This study evaluates the machining performance of newly developed modified jatropha oils (MJO1, MJO3 and MJO5), both with and without hexagonal boron nitride (hBN) particles (ranging between 0.05 and 0.5 wt%) during turning of AISI 1045 using minimum quantity lubrication (MQL). The experimental results indicated that, viscosity improved with the increase in MJOs molar ratio and hBN concentration. Excellent tribological behaviours is found to correlated with a better machining performance were achieved by MJO5a with 0.05 wt%. The MJO5a sample showed the lowest values of cutting force, cutting temperature and surface roughness, with a prolonged tool life and less tool wear, qualifying itself to be a potential alternative to the synthetic ester, with regard to the environmental concern

    Tactile Sensing for Assistive Robotics

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