1,720,983 research outputs found
Efficient Bayesian Exploration for Soft Morphology-Action Co-optimization
No full textMorphology been shown to be a fundamental aspect of tactile sensing in soft robotics, one that can aid, and indeed enable, complex discrimination tasks. For a robot to change its sensor morphology as well as control appropriately, the parametric search over morphology and control parameters is usually slow and unsuited for real-world applications. We develop a framework based on Bayesian Exploration, to allow a robot to co-optimize both changes in tactile sensing morphology and robot action control, to aid in complex tactile object discrimination tasks. We test the framework by performing object discrimination on a set of eight objects, varying three different physical properties: geometry, surface texture, and stiffness. We integrate a capacitive tactile sensor into a flat end-effector and create three soft silicon-based filters with varying morphological properties. We incorporate the end-effector onto a robotic arm and perform repetitive, parameterized touch experiments, on each object. We show morphing is indeed necessary to dissociate amongst different object properties with the sensor at hand. Moreover, we show the proposed framework can consistently achieve optimal morphology-action configurations in approximately half the time than systematic search over parameters. This work marks a step towards the creation of robots capable of using morphology and action control to actively aid in discrimination tasks
Soft morphological processing of tactile stimuli for autonomous category formation
No full textSensor morphology is a fundamental aspect of tactile sensing technology. Design choices induce stimuli to be morphologically processed, changing the sensory perception of the touched objects and affecting inference at a later processing stage. We develop a framework to analyze the filtered sensor response and observe the correspondent change in tactile information. We test the morphological processing effects on the tactile stimuli by integrating a capacitive tactile sensor into a flat end-effector and creating three soft silicon-based filters with varying thickness (3mm, 6mm and 10mm). We incorporate the end-effector onto a robotic arm. We control the arm in order to apply a calibrated force onto 4 objects, and retrieve tactile images. We create an unsupervised inference process through the use of Principal Component Analysis and K-Means Clustering. We use the process to group the sensed objects into 2 classes and observe how different soft filters affect the clustering results. The sensor response with the 3mm soft filter allows for edges to be the feature with most variance (captured by PC A) and induces the association of edged objects. With thicker soft filters the associations change, and with a 10mm filter the sensor response results more diverse for objects with different elongation. We show that the clustering is intrinsically driven by the morphology of the sensor and that the robot's world understanding changes according to it. © 2018 IEEE
Model-Free Soft-Structure Reconstruction for Proprioception Using Tactile Arrays
No full textContinuum body structures provide unique opportunities for soft robotics, with the infinite degrees of freedom enabling unconstrained and highly adaptive exploration and manipulation. However, the infinite degrees of freedom of continuum bodies makes sensing (both intrinsically and extrinsically) challenging. To address this, in this letter, we propose a model-free method for sensorizing tentacle-like continuum soft-structures using an array of spatially arranged capacitive tactile sensors. By using visual tracking, the relationship between the tactile response and the three-dimensional shape of the continuum soft-structure can be learned. A dataset of 15 000 random soft-body postures was used, with recorded camera-tracked positions logged synchronously to the tactile sensor responses. This was used to train a neural network that can predict posture. We show that it is possible to achieve proprioceptive awareness over all three axis of motion in space, reconstructing the body structure and inferring the soft body head's pose with an average accuracy of approximate to 1 mm in comparison to the visual tracked counterpart. To demonstrate the capabilities of the system, we perform random exploration of environments limiting the work-space of the sensorized robot. We find the method capable to autonomously reconstruct the reachable morphology of the environment without the need of external sensing units
Structuring of tactile sensory information for category formation in robotics palpation
No full textThis paper proposes a framework to investigate the influence of physical interactions to sensory information, during robotic palpation. We embed a capacitive tactile sensor on a robotic arm to probe a soft phantom and detect and classify hard inclusions within it. A combination of PCA and K-Means clustering is used to: first, reduce the dimensionality of the spatiotemporal data obtained through the probing of each area in the phantom; second categorize the re-encoded data into a given number of categories. Results show that appropriate probing interactions can be useful in compensating for the quality of the data, or lack thereof. Finally, we test the proposed framework on a palpation scenario where a Support Vector Machine classifier is trained to discriminate amongst different types of hard inclusions. We show the proposed framework is capable of predicting the best-performing motion strategy, as well as the relative classification performance of the SVM classifier, solely based on unsupervised cluster analysis methods
Achieving Robotically Peeled Lettuce
No full textRobotic technologies are being increasingly applied to agriculture, in particular to harvesting. Some types of produce such as iceberg lettuce require additional processing after harvesting in order to satisfy the needs of the end-user or customer. Lettuce must have its outer leaves removed, a task that is currently performed manually. The leaf removal task represents a challenging vision and manipulation problem: the lettuce is in a random pose on a flat surface, from which the outermost leaves must be removed quickly and without causing damage. This letter presents a novel vision pipeline and suction removal system that enables robotic lettuce leaf removal. A suction nozzle and control procedure are used for the removal itself, relying on the orientation estimation and stem detection provided by the vision pipeline. To the best of the author's knowledge, this is the first robotic lettuce leaf peeling system
Human-robot medical interaction
No full textAdvances in Soft Robotics, Haptics, AI and simulation have changed the medical robotics field, allowing robotics technologies to be deployed in medical environments. In this context, the relationship between doctors, robotics devices, and patients is fundamental, as only with the synergetic collaboration of the three parties results in medical robotics can be achieved. This workshop focuses on the use of soft robotics technologies, sensing, AI and Simulation, to further improve medical practitioner training, as well as the creation of new tools for diagnosis and healthcare through the medical interaction of humans and robots. The Robo-patient is more specifically the idea behind the creation of sensorised robotic patient with controllable organs to present a given set of physiological conditions. This is both to investigate the embodied nature of haptic interaction in physical examination, as well as the doctor-patient relationship to further improve medical practice through robotics technologies. The Robo-doctor aspect is also relevant, with robotics prototypes performing, or helping to perform, medical diagnosis. In the workshop, key technologies, as well as future views in the field, will be discussed both by expert and new upcoming researcher
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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