6 research outputs found

    Bent sheet grasping stability for sheet manipulation

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    [email protected] this study, we focused on sheet manipulation with robotic hands. This manipulation involves grasping the sides of the sheet and utilizing the convex area resulting from bending the sheet. This sheet manipulation requires the development of a model of a bent sheet grasped with fingertips. We investigated the relationship between the grasping force and bending of the sheet and developed a bent sheet model. We also performed experiments on the sheet grasping stability with a focus on the resistible force, which is defined as the maximum external force at which a fingertip can maintain contact when applying an external force. The main findings and contributions are as follows. 1) After the sheet buckles, the grasping force only increases slightly even if the fingertip pressure is increased. 2) The range of the applicable grasping forces depends on the stiffness of the fingertips. Stiffer fingertips cannot provide a small grasping force but can resist large external forces. Softer fingertips can provide a small grasping force but cannot resist large external forces. 3) A grasping strategy for sheet manipulation is presented that is based on controlling the stiffness of the fingertips. 漏 2016 IEEE

    Bent Sheet Grasping Stability for Sheet Manipulation

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    In this study, we focused on sheet manipulation with robotic hands. This manipulation involves grasping the sides of the sheet and utilizing the convex area resulting from bending the sheet. This sheet manipulation requires the development of a model of a bent sheet grasped with fingertips. We investigated the relationship between the grasping force and bending of the sheet and developed a bent sheet model. We also performed experiments on the sheet grasping stability with a focus on the resistible force, which is defined as the maximum external force at which a fingertip can maintain contact when applying an external force. The main findings and contributions are as follows. 1) After the sheet buckles, the grasping force only increases slightly even if the fingertip pressure is increased. 2) The range of the applicable grasping forces depends on the stiffness of the fingertips. Stiffer fingertips cannot provide a small grasping force but can resist large external forces. Softer fingertips can provide a small grasping force but cannot resist large external forces. 3) A grasping strategy for sheet manipulation is presented that is based on controlling the stiffness of the fingertips

    Tactile localization: dealing with uncertainty from the first touch

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    En aquesta tesi proposem un nou sistema per localitzar d'objectes amb sensors t脿ctils per a rob貌tica de manipulaci贸, que tracta, de forma expl铆cita, la incertesa inherent al sentit del tacte. Amb aquesta fi, estimem la completa distribuci贸 de probabilitat de la posici贸 de l'objecte. A m茅s a m茅s, donat el model 3D de l'objecte en q眉esti贸, el nostre sistema no requereix una exploraci贸 pr猫via de l'objecte amb el sensor, podent localizar-lo des del primer contacte. Donat un senyal provinent del sensor t脿ctil, dividim l'estimaci贸 de la distribuci贸 de probabilitat de la posici贸 de l'objecte en dos passos. Primer, abans de tocar l'objecte, definim un conjunt dens de posicions de l'objecte respecte al sensor, simulem el senyal que esperar铆em rebre del sensor si l'objecte fos tocat en aquestes posicions, i entrenem una funci贸 de semblan莽a entre aquests senyals. Segon, mentre l'objecte est脿 sent manipulat, comparem el senyal provinent del sensor amb els senyals simulats pr猫viament, i les semblances entre aquests donen la distribuci贸 de probabilitat discreta a l'espai de posicions de l'objecte respecte al sensor. Estenem aquesta feina analitzant l'escenari on m煤ltiples sensors t脿ctils toquen l'objecte a la vegada. Fusionem les distribucions de probabilitat provinents dels diferents sensors per obtenir una distribuci贸 millor. Presentem resultats quantitatius per quatre objectes. Tamb茅 mostrem una aplicaci贸 d'aquest sistema en un sistema m茅s gran i presentem recerca en la qual estem treballant actualment en percepci贸 activa.En esta tesis proponemos un nuevos sistema para localizar objetos con sensores t谩ctiles para rob贸tica de manipulaci贸n, que trata, de forma expl铆cita, la incertidumbre inherente al sentido del tacto. Con este fin, estimamos la completa distribuci贸n de probabilidad de la posici贸n del objeto. Adem谩s, dado el modelo 3D del objeto que cuesti贸n, nuestro sistema no requiere una exploraci贸n previa del objeto con el sensor, pudiendo localizarlo desde el primer contacto. Dada una se帽al proveniente del sensor t谩ctil, dividimos la estimaci贸n de la distribuci贸n de probabilidad de la posici贸n del objeto en dos pasos. Primero, antes de tocar el objeto, definimos un conjunto denso de posiciones del objeto respecto al sensor, simulamos la se帽al que esperar铆amos recibir del sensor si el objeto fuese tocado en estas posiciones, y entrenamos una funci贸n de semejanza entre estas se帽ales. Segundo, mientras el objeto est谩 siendo manipulado, comparamos la se帽al proveniente del sensor con las se帽ales simuladas previamente, y las semejanzas entre estas dan la distribuci贸n de probabilidad discreta en el espacio de posiciones del objeto respecto al sensor. Extendemos este trabajo analizando el escenario donde m煤ltiples sensores t谩ctiles tocan el objeto al mismo tiempo. Fusionamos las distribuciones de probabilidad que vienen de los diferentes sensores para obtener una distribuci贸n mejor. Presentamos resultados cuantitativos para cuatro objetos. Tambi茅n mostramos una aplicaci贸n de este sistema en un sistema m谩s grande y presentamos investigaci贸n en la que estamos trabajando actualmente en percepci贸n activa.In this thesis we present an approach to object tactile localization for robotic manipulation which explicitly deals with the uncertainty to overcome the locality of tactile sensing. To that purpose, we estimate full probability distributions of object pose. Moreover, given a 3D model of the object in question, our framework localizes from the first touch, meaning no physical exploration of the object is needed beforehand. Given a signal from the tactile sensor, we divide the estimation of a probability distribution of object pose in two main steps. First, before touching the object, we sample a dense set of poses of the object with respect to the sensor, we simulate the signal the sensor would get when touching the object at these poses, and we train a similarity function between these signals. In the second part, while manipulating the object, we compare the signal coming from the sensor to the set of previously simulated ones, and the similarities between these give the discretized probability distribution over the possible poses of the object with respect to the sensor. We extend this work by analyzing the scenario where multiple tactile sensors are touching the object at the same time, by fusing the probability distributions coming from the individual sensors to get a better distribution. We present quantitative results for four objects. We also present the application of this approach in a larger system and an ongoing research direction towards tactile active perception.Outgoin

    Bent Sheet Grasping Stability for Sheet Manipulation

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    Unmanned aerial vehicle implementation in renewable energy applications

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    Perception-driven optimal motion planning under resource constraints

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    Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Applied Ocean Science & Engineering at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution February 2019.Over the past few years there has been a new wave of interest in fully autonomous robots operating in the real world, with applications from autonomous driving to search and rescue. These robots are expected to operate at high speeds in unknown, unstructured environments using only onboard sensing and computation, presenting significant challenges for high performance autonomous navigation. To enable research in these challenging scenarios, the first part of this thesis focuses on the development of a custom high-performance research UAV capable of high speed autonomous flight using only vision and inertial sensors. This research platform was used to develop stateof-the-art onboard visual inertial state estimation at high speeds in challenging scenarios such as flying through window gaps. While this platform is capable of high performance state estimation and control, its capabilities in unknown environments are severely limited by the computational costs of running traditional vision-based mapping and motion planning algorithms on an embedded platform. Motivated by these challenges, the second part of this thesis presents an algorithmic approach to the problem of motion planning in an unknown environment when the computational costs of mapping all available sensor data is prohibitively high. The algorithm is built around a tree of dynamically feasible and free space optimal trajectories to the goal state in configuration space. As the algorithm progresses it iteratively switches between processing new sensor data and locally updating the search tree. We show that the algorithm produces globally optimal motion plans, matching the optimal solution for the case with the full (unprocessed) sensor data, while only processing a subset of the data. The mapping and motion planning algorithm is demonstrated on a number of test systems, with a particular focus on a six-dimensional thrust limited model of a quadrotor
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