Bent sheet grasping stability for sheet manipulation

[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. © 2016 IEEE

Bent Sheet Grasping Stability for Sheet Manipulation

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

En aquesta tesi proposem un nou sistema per localitzar d'objectes amb sensors tàctils per a robòtica de manipulació, que tracta, de forma explícita, la incertesa inherent al sentit del tacte. Amb aquesta fi, estimem la completa distribució de probabilitat de la posició de l'objecte. A més a més, donat el model 3D de l'objecte en qüestió, el nostre sistema no requereix una exploració prèvia de l'objecte amb el sensor, podent localizar-lo des del primer contacte. Donat un senyal provinent del sensor tàctil, dividim l'estimació de la distribució de probabilitat de la posició 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 esperaríem rebre del sensor si l'objecte fos tocat en aquestes posicions, i entrenem una funció de semblança entre aquests senyals. Segon, mentre l'objecte està sent manipulat, comparem el senyal provinent del sensor amb els senyals simulats prèviament, i les semblances entre aquests donen la distribució de probabilitat discreta a l'espai de posicions de l'objecte respecte al sensor. Estenem aquesta feina analitzant l'escenari on múltiples sensors tàctils toquen l'objecte a la vegada. Fusionem les distribucions de probabilitat provinents dels diferents sensors per obtenir una distribució millor. Presentem resultats quantitatius per quatre objectes. També mostrem una aplicació d'aquest sistema en un sistema més gran i presentem recerca en la qual estem treballant actualment en percepció activa.En esta tesis proponemos un nuevos sistema para localizar objetos con sensores táctiles para robótica de manipulación, que trata, de forma explícita, la incertidumbre inherente al sentido del tacto. Con este fin, estimamos la completa distribución de probabilidad de la posición del objeto. Además, dado el modelo 3D del objeto que cuestión, nuestro sistema no requiere una exploración previa del objeto con el sensor, pudiendo localizarlo desde el primer contacto. Dada una señal proveniente del sensor táctil, dividimos la estimación de la distribución de probabilidad de la posición del objeto en dos pasos. Primero, antes de tocar el objeto, definimos un conjunto denso de posiciones del objeto respecto al sensor, simulamos la señal que esperaríamos recibir del sensor si el objeto fuese tocado en estas posiciones, y entrenamos una función de semejanza entre estas señales. Segundo, mientras el objeto está siendo manipulado, comparamos la señal proveniente del sensor con las señales simuladas previamente, y las semejanzas entre estas dan la distribución de probabilidad discreta en el espacio de posiciones del objeto respecto al sensor. Extendemos este trabajo analizando el escenario donde múltiples sensores táctiles tocan el objeto al mismo tiempo. Fusionamos las distribuciones de probabilidad que vienen de los diferentes sensores para obtener una distribución mejor. Presentamos resultados cuantitativos para cuatro objetos. También mostramos una aplicación de este sistema en un sistema más grande y presentamos investigación en la que estamos trabajando actualmente en percepción 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

Perception-driven optimal motion planning under resource constraints

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