929 research outputs found
A Robust Controller for Stable 3D Pinching using Tactile Sensing
This paper proposes a controller for stable grasping of unknown-shaped
objects by two robotic fingers with tactile fingertips. The grasp is stabilised
by rolling the fingertips on the contact surface and applying a desired
grasping force to reach an equilibrium state. The validation is both in
simulation and on a fully-actuated robot hand (the Shadow Modular Grasper)
fitted with custom-built optical tactile sensors (based on the BRL TacTip). The
controller requires the orientations of the contact surfaces, which are
estimated by regressing a deep convolutional neural network over the tactile
images. Overall, the grasp system is demonstrated to achieve stable equilibrium
poses on various objects ranging in shape and softness, with the system being
robust to perturbations and measurement errors. This approach also has promise
to extend beyond grasping to stable in-hand object manipulation with multiple
fingers.Comment: 8 pages, 10 figures, 1 appendix. Accepted for publication in IEEE
Robotics and Automation Letters and in IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS 2021). Supplemental video:
https://youtu.be/rfQesw3FDA
Design of a cybernetic hand for perception and action
Strong motivation for developing new prosthetic hand devices is provided by the fact that low functionality and controllability—in addition to poor cosmetic appearance—are the most important reasons why amputees do not regularly use their prosthetic hands. This paper presents the design of the CyberHand, a cybernetic anthropomorphic hand intended to provide amputees with functional hand replacement. Its design was bio-inspired in terms of its modular architecture, its physical appearance, kinematics, sensorization, and actuation, and its multilevel control system. Its underactuated mechanisms allow separate control of each digit as well as thumb–finger opposition and, accordingly, can generate a multitude of grasps. Its sensory system was designed to provide proprioceptive information as well as to emulate fundamental functional properties of human tactile mechanoreceptors of specific importance for grasp-and-hold tasks. The CyberHand control system presumes just a few efferent and afferent channels and was divided in two main layers: a high-level control that interprets the user’s intention (grasp selection and required force level) and can provide pertinent sensory feedback and a low-level control responsible for actuating specific grasps and applying the desired total force by taking advantage of the intelligent mechanics. The grasps made available by the high-level controller include those fundamental for activities of daily living: cylindrical, spherical, tridigital (tripod), and lateral grasps. The modular and flexible design of the CyberHand makes it suitable for incremental development of sensorization, interfacing, and control strategies and, as such, it will be a useful tool not only for clinical research but also for addressing neuroscientific hypotheses regarding sensorimotor control
Performance of modified jatropha oil in combination with hexagonal boron nitride particles as a bio-based lubricant for green machining
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
Cable Manipulation with a Tactile-Reactive Gripper
Cables are complex, high dimensional, and dynamic objects. Standard
approaches to manipulate them often rely on conservative strategies that
involve long series of very slow and incremental deformations, or various
mechanical fixtures such as clamps, pins or rings. We are interested in
manipulating freely moving cables, in real time, with a pair of robotic
grippers, and with no added mechanical constraints. The main contribution of
this paper is a perception and control framework that moves in that direction,
and uses real-time tactile feedback to accomplish the task of following a
dangling cable. The approach relies on a vision-based tactile sensor, GelSight,
that estimates the pose of the cable in the grip, and the friction forces
during cable sliding. We achieve the behavior by combining two tactile-based
controllers: 1) Cable grip controller, where a PD controller combined with a
leaky integrator regulates the gripping force to maintain the frictional
sliding forces close to a suitable value; and 2) Cable pose controller, where
an LQR controller based on a learned linear model of the cable sliding dynamics
keeps the cable centered and aligned on the fingertips to prevent the cable
from falling from the grip. This behavior is possible by a reactive gripper
fitted with GelSight-based high-resolution tactile sensors. The robot can
follow one meter of cable in random configurations within 2-3 hand regrasps,
adapting to cables of different materials and thicknesses. We demonstrate a
robot grasping a headphone cable, sliding the fingers to the jack connector,
and inserting it. To the best of our knowledge, this is the first
implementation of real-time cable following without the aid of mechanical
fixtures.Comment: Accepted to RSS 202
Soft Fingertips with Tactile Sensing and Active Deformation for Robust Grasping of Delicate Objects
Soft fingertips have shown significant adaptability for grasping a wide range of object shapes thanks to elasticity. This ability can be enhanced to grasp soft, delicate objects by adding touch sensing. However, in these cases, the complete restraint and robustness of the grasps have proved to be challenging, as the exertion of additional forces on the fragile object can result in damage. This paper presents a novel soft fingertip design for delicate objects based on the concept of embedded air cavities, which allow the dual ability of adaptive sensing and active shape changing. The pressurized air cavities act as soft tactile sensors to control gripper position from internal pressure variation; and active fingertip deformation is achieved by applying positive pressure to these cavities, which then enable a delicate object to be kept securely in position, despite externally applied forces, by form closure. We demonstrate this improved grasping capability by comparing the displacement of grasped delicate objects exposed to high-speed motions. Results show that passive soft fingertips fail to restrain fragile objects at accelerations as low as 0.1m/s2 , in contrast, with the proposed fingertips, delicate objects are completely secure even at accelerations of more than 5m/s2
Proceedings of the first workshop on Peripheral Machine Interfaces: going beyond traditional surface electromyography
abstract: One of the hottest topics in rehabilitation robotics is that of proper control of prosthetic devices. Despite decades of research, the state of the art is dramatically behind the expectations. To shed light on this issue, in June, 2013 the first international workshop on Present and future of non-invasive peripheral nervous system (PNS)–Machine Interfaces (MI; PMI) was convened, hosted by the International Conference on Rehabilitation Robotics. The keyword PMI has been selected to denote human–machine interfaces targeted at the limb-deficient, mainly upper-limb amputees, dealing with signals gathered from the PNS in a non-invasive way, that is, from the surface of the residuum. The workshop was intended to provide an overview of the state of the art and future perspectives of such interfaces; this paper represents is a collection of opinions expressed by each and every researcher/group involved in it
TIAGo RL: Simulated Reinforcement Learning Environments with Tactile Data for Mobile Robots
Tactile information is important for robust performance in robotic tasks that
involve physical interaction, such as object manipulation. However, with more
data included in the reasoning and control process, modeling behavior becomes
increasingly difficult. Deep Reinforcement Learning (DRL) produced promising
results for learning complex behavior in various domains, including
tactile-based manipulation in robotics. In this work, we present our
open-source reinforcement learning environments for the TIAGo service robot.
They produce tactile sensor measurements that resemble those of a real
sensorised gripper for TIAGo, encouraging research in transfer learning of DRL
policies. Lastly, we show preliminary training results of a learned force
control policy and compare it to a classical PI controller
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