PUT-Hand—Hybrid Industrial and Biomimetic Gripper for Elastic Object Manipulation

Mańkowski, Tomasz; Tomczyński, Jakub; Walas, Krzysztof; Belter, Dominik

doi:10.3390/electronics9071147

Cited by 14 publications

(10 citation statements)

References 59 publications

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“…A leaner approach is demonstrated in [13]. The optical sensor OptoForce OMD-10 [14], now discontinued as a standalone commercial component, is used.…”

Section: B Technologies For Tactile Sensingmentioning

confidence: 99%

A Gradiometric Magnetic Force Sensor Immune to Stray Magnetic Fields for Robotic Hands and Grippers

Signor¹,

Dupré²,

Close³

2022

IEEE Robot. Autom. Lett.

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To enable precise manipulation of fragile objects, robots need the sense of touch. Localized sensing of the 3D force vector with a few mN of resolution is desired. Magnetic force sensors, consisting of a magnet embedded in an elastomer and a 3D magnetometer, were demonstrated as desirable candidates. These prior-art sensors measure the local 3D magnetic field at a single sensing pixel. Hence, they cannot distinguish between the signal and stray magnetic fields. Any stray field directly leaks into the force readout signal path. This paper introduces a design immune to stray magnetic fields. The sensor uses multiple magnetic pixels, and operates on the gradient of the magnetic field. The pixels and conditioning electronics are fully integrated on chip. The 3D force calculation is based on a polynomial model fitted to a calibration data set. The impact of a 2-mT magnetic stray field on the force output is limited to 0.3% of the full scale-about two orders magnitude improvement over the prior art. The force resolution is 2.7 mN and remains competitive. Furthermore, an on-chip temperature sensor and an algorithm are used to compensate the intrinsic thermal drift in the range 0-50°C. To validate the proper force regulation in spite of a nearby magnet, we integrated our prototype into a robotic hand. Our results demonstrate the robustness of 3D magnetic force sensors in the presence of real-world parasitic disturbances.

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“…A leaner approach is demonstrated in [13]. The optical sensor OptoForce OMD-10 [14], now discontinued as a standalone commercial component, is used.…”

Section: B Technologies For Tactile Sensingmentioning

confidence: 99%

A Gradiometric Magnetic Force Sensor Immune to Stray Magnetic Fields for Robotic Hands and Grippers

Signor¹,

Dupré²,

Close³

2022

IEEE Robot. Autom. Lett.

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“…To achieve grasps available only to the fully-actuated mechanisms, authors of [33] proposed an underactuated gripper with electrostatic brakes in joints, which enable to carry heavy objects by reducing power consumption and motor torque during a steady grasp. Nowadays, hybrid approaches, which combines fully actuated fingers for precise manipulation and underactuated ones for power grasp and compliance, are becoming popular [34,35]. A different hybrid approach is presented in [36], where authors proposed an underactuated gripper with a suction cup for picking up various objects in different working environments.…”

Section: Underactuated and Soft Grippersmentioning

confidence: 99%

“…Interested readers may refer to recent more comprehensive underactuated and soft grippers reviews such as [34,38,45].…”

Section: Underactuated and Soft Grippersmentioning

confidence: 99%

Gaining a Sense of Touch Object Stiffness Estimation Using a Soft Gripper and Neural Networks

et al. 2021

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Soft grippers are gaining significant attention in the manipulation of elastic objects, where it is required to handle soft and unstructured objects, which are vulnerable to deformations. The crucial problem is to estimate the physical parameters of a squeezed object to adjust the manipulation procedure, which poses a significant challenge. The research on physical parameters estimation using deep learning algorithms on measurements from direct interaction with objects using robotic grippers is scarce. In our work, we proposed a trainable system which performs the regression of an object stiffness coefficient from the signals registered during the interaction of the gripper with the object. First, using the physics simulation environment, we performed extensive experiments to validate our approach. Afterwards, we prepared a system that works in a real-world scenario with real data. Our learned system can reliably estimate the stiffness of an object, using the Yale OpenHand soft gripper, based on readings from Inertial Measurement Units (IMUs) attached to the fingers of the gripper. Additionally, during the experiments, we prepared three datasets of IMU readings gathered while squeezing the objects—two created in the simulation environment and one composed of real data. The dataset is the contribution to the community providing the way for developing and validating new approaches in the growing field of soft manipulation.

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“…As all the moving parts are submerged in lubrication fluid with a latex skin, the finger mechanism has high efficiency and control performance. Mańkowski, Tomasz, et al [12] developed the PUT-Hand, the five-finger anthropomorphic gripper intended for elastic object manipulation. PUT-Hand is a hybrid design, taking advantage of both fully actuated and under-actuated designs.…”

Section: Introductionmentioning

confidence: 99%

Design and Experiment of an Anthropomorphic Robot Hand for Variable Grasping Stiffness

et al. 2021

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This paper introduces an anthropomorphic robot hand that has variable grasping stiffness to unknown objects without damage. The robot hand has four fingers, 13 DoF (Degree of Freedom) with three SEA (Series Elastic Actuator) modules; in the index, middle, and ring finger. The performance for broader grasping stiffness using variable impedance control based on the parameter update block and the procedure of grasping capability evaluations of the robot hand are explained. The robot finger modules are constructed with a thumb, index, middle, and ring finger to complete the anthropomorphic type robot hand. The robot finger module is has 3 degrees of freedom and functions as one actuator based on an under-actuated mechanism. And external force measurement is possible in the fingertip force sensor and SEA (Series Elastic Actuator) module. For the variable impedance control implementation, a parameter update block is added to adjust the desired impedance parameter. To implement the parameter update block, we define and evaluate the correlation equation between the fingertip forceshore hardnessdamping ratio.INDEX TERMS anthropomorphic robot hand, modularized robot finger, multi-finger robot hand, variable grasping stiffness, impedance control, series elastic actuator module.

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PUT-Hand—Hybrid Industrial and Biomimetic Gripper for Elastic Object Manipulation

Cited by 14 publications

References 59 publications

A Gradiometric Magnetic Force Sensor Immune to Stray Magnetic Fields for Robotic Hands and Grippers

A Gradiometric Magnetic Force Sensor Immune to Stray Magnetic Fields for Robotic Hands and Grippers

Gaining a Sense of Touch Object Stiffness Estimation Using a Soft Gripper and Neural Networks

Design and Experiment of an Anthropomorphic Robot Hand for Variable Grasping Stiffness

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