Open-source, affordable, modular, light-weight, underactuated robot hands

Zisimatos, Agisilaos G.; Liarokapis, Minas; Mavrogiannis, Christoforos; Kyriakopoulos, Kostas J.

doi:10.1109/iros.2014.6943007

Cited by 55 publications

(28 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lower-performing motors of similar size (e.g., Pololu micrometal gearmotors) (4,5) are substantially lower in cost (~$15) but require choosing between applying sufficient force or speed to the prosthetic. Examples of prosthetic hands that use low-cost motors are Brunel Hand (~$1500; Open Bionics Inc.) (6) and open-source initiatives (~$200) from OPENBIONICS (7)(8)(9). The combination of speed and strength of these powered hands is limited due to the use of less costly motors, as well as the materials from which they are made [i.e., acrylic, thermoplastic urethane (TPU), and polylactic acid (PLA)].…”

Section: Introductionmentioning

confidence: 99%

Elastomeric passive transmission for autonomous force-velocity adaptation applied to 3D-printed prosthetics

O’Brien

Levine

et al. 2018

Sci. Robot.

View full text Add to dashboard Cite

The force, speed, dexterity, and compact size required of prosthetic hands present extreme design challenges for engineers. Current prosthetics rely on high-quality motors to achieve adequate precision, force, and speed in a small enough form factor with the trade-off of high cost. We present a simple, compact, and cost-effective continuously variable transmission produced via projection stereolithography. Our transmission, which we call an elastomeric passive transmission (EPT), is a polyurethane composite cylinder that autonomously adjusts its radius based on the tension in a wire spooled around it. We integrated six of these EPTs into a three-dimensionally printed soft prosthetic hand with six active degrees of freedom. Our EPTs provided the prosthetic hand with about three times increase in grip force without compromising flexion speed. This increased performance leads to finger closing speeds of ~0.5 seconds (average radial velocity, ~180 degrees second−1) and maximum fingertip forces of ~32 newtons per finger.

show abstract

Section: Introductionmentioning

confidence: 99%

Elastomeric passive transmission for autonomous force-velocity adaptation applied to 3D-printed prosthetics

O’Brien

Levine

et al. 2018

Sci. Robot.

View full text Add to dashboard Cite

show abstract

“…Adaptive hands, such as those used in the works mentioned above, have become an exciting topic of research over the last decade, with recent development in several areas, such as new designs [20,21], reliable grasping [12,22], and dexterous in-hand manipulation [23,24]. Due to its unique ability to grasp and adapt to unknown objects, special attention has been devoted to the research of underactuated hands in unstructured environments.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Reasonable dexterity was achieved by having opposed fingers with individual actuators and tactile feedback provided by its force sensors. Even though little space for customization is observed in the solution as mentioned above, Zisimatos et al [20] designed a single actuated robotic gripper that supported up to six modular fingers pulled by a differential disk. Later, we redesigned the base and finger to enable dual actuation for opposed fingers which, in conjunction to modified phalanges, made mounted multimodal tactile sensing modules possible with similar dexterity.…”

Section: Literature Reviewmentioning

confidence: 99%

Estimating the Orientation of Objects from Tactile Sensing Data Using Machine Learning Methods and Visual Frames of Reference

Fonseca

Oliveira

Petriu

2019

Sensors

View full text Add to dashboard Cite

Underactuated hands are useful tools for robotic in-hand manipulation tasks due to their capability to seamlessly adapt to unknown objects. To enable robots using such hands to achieve and maintain stable grasping conditions even under external disturbances while keeping track of an in-hand object’s state requires learning object-tactile sensing data relationships. The human somatosensory system combines visual and tactile sensing information in their “What and Where” subsystem to achieve high levels of manipulation skills. The present paper proposes an approach for estimating the pose of in-hand objects combining tactile sensing data and visual frames of reference like the human “What and Where” subsystem. The system proposed here uses machine learning methods to estimate the orientation of in-hand objects from the data gathered by tactile sensors mounted on the phalanges of underactuated fingers. While tactile sensing provides local information about objects during in-hand manipulation, a vision system generates egocentric and allocentric frames of reference. A dual fuzzy logic controller was developed to achieve and sustain stable grasping conditions autonomously while forces were applied to in-hand objects to expose the system to different object configurations. Two sets of experiments were used to explore the system capabilities. On the first set, external forces changed the orientation of objects while the fuzzy controller kept objects in-hand for tactile and visual data collection for five machine learning estimators. Among these estimators, the ridge regressor achieved an average mean squared error of 0.077 ∘ . On the second set of experiments, one of the underactuated fingers performed open-loop object rotations and data recorded were supplied to the same set of estimators. In this scenario, the Multilayer perceptron (MLP) neural network achieved the lowest mean squared error of 0.067 ∘ .

show abstract

“…There are multiple research and commercial hands that have been developed over the past several years that are multi-joint, articulated hands. Of the hands that were chosen for comparison, the Dextrus Hand [57], InMoov Hand [58], Modular Hand [59], the iCub Hand [60], Athens Hand [61], Alaris Hand [62], and Brussels Hand [63] are open source hands (many of these hands also utilize additive manufacturing) that have recently been presented or described. The specifications for these hands as well as the Bebionic [8], iLimb [9], Michelangelo [37], SmartHand [17], Vanderbilt [13], and Vincent Hands [12] are listed in Table 3.…”

Section: Fabrication Of Hand Prototypementioning

confidence: 99%

Design and Fabrication of a Six Degree-of-Freedom Open Source Hand

Krausz

Rorrer

Weir

2016

IEEE Trans. Neural Syst. Rehabil. Eng.

View full text Add to dashboard Cite

Currently, most externally powered prostheses are controlled using electromyography (or EMG), which is the measure of the electrical signals that are produced when voluntary muscle is contracted. One of the major problems is that there are a limited number of muscular control sites that can be used, which limits the complexity of the hands that are controllable. Many upper-limb prosthetics researchers are searching for methods to simply and effectively control complex prosthetic hands, and a significant number of these researchers have utilized virtual hands and other simulations to perform testing of these control algorithms (oftentimes on able bodied subjects). Thus, these control techniques remain firmly planted in the virtual realm, and the authors postulate that the development of a physical hand would help to validate results obtained through use of virtual hands and could help establish whether or not a given control scheme is realistically applicable for use by amputees. The development of such a hand would be beneficial for researchers in the field. A six degree-of-freedom hand was developed with such a purpose in mind, and two of the major goals of the project were that the hand be inexpensive and open source. The hand design is being shared on .

show abstract

Open-source, affordable, modular, light-weight, underactuated robot hands

Cited by 55 publications

References 22 publications

Elastomeric passive transmission for autonomous force-velocity adaptation applied to 3D-printed prosthetics

Elastomeric passive transmission for autonomous force-velocity adaptation applied to 3D-printed prosthetics

Estimating the Orientation of Objects from Tactile Sensing Data Using Machine Learning Methods and Visual Frames of Reference

Design and Fabrication of a Six Degree-of-Freedom Open Source Hand

Contact Info

Product

Resources

About