In this work, we discuss the design of soft robotic fingers for robust precision grasping. Through a conceptual analysis of the finger shape and compliance during grasping, we confirm that antipodal grasps are more stable when contact with the object occurs on the side of the fingers (i.e., pinch grasps) instead of the fingertips. In addition, we show that achieving such pinch grasps with soft fingers for a wide variety of objects requires at least two independent bending segments each, but only requires actuation in the proximal segment. Using a physical prototype hand, we evaluate the improvement in pinch-grasping performance of this two-segment proximally actuated finger design compared to more typical, uniformly actuated fingers. Through an exploration of the relative lengths of the two finger segments, we show the tradeoff between power grasping strength and precision grasping capabilities for fingers with passive distal segments. We characterize grasping on the basis of the acquisition region, object sizes, rotational stability, and robustness to external forces. Based on these metrics, we confirm that higher-quality precision grasping is achieved through pinch grasping via fingers with the proximally actuated finger design compared to uniformly actuated fingers. However, power grasping is still best performed with uniformly actuated fingers. Accordingly, soft continuum fingers should be designed to have at least two independently actuated serial segments, since such fingers can maximize grasping performance during both power and precision grasps through controlled adaptation between uniform and proximally actuated finger structures.
Development and Evaluation of a Passive Multiloop Wearable Hand Device for Natural Motion
Abstract This article describes the development and evaluation of our passively actuated closed-loop articulated wearable (CLAW) that uses a common slider to passively drive its exo-fingers for use in physical training of people with limited hand mobility. Our design approach utilizes physiological tasks for dimensional synthesis and yields a variety of design candidates that fulfill the desired fingertip precision grasping trajectory. Once it is ensured that the synthesized fingertip motion is close to the physiological fingertip grasping trajectories, performance assessment criteria related to user–device interference and natural joint angle movement are taken into account. After the most preferred design for each finger is chosen, minor modifications are made related to substituting the backbone chain with the wearer’s limb to provide the skeletal structure for the customized passive device. Subsequently, we evaluate it for natural joint motion based on a novel design candidate assessment method. A hand prototype is printed, and its preliminary performance regarding natural joint motion, wearability, and scalability are assessed. The pilot experimental test on a range of healthy subjects with different hand/finger sizes shows that the CLAW hand is easy to operate and guides the user’s fingers without causing any discomfort. It also ensures both precision and power grasping in a natural manner. This study establishes the importance of incorporating novel design candidate assessment techniques, based on human finger kinematic models, on a conceptual design level that can assist in finding design candidates for natural joint motion coordination.
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- Award ID(s):
- 1751770
- NSF-PAR ID:
- 10434019
- Date Published:
- Journal Name:
- Journal of Mechanisms and Robotics
- Volume:
- 15
- Issue:
- 1
- ISSN:
- 1942-4302
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Stable precision grips using the fingertips are a cornerstone of human hand dexterity. However, our fingers become unstable sometimes and snap into a hyperextended posture. This is because multilink mechanisms like our fingers can buckle under tip forces. Suppressing this instability is crucial for hand dexterity, but how the neuromuscular system does so is unknown. Here we show that people rely on the stiffness from muscle contraction for finger stability. We measured buckling time constants of 50 ms or less during maximal force application with the index finger—quicker than feedback latencies—which suggests that muscle-induced stiffness may underlie stability. However, a biomechanical model of the finger predicts that muscle-induced stiffness cannot stabilize at maximal force unless we add springs to stiffen the joints or people reduce their force to enable cocontraction. We tested this prediction in 38 volunteers. Upon adding stiffness, maximal force increased by 34 ± 3%, and muscle electromyography readings were 21 ± 3% higher for the finger flexors (mean ± SE). Muscle recordings and mathematical modeling show that adding stiffness offloads the demand for muscle cocontraction, thus freeing up muscle capacity for fingertip force. Hence, people refrain from applying truly maximal force unless an external stabilizing stiffness allows their muscles to apply higher force without losing stability. But more stiffness is not always better. Stiff fingers would affect the ability to adapt passively to complex object geometries and precisely regulate force. Thus, our results show how hand function arises from neurally tuned muscle stiffness that balances finger stability with compliance.more » « less
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Objective: Robust neural decoding of intended motor output is crucial to enable intuitive control of assistive devices, such as robotic hands, to perform daily tasks. Few existing neural decoders can predict kinetic and kinematic variables simultaneously. The current study developed a continuous neural decoding approach that can concurrently predict fingertip forces and joint angles of multiple fingers. Methods: We obtained motoneuron firing activities by decomposing high-density electromyogram (HD EMG) signals of the extrinsic finger muscles. The identified motoneurons were first grouped and then refined specific to each finger (index or middle) and task (finger force and dynamic movement) combination. The refined motoneuron groups (separate matrix) were then applied directly to new EMG data in real-time involving both finger force and dynamic movement tasks produced by both fingers. EMG-amplitude-based prediction was also performed as a comparison. Results: We found that the newly developed decoding approach outperformed the EMG-amplitude method for both finger force and joint angle estimations with a lower prediction error (Force: 3.47±0.43 vs 6.64±0.69% MVC, Joint Angle: 5.40±0.50° vs 12.8±0.65°) and a higher correlation (Force: 0.75±0.02 vs 0.66±0.05, Joint Angle: 0.94±0.01 vs 0.5±0.05) between the estimated and recorded motor output. The performance was also consistent for both fingers. Conclusion: The developed neural decoding algorithm allowed us to accurately and concurrently predict finger forces and joint angles of multiple fingers in real-time. Significance: Our approach can enable intuitive interactions with assistive robotic hands, and allow the performance of dexterous hand skills involving both force control tasks and dynamic movement control tasks.more » « less
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This paper explores a novel approach to dexterous manipulation, aimed at levels of speed, precision, robustness, and simplicity suitable for practical deployment. The enabling technology is a Direct-drive Hand (DDHand) comprising two fingers, two DOFs each, that exhibit high speed and a light touch. The test application is the dexterous manipulation of three small and irregular parts, moving them to a grasp suitable for a subsequent assembly operation, regardless of initial presentation. We employed four primitive behaviors that use ground contact as a “third finger”, prior to or during the grasp process: pushing, pivoting, toppling, and squeeze- grasping. In our experiments, each part was presented from 30 to 90 times randomly positioned in each stable pose. Success rates varied from 83% to 100%. The time to manipulate and grasp was 6.32 seconds on average, varying from 2.07 to 16 seconds. In some cases, performance was robust, precise, and fast enough for practical applications, but in other cases, pose uncertainty required time-consuming vision and arm motions. The paper concludes with a discussion of further improvements required to make the primitives robust, eliminate uncertainty, and reduce this dependence on vision and arm motion.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1115/1.4054168
