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Keywords = soft-contact joint

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15 pages, 4348 KiB  
Article
Impact of Structural Compliance of a Six Degree of Freedom Joint Simulator on Virtual Ligament Force Calculation in Total Knee Endoprosthesis Testing
by Eric Kleist, Paul Henke, Leo Ruehrmund, Maeruan Kebbach, Rainer Bader and Christoph Woernle
Life 2024, 14(4), 531; https://doi.org/10.3390/life14040531 - 21 Apr 2024
Viewed by 1580
Abstract
The AMTI VIVO™ six degree of freedom joint simulator allows reproducible preclinical testing of joint endoprostheses under specific kinematic and loading conditions. When testing total knee endoprosthesis, the articulating femoral and tibial components are each mounted on an actuator with two and four [...] Read more.
The AMTI VIVO™ six degree of freedom joint simulator allows reproducible preclinical testing of joint endoprostheses under specific kinematic and loading conditions. When testing total knee endoprosthesis, the articulating femoral and tibial components are each mounted on an actuator with two and four degrees of freedom, respectively. To approximate realistic physiological conditions with respect to soft tissues, the joint simulator features an integrated virtual ligament model that calculates the restoring forces of the ligament apparatus to be applied by the actuators. During joint motion, the locations of the ligament insertion points are calculated depending on both actuators’ coordinates. In the present study, we demonstrate that unintended elastic deformations of the actuators due to the specifically high contact forces in the artificial knee joint have a considerable impact on the calculated ligament forces. This study aims to investigate the effect of this structural compliance on experimental results. While the built-in algorithm for calculating the ligament forces cannot be altered by the user, a reduction of the ligament force deviations due to the elastic deformations could be achieved by preloading the articulating implant components in the reference configuration. As a proof of concept, a knee flexion motion with varying ligament conditions was simulated on the VIVO simulator and compared to data derived from a musculoskeletal multibody model of a total knee endoprosthesis. Full article
(This article belongs to the Special Issue Advances in Knee Biomechanics)
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Figure 1

Figure 1
<p>Flowchart of paper’s structure.</p>
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<p>VIVO joint simulator with mounted femoral and tibial knee implants and schematic depiction of DOF. Upper actuator with two rotational DOFs for flexion/extension and adduction/abduction; lower actuator providing omnidirectional translations and internal/external rotation.</p>
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<p>Test setup for examination of structural compliance of the VIVO at the reference configuration with 0° rotation of the flexion arm. Tests were also conducted at 30°, 60°, and 90° flexion angles.</p>
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<p>Results of structural compliance examination—vertical displacements of the lower actuator <span class="html-italic">s</span><sub>1</sub> over the vertical force <span class="html-italic">F</span> for different flexion arm rotations.</p>
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<p>(<b>a</b>) Vertical displacements Δ<span class="html-italic">s</span><sub>1</sub> and Δ<span class="html-italic">s</span><sub>2</sub> of VIVO actuators and virtual ligament insertion points under vertical load. The elastic displacement Δ<span class="html-italic">s</span><sub>2</sub> is not captured by the VIVO’s sensors and is instead assumed zero for ligament force calculation. (<b>b</b>) Decreasing ligament force of the exemplary ligament under increasing vertical force. Supposing a perfectly rigid upper actuator, neither actuator would move vertically (Δ<span class="html-italic">s</span><sub>1</sub> = Δ<span class="html-italic">s</span><sub>2</sub> = 0) and the curve would be constant at the starting value of 268 N.</p>
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<p>Functionality of embedding templates. Shown parts are merged with epoxy resin: (<b>a</b>) femoral component: holder 1, embedding template 2, implant 3; (<b>b</b>) tibial component: tibia insert 4, embedding template 5, holder 6. Holders 1 and 6 feature coupling interfaces matching the VIVO’s actuators.</p>
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<p>Setting of reference configuration and definition of virtual ligaments: (<b>a</b>) The relative position between implants obtained in the MBS is set kinematically; (<b>b</b>) a given initial vertical force is applied, causing both actuators to move up vertically by Δ<span class="html-italic">s</span>. The resulting configuration is defined as reference configuration. (<b>c</b>) The virtual ligament insertion points are defined in the reference configuration. Virtual ligaments are visualized as springs. (<b>d</b>) Passive flexion motion is executed, during which the resulting force of all individual virtual ligament forces <span class="html-italic">F</span><sub>lig</sub> is applied by the lower actuator.</p>
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<p>Musculoskeletal multibody model: (<b>a</b>) open kinematic chain with bones and implants shown at different flexion angles. (<b>b</b>) Detailed view of the knee joint model (posterior–lateral view) with considered ligament bundles (MCL not visible, implants hidden for clarity). These ligaments were also considered in the VIVO experiments.</p>
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<p>Results of passive flexion test on the VIVO in comparison to MBS simulation: (<b>a</b>) axial contact force on tibia; (<b>b</b>) tibia internal/external rotation; (<b>c</b>) femoral AP displacement. Anatomical directions are indicated by arrows on the vertical axis.</p>
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<p>Results for different PCL stiffnesses during passive flexion. (<b>a1</b>–<b>a3</b>): VIVO experiment; (<b>b1</b>–<b>b3</b>): MBS simulation; (<b>a1</b>,<b>b1</b>) axial contact force on tibia; (<b>a2</b>,<b>b2</b>) detail view of (<b>a1</b>,<b>b1</b>) at high flexion angles; (<b>a3</b>,<b>b3</b>) femoral AP displacement.</p>
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21 pages, 6396 KiB  
Article
The Influence of Explosive and Rock Mass Properties on Blast Damage in a Single-Hole Blasting
by Magreth S. Dotto and Yashar Pourrahimian
Mining 2024, 4(1), 168-188; https://doi.org/10.3390/mining4010011 - 20 Mar 2024
Cited by 5 | Viewed by 4544
Abstract
In rock blasting for mining production, stress waves play a major role in rock fracturing, along with explosive gases. Better energy distribution improves fragmentation and safety, lowers production costs, increases productivity, and controls ore losses and dilution. Blast outcomes vary significantly depending on [...] Read more.
In rock blasting for mining production, stress waves play a major role in rock fracturing, along with explosive gases. Better energy distribution improves fragmentation and safety, lowers production costs, increases productivity, and controls ore losses and dilution. Blast outcomes vary significantly depending on the choice of the explosive and the properties of the rock mass encountered. This study analyzes the effects of rock mass and explosive properties on blast outcomes via numerical simulation using data from the case study, and later validates the simulation results from the field blast fragmentation. The findings suggest that, for a given set of rock properties, the choice of explosive has a major influence on the resulting fragmentation. Strong explosives (high VOD and detonation pressure) favor large fracture extents in hard rocks, while weaker explosives offer a better distribution of explosive energy and fractures. The presence of rock structures such as rock contacts and joints influences the propagation of stress waves and fractures depending on the structures’ material properties, the intensity and orientations, and the direction and strength of the stress wave. When the stress wave encounters a contact depending on its direction, it is enhanced when traveling from soft to hard and attenuates in the opposite direction. The ability of the stress wave to cause fracturing on the opposite side of the contact depends on the intensity of the transmitted wave and the strength of the rock. Transmitted wave intensity is a function of the strength of the incident wave and the impedance difference between the interface materials. The presence of joints in the rock mass affects the propagation of the stress wave, mainly depending on the infill material properties and the angle at which the stress wave approaches the joint. Less compressible, higher stiffness joints transmit more energy. More energy is also transmitted in the areas where the stress wave hits the joint perpendicularly. Joints parallel to the free face offer additional fracturing on the opposite side of the joint. Other parameters, such as the joint width, continuity, fracture frequency, and the distance from the charge, enhance the effects. To achieve effective fragmentation, the blast design should mitigate the effect of variability in the rock mass via explosive selection and pattern design to ensure adequate energy distribution within the limits of geometric design. Full article
(This article belongs to the Special Issue Women’s Special Issue Series: Mining)
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Figure 1

Figure 1
<p>Stress limit surfaces and loading scenario [<a href="#B23-mining-04-00011" class="html-bibr">23</a>].</p>
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<p>Single blasthole pattern.</p>
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<p>Peak pressure and PPV profiles.</p>
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<p>Damage distribution in variable explosive and rock properties.</p>
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<p>General models for contact simulation.</p>
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<p>Pressure across the contacts.</p>
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<p>Pressure across the contacts.</p>
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<p>Damage across the contacts.</p>
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<p>Damage distribution on various joint infill material.</p>
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<p>Joint width and persistence influence on fracture distribution.</p>
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<p>Damage distribution at various distances from the charge.</p>
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<p>Damage distribution at various orientations.</p>
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<p>Effect of multiple joints and their orientations on fracture distribution.</p>
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<p>Analysis summary.</p>
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<p>Major structures mapping and locations for fragmentation analysis.</p>
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<p>Fragmentation along burden in various monitoring locations.</p>
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14 pages, 4920 KiB  
Article
Biomimetic Design of a Tendon-Driven Myoelectric Soft Hand Exoskeleton for Upper-Limb Rehabilitation
by Rodrigo C. Silva, Bruno. G. Lourenço, Pedro H. F. Ulhoa, Eduardo A. F. Dias, Fransergio L. da Cunha, Cristiane P. Tonetto, Luis G. Villani, Claysson B. S. Vimieiro, Guilherme A. Lepski, Marina Monjardim and Rafhael M. Andrade
Biomimetics 2023, 8(3), 317; https://doi.org/10.3390/biomimetics8030317 - 19 Jul 2023
Cited by 8 | Viewed by 3387
Abstract
Degenerative diseases and injuries that compromise hand movement reduce individual autonomy and tend to cause financial and psychological problems to their family nucleus. To mitigate these limitations, over the past decade, hand exoskeletons have been designed to rehabilitate or enhance impaired hand movements. [...] Read more.
Degenerative diseases and injuries that compromise hand movement reduce individual autonomy and tend to cause financial and psychological problems to their family nucleus. To mitigate these limitations, over the past decade, hand exoskeletons have been designed to rehabilitate or enhance impaired hand movements. Although promising, these devices still have limitations, such as weight and cost. Moreover, the movements performed are not kinematically compatible with the joints, thereby reducing the achievements of the rehabilitation process. This article presents the biomimetic design of a soft hand exoskeleton actuated using artificial tendons designed to achieve low weight, volume, and cost, and to improve kinematic compatibility with the joints, comfort, and the sensitivity of the hand by allowing direct contact between the hand palm and objects. We employed two twisted string actuators and Bowden cables to move the artificial tendons and perform the grasping and opening of the hand. With this configuration, the heavy part of the system was reallocated to a test bench, allowing for a lightweight set of just 232 g attached to the arm. The system was triggered by the myoelectric signals of the biceps captured from the user’s skin to encourage the active participation of the user in the process. The device was evaluated by five healthy subjects who were asked to simulate a paralyzed hand, and manipulate different types of objects and perform grip strength. The results showed that the system was able to identify the intention of movement of the user with an accuracy of 90%, and the orthosis was able to enhance the ability of handling objects with gripping force up to 1.86 kgf. Full article
(This article belongs to the Special Issue Bionic Technology – Robotic Exoskeletons and Prostheses)
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Figure 1

Figure 1
<p>Bioinspired artificial tendons. (<b>a</b>) In green, the extensor tendon (extensor tendons) and the flexion tendons in purple and blue (digitorum profundus and digitorum superficialis, respectively). The contraction of these tendons induces angular movement of the phalanges around the DIP (distal phalanx joint), PIP (proximal phalanx joint), and MCP (metacarpal phalanx joint) articulation points. (<b>b</b>) Independent motions of the finger performed by each artificial tendon. (A) Angular movement around MCP joint; (B) finger flexion; (C) finger extension. (<b>c</b>) The index finger tendon scheme shows the distribution of the artificial extensor/flexor tendons, their fixations, and connections. This pattern is also used on the other fingers.</p>
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<p>Bioinspired artificial tendons. (<b>a</b>) In green, the extensor tendon (extensor tendons) and the flexion tendons in purple and blue (digitorum profundus and digitorum superficialis, respectively). The contraction of these tendons induces angular movement of the phalanges around the DIP (distal phalanx joint), PIP (proximal phalanx joint), and MCP (metacarpal phalanx joint) articulation points. (<b>b</b>) Independent motions of the finger performed by each artificial tendon. (A) Angular movement around MCP joint; (B) finger flexion; (C) finger extension. (<b>c</b>) The index finger tendon scheme shows the distribution of the artificial extensor/flexor tendons, their fixations, and connections. This pattern is also used on the other fingers.</p>
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<p>Digital prototype of the Soft Hand Exoskeleton.</p>
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<p>Kinematics of artificial tendons. In purple, on the back of the finger, is the tendon responsible for finger extension. The green tendon retracts the finger, while the blue one rotates the finger around the MCP (metacarpal) joint. The combined action of the blue and green tendons promotes proper flexion movement. <math display="inline"> <semantics> <mrow> <msub> <mrow> <mi>θ</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> <mo>,</mo> <msub> <mrow> <mi>θ</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> <mo>,</mo> <mo> </mo> <mi>a</mi> <mi>n</mi> <mi>d</mi> <mo> </mo> <msub> <mrow> <mi>θ</mi> </mrow> <mrow> <mn>3</mn> </mrow> </msub> </mrow> </semantics> </math> are the MCP, proximal interphalangeal (PIP), and distal interphalangeal DIP joint angles, respectively. <math display="inline"> <semantics> <mrow> <mi mathvariant="script">l</mi> </mrow> </semantics> </math> is the curvature radius reference. <math display="inline"> <semantics> <mrow> <msub> <mrow> <mi>P</mi> </mrow> <mrow> <mi>k</mi> </mrow> </msub> <mo>∈</mo> <mfenced open="[" close="]" separators="|"> <mrow> <mn>1</mn> <mo>,</mo> <mn>8</mn> </mrow> </mfenced> </mrow> </semantics> </math> are the wire anchor points.</p>
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<p>Kinematics analysis. (<b>a</b>) Angular variation: <math display="inline"> <semantics> <mrow> <msub> <mrow> <mi>θ</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </mrow> </semantics> </math> distal angle; <math display="inline"> <semantics> <mrow> <msub> <mrow> <mi>θ</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics> </math> medial angle; <math display="inline"> <semantics> <mrow> <msub> <mrow> <mi>θ</mi> </mrow> <mrow> <mn>3</mn> </mrow> </msub> </mrow> </semantics> </math> metacarpal angle. (<b>b</b>) Wire length variation: <math display="inline"> <semantics> <mrow> <msubsup> <mrow> <mi>w</mi> </mrow> <mrow> <mi>e</mi> <mi>a</mi> <mi>t</mi> </mrow> <mrow> <mi>P</mi> </mrow> </msubsup> </mrow> </semantics> </math> extension length variation; <math display="inline"> <semantics> <mrow> <msubsup> <mrow> <mi>w</mi> </mrow> <mrow> <mi>p</mi> <mi>a</mi> <mi>t</mi> </mrow> <mrow> <mi>P</mi> </mrow> </msubsup> </mrow> </semantics> </math> palmar length variation (rotates the finger around the MCP joint); <math display="inline"> <semantics> <mrow> <msubsup> <mrow> <mi>w</mi> </mrow> <mrow> <mi>l</mi> <mi>a</mi> <mi>t</mi> </mrow> <mrow> <mi>P</mi> </mrow> </msubsup> </mrow> </semantics> </math> lateral length variation (contracts the finger).</p>
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<p>The physical prototype and experimental evaluation. (<b>a</b>) (1) DC motors. (2) Twisted string actuator cables. (3) Screen to prevent the actuator cable from tangling. (4) Actuator–conduit cable connection. (5) Bowden cables. (6) Thermoplastic orthosis. (7) Artificial tendons. (8) Addressing screen for artificial tendons. (9) Finger rings. (10) Electronics composed by Arduino Uno, and H-bridge circuit to drive the motors; (11) myoelectric sensor. (<b>b</b>) Grip strength test. (<b>c</b>) Handling a cell phone. (<b>d</b>) Handling a screwdriver. (<b>e</b>) Handling a bottle of alcohol gel.</p>
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<p>Twisted string actuator. The DC motor drives the string twisting zone, producing linear displacement of the cable.</p>
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<p>Twisted string actuation optimized parameters.</p>
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<p>Hand orthosis operational system flowchart for the experimental protocol.</p>
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<p>The accuracy obtained for training and test sections.</p>
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<p>Object manipulation test. The colored symbols represent the precision score for each subject. The black circle is the average score across subjects and the error bar is the standard deviation.</p>
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<p>Grip strength test.</p>
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17 pages, 8773 KiB  
Article
Study on Collision Dynamics Model and Multi-Body Contact Forces of Ball Cage Flexible Joint Considering Clearance
by Xiuxing Zhu, Fei Zhao, Xiaowei Yang, Yingpeng Xu, Peng Jia, Bo Zhou and Shifeng Xue
Machines 2023, 11(4), 466; https://doi.org/10.3390/machines11040466 - 8 Apr 2023
Viewed by 2318
Abstract
Flexible joints are widely used in ‘soft’ touching and holding, and they represent the main component of ultra-short radius drilling tools. The analysis of contact and motion characteristics is an essential issue in the design and development stage of flexible joints. In this [...] Read more.
Flexible joints are widely used in ‘soft’ touching and holding, and they represent the main component of ultra-short radius drilling tools. The analysis of contact and motion characteristics is an essential issue in the design and development stage of flexible joints. In this study, a collision dynamics model of a ball cage flexible joint (BCFJ), which is suitable for the characteristics of small clearance and large load, is established. The model contains a nonlinear stiffness coefficient and can describe the contact force between the ball key and the raceways. Moreover, the computational procedure for the dynamic analysis of BCFJ with clearance is established, and the dynamic simulation for collision and contact between ball, cage, outer race, and inner race was carried out. By numerical calculation, the variation of contact force on the five contact points of the ball key and ball cage is discussed, and the influence of ball cage clearance on contact force between ball key, ball seat, and ball cage is obtained. The results indicate that the effects of the ball cage clearance on the contact force cannot be ignored, which is the main cause for the vibration of the flexible joint system, and the amplitude of the contact force will gradually increase with the increase of the clearance. The proposed model and procedure can analyze the dynamic behavior of flexible joints with small clearance and large load, providing a basis for further research on wear prediction and safety evaluation of the BCFJ with clearance. Full article
(This article belongs to the Section Machine Design and Theory)
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Figure 1

Figure 1
<p>The structure components of BCFJ.</p>
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<p>The influence of clearance <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>R</mi> </mrow> </semantics></math> and elastic deformation <span class="html-italic">δ</span> on the normal contact force <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>F</mi> </mrow> <mrow> <mi>N</mi> </mrow> </msub> </mrow> </semantics></math>.</p>
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<p>The influence of recovery coefficient <math display="inline"><semantics> <mrow> <msub> <mi>C</mi> <mi>r</mi> </msub> </mrow> </semantics></math> and elastic deformation <span class="html-italic">δ</span> on the normal contact force <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>F</mi> </mrow> <mrow> <mi>N</mi> </mrow> </msub> </mrow> </semantics></math>.</p>
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<p>The evolution of tangential friction coefficient with relative velocity.</p>
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<p>Position view of contact point on flexible joint (five points).</p>
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<p>Flowchart of computational procedure for dynamic analysis of BCFJ with clearance.</p>
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<p>Geometric parameters of BCFJ (mm).</p>
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<p>The variation of contact force between outer race and cage.</p>
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<p>The variation of contact force between inner race and cage.</p>
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<p>The variation of contact force between cage and ball.</p>
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<p>The variation of contact force between outer race and ball keys.</p>
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<p>The variation of contact force between inner race and ball keys.</p>
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<p>The influence of clearance on contact force between cage and ball seat. (<b>a</b>) The normal contact force (1.18 s–1.82 s); (<b>b</b>) The axial contact force (1.52 s–2.75 s).</p>
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<p>The influence of clearance on contact force between ball keys and ball seat (0.79 s–1.43 s). (<b>a</b>) Contact force of ball key 1; (<b>b</b>) Contact force of ball key 2; (<b>c</b>) Contact force of ball key 3; (<b>d</b>) Contact force of ball key 4; (<b>e</b>) Contact force of ball key 5; (<b>f</b>) Contact force of ball key 6.</p>
Full article ">Figure 14 Cont.
<p>The influence of clearance on contact force between ball keys and ball seat (0.79 s–1.43 s). (<b>a</b>) Contact force of ball key 1; (<b>b</b>) Contact force of ball key 2; (<b>c</b>) Contact force of ball key 3; (<b>d</b>) Contact force of ball key 4; (<b>e</b>) Contact force of ball key 5; (<b>f</b>) Contact force of ball key 6.</p>
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17 pages, 18565 KiB  
Article
Tendon-Driven Gripper with Variable Stiffness Joint and Water-Cooled SMA Springs
by Phuoc Thien Do, Quang Ngoc Le, Quoc Viet Luong, Hyun-Ho Kim, Hyeong-Mo Park and Yeong-Jin Kim
Actuators 2023, 12(4), 160; https://doi.org/10.3390/act12040160 - 4 Apr 2023
Cited by 15 | Viewed by 4572
Abstract
In recent years, there has been an increase in the development of medical robots to enhance interventional MRI-guided therapies and operations. Magnetic resonance imaging (MRI) surgical robots are particularly attractive due to their ability to provide excellent soft-tissue contrast during these procedures. This [...] Read more.
In recent years, there has been an increase in the development of medical robots to enhance interventional MRI-guided therapies and operations. Magnetic resonance imaging (MRI) surgical robots are particularly attractive due to their ability to provide excellent soft-tissue contrast during these procedures. This paper describes a novel design for a tendon-driven gripper that utilizes four shape memory alloy (SMA) spring actuators and variable stiffness joints controlled by SMA coils for use in MRI surgical robot applications. The contact force of the gripper link is determined by the mechanical properties of the SMA spring actuators (SSA) and the angle of each linkage, and the joint stiffness can be adjusted by varying the electrical current applied to the SMA coil. To enhance the efficiency of the SSAs, a new cooling system using water has been proposed and implemented. To validate the effectiveness of our proposed gripper, we conducted three types of experiments, namely, a single SSA experiment, a single SMA coil experiment, and a whole gripper experiment. The experimental results demonstrate that the proposed water-cooling system can effectively solve temperature issues of SMA, and the joint stiffness in the austenite state is higher than that in the martensite state. Moreover, our experiments show that the presented gripper is capable of grasping and holding objects of various shapes and weights. Full article
(This article belongs to the Section Actuators for Robotics)
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Figure 1
<p>(<b>a</b>) 3D design schematic of the gripper (front view); (<b>b</b>) 3D isometric projection of the gripper; (<b>c</b>) SSA 1 at martensite state and SSA 2 at austenite state; (<b>d</b>) SSA 1 at austenite state and SSA 2 at martensite state.</p>
Full article ">Figure 1 Cont.
<p>(<b>a</b>) 3D design schematic of the gripper (front view); (<b>b</b>) 3D isometric projection of the gripper; (<b>c</b>) SSA 1 at martensite state and SSA 2 at austenite state; (<b>d</b>) SSA 1 at austenite state and SSA 2 at martensite state.</p>
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<p>Structure of the silicone SSAs with mechanical connections and electrical connections.</p>
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<p>(<b>a</b>) Front projection of the links of Finger 1; (<b>b</b>) section view of the robot jaw; (<b>c</b>) SMA coil at austenite state; (<b>d</b>) prototype of the gripper jaw.</p>
Full article ">Figure 3 Cont.
<p>(<b>a</b>) Front projection of the links of Finger 1; (<b>b</b>) section view of the robot jaw; (<b>c</b>) SMA coil at austenite state; (<b>d</b>) prototype of the gripper jaw.</p>
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<p>Free body diagram of a gripper jaw.</p>
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<p>(<b>a</b>) Experimental setup of force measurement of single SSA; (<b>b</b>) schematic diagram of the experiment setup.</p>
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<p>(<b>a</b>) Experimental setup of force measurement of single SSA; (<b>b</b>) schematic diagram of the experiment setup.</p>
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<p>The block diagram of the temperature control system with K<sub>P</sub> = 40, K<sub>I</sub> = 1, and K<sub>D</sub> = 0.</p>
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<p>The temperature decreasing time between the natural cooling and the water cooling of the SMA spring in experiments in <a href="#actuators-12-00160-f005" class="html-fig">Figure 5</a>.</p>
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<p>The relationship between force and temperature relation.</p>
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<p>(<b>a</b>) Experimental setup for finger force measurement; (<b>b</b>) experimental result of the contact force.</p>
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<p>(<b>a</b>) Experimental setup for finger force measurement; (<b>b</b>) experimental result of the contact force.</p>
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<p>The finger when applying a load onto the edge: (<b>a</b>) Experimental setup; (<b>b</b>) experimental result of the variable stiffness with the joint in martensite phase and austenite state.</p>
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<p>The experiment setup for grasping tests: (<b>a</b>) real photo of the experiment; (<b>b</b>) schematic diagram of the experiment.</p>
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<p>The experiment setup for grasping tests: (<b>a</b>) real photo of the experiment; (<b>b</b>) schematic diagram of the experiment.</p>
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<p>Overview of the gripper grasping objects: (<b>a</b>) The resting state of the gripper; (<b>b</b>) the open state of the gripper; (<b>c</b>) the closed state of the gripper; (<b>d</b>) grasping of a scale weight 1; (<b>e</b>) grasping of a scale weight 2; (<b>f</b>) grasping of a scale weight 1; (<b>g</b>) grasping of a battery; (<b>h</b>) grasping of an egg; (<b>i</b>) grasping of a box.</p>
Full article ">Figure 12 Cont.
<p>Overview of the gripper grasping objects: (<b>a</b>) The resting state of the gripper; (<b>b</b>) the open state of the gripper; (<b>c</b>) the closed state of the gripper; (<b>d</b>) grasping of a scale weight 1; (<b>e</b>) grasping of a scale weight 2; (<b>f</b>) grasping of a scale weight 1; (<b>g</b>) grasping of a battery; (<b>h</b>) grasping of an egg; (<b>i</b>) grasping of a box.</p>
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<p>Control algorithm block diagram of the gripper.</p>
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24 pages, 49676 KiB  
Article
Soft-Sensor System for Grasp Type Recognition in Underactuated Hand Prostheses
by Laura De Arco, María José Pontes, Marcelo E. V. Segatto, Maxwell E. Monteiro, Carlos A. Cifuentes and Camilo A. R. Díaz
Sensors 2023, 23(7), 3364; https://doi.org/10.3390/s23073364 - 23 Mar 2023
Cited by 9 | Viewed by 2747
Abstract
This paper presents the development of an intelligent soft-sensor system to add haptic perception to the underactuated hand prosthesis PrHand. Two sensors based on optical fiber were constructed, one for finger joint angles and the other for fingertips’ contact force. Three sensor fabrications [...] Read more.
This paper presents the development of an intelligent soft-sensor system to add haptic perception to the underactuated hand prosthesis PrHand. Two sensors based on optical fiber were constructed, one for finger joint angles and the other for fingertips’ contact force. Three sensor fabrications were tested for the angle sensor by axially rotating the sensors in four positions. The configuration with the most similar response in the four rotations was chosen. The chosen sensors presented a polynomial response with R2 higher than 92%. The tactile force sensors tracked the force made over the objects. Almost all sensors presented a polynomial response with R2 higher than 94%. The system monitored the prosthesis activity by recognizing grasp types. Six machine learning algorithms were tested: linear regression, k-nearest neighbor, support vector machine, decision tree, k-means clustering, and hierarchical clustering. To validate the algorithms, a k-fold test was used with a k = 10, and the accuracy result for k-nearest neighbor was 98.5%, while that for decision tree was 93.3%, enabling the classification of the eight grip types. Full article
(This article belongs to the Section Sensors and Robotics)
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<p>Working principle: side-polished angle sensor.</p>
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<p>Working principle: jacket remotion with cladding and core axial polish angle sensor.</p>
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<p>Devices used in the sensor’s construction. (<b>a</b>) CNC. (<b>b</b>) 3D-printed structure and fiber cladding separated from its jacket. (<b>c</b>) 3D-printed structure to obtain the polish around the fiber.</p>
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<p>Test bench for the angle sensor and first position per fiber configuration. (<b>a</b>) Test bench CAD. (<b>b</b>) Fiber sensor with side polish. (<b>c</b>) Fiber sensor jacket remotion. (<b>d</b>) Fiber sensor jacket remotion with cladding and core axial polish.</p>
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<p>Working principle of contact force sensor.</p>
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<p>Construction of the contact force sensor. (<b>a</b>) 3D-printed mold. (<b>b</b>) Contact force sensor.</p>
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<p>Test bench for the contact force sensor. (<b>a</b>) CAD. (<b>b</b>) Real setup.</p>
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<p>Prosthesis with the soft-sensor integrated, holding one object for the protocol.</p>
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<p>Results of the three sensor configurations per rotation: the dotted blue line corresponds to 0°, the dotted and dashed orange line corresponds to 90°, the squared gray line corresponds to 180°, and the dashed yellow line corresponds to 270°. (<b>a</b>) Side polish fiber. (<b>b</b>) Jacket remotion. (<b>c</b>) Jacket remotion with cladding and core axial polish.</p>
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<p>Angle sensor characterization per finger: the dotted blue line represents the closing and the dashed orange line the opening. (<b>a</b>) Little. (<b>b</b>) Ring. (<b>c</b>) Middle. (<b>d</b>) Index. (<b>e</b>) Thumb.</p>
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<p>Contact force sensor characterization per finger: the dotted blue line represents compression, and the dashed orange line reflects decompression. (<b>a</b>) Little. (<b>b</b>) Ring. (<b>c</b>) Middle. (<b>d</b>) Index. (<b>e</b>) Thumb.</p>
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<p>Accuracy per type of grasp for the linear regression algorithm.</p>
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<p>Accuracy per type of grasp for the k-NN algorithm.</p>
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<p>Accuracy per grasp type for the support vector machine algorithm.</p>
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<p>Accuracy per type of grasp for the decision tree algorithm.</p>
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<p>Accuracy per type of grasp for the KMC algorithm.</p>
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<p>Accuracy per type of grasp for the hierarchical clustering algorithm.</p>
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<p>Principal Components Analysis (PCA).</p>
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16 pages, 3721 KiB  
Essay
Modeling and Analysis of a Reconfigurable Rover for Improved Traversing over Soft Sloped Terrains
by Shipeng Lyu, Wenyao Zhang, Chen Yao, Zheng Zhu and Zhenzhong Jia
Biomimetics 2023, 8(1), 131; https://doi.org/10.3390/biomimetics8010131 - 22 Mar 2023
Cited by 2 | Viewed by 2802
Abstract
Adjusting the roll angle of a rover’s body is a commonly used strategy to improve its traversability over sloped terrains. However, its range of adjustment is often limited, due to constraints imposed by the rover design and geometry factors such as suspension, chassis, [...] Read more.
Adjusting the roll angle of a rover’s body is a commonly used strategy to improve its traversability over sloped terrains. However, its range of adjustment is often limited, due to constraints imposed by the rover design and geometry factors such as suspension, chassis, size, and suspension travel. In order to improve the rover’s traversability under these constraints, this paper proposes a reconfigurable rover design with a two-level (sliding and rolling) mechanism to adjust the body’s roll angle. Specifically, the rolling mechanism is a bionic structure, akin to the human ankle joint which can change the contact pose between the wheel and the terrain. This novel adjustment mechanism can modulate the wheel–terrain contact pose, center-of-mass projection over the supporting polygon, wheel load, and the rover driving mode. Combining the wheel–load model and terramechanics-based wheel–terrain interaction model, we develop an integrated model to describe the system dynamics, especially the relationship between rover pose and wheel slippage parameters. Using this model, we develop an associated attitude control strategy to calculate the desired rover pose using particle swarm algorithm while considering the slip rate and angle constraints. We then adjust the sliding and rolling servo angles accordingly for slope traversing operations. To evaluate the proposed design and control strategies, we conduct extensive simulation and experimental studies. The results indicate that our proposed rover design and associated control strategy can double the maximum slope angles that the rover can negotiate, resulting in significantly improved traversability over soft sloped terrains. Full article
(This article belongs to the Special Issue Bio-Inspired Design and Control of Legged Robot)
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<p>Introduction to the soft terrain slope traversing task using our ROMA-Sloper rover as an example. This task can be divided into three parts: (1) rover state estimation and terrain perception, (2) rover attitude control parameter calculation, and (3) motion control execution. ROMA-Sloper has 4 wheels, 12 motors, 4 F/T sensors and an Intel T265 camera (with built-in visual odometry function) and an Intel D435i RGB-D camera for terrain perception.</p>
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<p>The ROMA-Sloper rover is reconfigurable, and it contains an active two-level adjustment mechanism: sliding-part and rolling-part. The detailed structures of the two parts are shown in the exploded view. Some coordinate systems are attached to this rover.</p>
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<p>Geometrical parameter definitions for slope traversing. Some important distance notices are marked in the simplified rover figure. The COM’s position and wheel load can be calculated by these notices.</p>
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<p>The relations between the rover roll angle and the COM, COB, and COW positions in <math display="inline"><semantics> <msup> <mi>Y</mi> <mi>W</mi> </msup> </semantics></math> direction when rover operates in different driving modes. The black triangle refers to the cross of green dash line (<math display="inline"><semantics> <mrow> <mi>C</mi> <mi>O</mi> <msub> <mi>W</mi> <mi>y</mi> </msub> </mrow> </semantics></math>) and red solid line <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>C</mi> <mi>O</mi> <msub> <mi>M</mi> <mi>y</mi> </msub> <mo>)</mo> </mrow> </semantics></math>; The red star refers to the cross of green dash line (<math display="inline"><semantics> <mrow> <mi>C</mi> <mi>O</mi> <msub> <mi>W</mi> <mi>y</mi> </msub> </mrow> </semantics></math>) and blue dotted line (<math display="inline"><semantics> <mrow> <mi>C</mi> <mi>O</mi> <msub> <mi>B</mi> <mi>y</mi> </msub> </mrow> </semantics></math>). For situation a, the condition is that the rover traverses <math display="inline"><semantics> <msup> <mn>10</mn> <mo>∘</mo> </msup> </semantics></math> slope in mode I, while the slope angle is <math display="inline"><semantics> <msup> <mn>20</mn> <mo>∘</mo> </msup> </semantics></math> for situation b. For situation c, the condition is that the rover traverses <math display="inline"><semantics> <msup> <mn>20</mn> <mo>∘</mo> </msup> </semantics></math> slope in mode II, while the slope angle is <math display="inline"><semantics> <msup> <mn>30</mn> <mo>∘</mo> </msup> </semantics></math> for situation b.</p>
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<p>Experimental and simulation results of rover slippages during <math display="inline"><semantics> <msup> <mn>10</mn> <mo>∘</mo> </msup> </semantics></math> and <math display="inline"><semantics> <msup> <mn>30</mn> <mo>∘</mo> </msup> </semantics></math> slope traverses. The wheel’s angular velocity is 20 rpm. Sub-figures (<b>a</b>,<b>b</b>) show the slip ratio and angle for the rover traversing on a <math display="inline"><semantics> <msup> <mn>10</mn> <mo>∘</mo> </msup> </semantics></math> slope in mode I, while the conditions of sub-figures (<b>c</b>,<b>d</b>) are the mode II and <math display="inline"><semantics> <msup> <mn>30</mn> <mo>∘</mo> </msup> </semantics></math> slope. We measure the slip parameters of the rover as it traverses the slope at different flow angles.</p>
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<p>The rover’s optimal configuration and roll angle under different slope angles. The left part (red line) corresponds to Mode I, and the right part (green line) corresponds to Mode II. The slope angle threshold <math display="inline"><semantics> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> <mo>(</mo> <msub> <mi>θ</mi> <mi>b</mi> </msub> <mo>)</mo> </mrow> </semantics></math> between Mode I and Mode II is about <math display="inline"><semantics> <msup> <mn>16</mn> <mo>∘</mo> </msup> </semantics></math>.</p>
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<p>The rover trajectory and force/torque data were collected in the traversing task on a <math display="inline"><semantics> <msup> <mn>20</mn> <mo>∘</mo> </msup> </semantics></math> slope. For sub-figure (<b>a</b>), the rover state is the mode I, While mode II is selected for the rover in sub-figure (<b>b</b>). The first column shows the experimental scenarios and the motion modes of ROMA-Sloper. The second column shows the trajectory of ROMA-Sloper after traversing the slope. The last two columns show the force and torque information of the wheel during the traversing process.</p>
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<p>The rover trajectory and force/torque data (expressed in wheel frame <math display="inline"><semantics> <msub> <mo>∑</mo> <mi>C</mi> </msub> </semantics></math>) for different optimization objectives in the fine adjustment process. The first column shows the experimental scenarios, and the wheel-terrain contact states are shown as sub-figures (<b>left</b>-<b>top</b>) in detail. The last three columns show the force and torque information of the wheel during the traversing process. In sub-figure (<b>a</b>), the rover’s wheels are parallel to the slope (contact pose optimization); In the sub-figure (<b>b</b>), the rover’s wheels are parallel to the direction of gravity (contact force optimization).</p>
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15 pages, 9808 KiB  
Article
Uneven Terrain Walking with Linear and Angular Momentum Allocation
by Zhicheng He, Songhao Piao, Xiaokun Leng and Yucong Wu
Sensors 2023, 23(4), 2027; https://doi.org/10.3390/s23042027 - 10 Feb 2023
Viewed by 2142
Abstract
Uneven terrain walking is hard to achieve for most child-size humanoid robots, as they are unable to accurately detect ground conditions. In order to reduce the demand for ground detection accuracy, a walking control framework based on centroidal momentum allocation is studied in [...] Read more.
Uneven terrain walking is hard to achieve for most child-size humanoid robots, as they are unable to accurately detect ground conditions. In order to reduce the demand for ground detection accuracy, a walking control framework based on centroidal momentum allocation is studied in this paper, enabling a child-size humanoid robot to walk on uneven terrain without using ground flatness information. The control framework consists of three controllers: momentum decreasing controller, posture controller, admittance controller. First, the momentum decreasing controller is used to quickly stabilize the robot after disturbance. Then, the posture controller restores the robot posture to adapt to the unknown terrain. Finally, the admittance controller aims to decrease contact impact and adapt the robot to the terrain. Note that the robot uses a mems-based inertial measurement unit (IMU) and joint position encoders to calculate centroidal momentum and use force-sensitive resistors (FSR) on the robot foot to perform admittance control. None of these is a high-cost component. Experiments are conducted to test the proposed framework, including standing posture balancing, structured non-flat ground walking, and soft uneven terrain walking, with a speed of 2.8 s per step, showing the effectiveness of the momentum allocation method. Full article
(This article belongs to the Special Issue Sensors and Artificial Intelligence)
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<p>External forces analysis. In the <math display="inline"><semantics> <mrow> <mi>x</mi> <mi>O</mi> <mi>z</mi> </mrow> </semantics></math> plane, the robot is subjected to gravity force, ground reaction force, and the inertial force of its own acceleration.</p>
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<p>Angular momentum compensation strategy. The left side is the original situation, and the robot has a large rotation trend. On the right is the case of generating backward acceleration, which reduces the rotation trend.</p>
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<p>Linear momentum compensation strategy. The left side is the original situation, and the robot has a larger trend of accelerating backward. On the right is the case of generating instantaneous clockwise rotation, which reduces the trend of accelerating backward.</p>
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<p>Illustration of the leg length compensation method.</p>
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<p>Picture of force sensitive resistor (FSR) sensors and a simplified force measuring schematic diagram.</p>
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<p>Overall control framework. In the single and double support phases, different controllers are enabled for trajectory control. The momentum decreasing controller and the admittance controller are enabled only in the double support phase. The red line represents the feedback states.</p>
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<p>The standing balancing experiment setup. The first row is the robot falling down without momentum compensation control. The second row is the robot recovery from disturbance with momentum compensation control.</p>
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<p>The inclination change of standing balancing. The left is the torso and sole inclination with momentum compensation, and the right is the torso and sole inclination without momentum compensation.</p>
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<p>The linear and angular momentum change of standing balancing. Both the linear and angular momentum are decreased after the allocation control. The maximum angular velocity of the torso is also reduced because of the rapid stabilization process.</p>
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<p>Changes of the contact wrench in standing balancing. The torque changes greatly in the Y direction of the sole.</p>
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<p>Discrete terrain walking. The walking area is limited to two rows of support columns.</p>
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<p>Different controller convergence times. Most of the time, the admittance controller converges first, then the momentum controller, and finally the posture controller.</p>
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<p>Walking on the floor covered with building blocks.</p>
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<p>Walking on a sandy slope.</p>
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14 pages, 3961 KiB  
Article
Finite Element Modeling of the Fingers and Wrist Flexion/Extension Effect on Median Nerve Compression
by Saveliy Peshin, Yulia Karakulova and Alex G. Kuchumov
Appl. Sci. 2023, 13(2), 1219; https://doi.org/10.3390/app13021219 - 16 Jan 2023
Cited by 3 | Viewed by 3179
Abstract
Carpal tunnel syndrome (CTS) is the most common pathology among disorders of the peripheral nervous system related to median nerve compression. To our knowledge, there are limited data on the effect of tendon movement on median nerve compression. This study focuses on the [...] Read more.
Carpal tunnel syndrome (CTS) is the most common pathology among disorders of the peripheral nervous system related to median nerve compression. To our knowledge, there are limited data on the effect of tendon movement on median nerve compression. This study focuses on the understanding of the carpal syndrome by simulating the impact of tendons movement caused by fingers flexion by Finite Element Analysis. Therefore, such modeling is the step toward the development of a personalized technique for value determining median nerve compression. Open-source MRI of the human right hand was used to build patient-specific phalanges of the fingers. Carpal tunnel soft tissues were considered as hyper-elastic materials, while bone structures were considered as elastic ones. The final finite-element model had 40 solid bodies which contacted the joint. Results were obtained for four cases of wrist movements: finger flexion, hand flexion/extension, and wrist extension with subsequent by finger flexion. Compression of the median nerve ranged from 129 Pa to 227 Pa. The results show that compression of the median nerve occurs faster during wrist flexion than during wrist extension or finger flexion. A decrease in compression during finger flexion was noticed with wrist extension followed by finger flexion. Full article
(This article belongs to the Special Issue Hand and Wrist Biomechanics)
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<p>Geometry of the model (<b>b</b>) based on MRI (<b>a</b>).</p>
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<p>Boundary condition for all cases. (<b>a</b>) finger flexion, (<b>b</b>) wrist extension, (<b>c</b>) wrist extension and subsequent fingers flexion, (<b>d</b>) wrist flexion.</p>
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<p>Stress [Pa] (<b>a</b>) SSCT with fingers flexion, (<b>b</b>) median nerve with fingers flexion, (<b>c</b>) transverse ligament with fingers flexion, (<b>d</b>) SSCT with wrist extension, (<b>e</b>) median nerve with wrist extension, (<b>f</b>) transverse ligament with wrist extension, (<b>g</b>) SSCT with wrist flexion, (<b>h</b>) median nerve with wrist flexion, (<b>i</b>) transverse ligament with wrist flexion.</p>
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<p>Dependence of pressure in the median nerve on the angle of the phalanx’s rotation.</p>
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<p>Wrist extension and subsequent fingers flexion.</p>
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<p>Pressure distribution in percentage.</p>
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<p>Finger tendon pressure distribution for cases 1, 2 and 4.</p>
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<p>Distribution of directed movements X and Y by tendon (1) Thumb, 2.1−superficial flexor index finger, 2.2−deep flexor index finger, 3.1−superficial flexor middle finger, 3.2−deep flexor middle finger, 4.1−superficial flexor ring finger, 4.2−deep flexor ring finger, 5.1−superficial flexor pinky, 5.2−deep flexor pinky). (<b>a</b>) Coordinate system, (<b>b</b>) case 1 (finger flexion), (<b>c</b>) case 2 (wrist extension), (<b>d</b>) case 4 (wrist flexion).</p>
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14 pages, 708 KiB  
Article
Force-Sensorless Identification and Classification of Tissue Biomechanical Parameters for Robot-Assisted Palpation
by Alejandro Gutierrez-Giles, Miguel A. Padilla-Castañeda, Luis Alvarez-Icaza and Enoch Gutierrez-Herrera
Sensors 2022, 22(22), 8670; https://doi.org/10.3390/s22228670 - 10 Nov 2022
Cited by 1 | Viewed by 2212
Abstract
The implementation of robotic systems for minimally invasive surgery and medical procedures is an active topic of research in recent years. One of the most common procedures is the palpation of soft tissues to identify their mechanical characteristics. In particular, it is very [...] Read more.
The implementation of robotic systems for minimally invasive surgery and medical procedures is an active topic of research in recent years. One of the most common procedures is the palpation of soft tissues to identify their mechanical characteristics. In particular, it is very useful to identify the tissue’s stiffness or equivalently its elasticity coefficient. However, this identification relies on the existence of a force sensor or a tactile sensor mounted at the tip of the robot, as well as on measuring the robot velocity. For some applications it would be desirable to identify the biomechanical characteristics of soft tissues without the need for a force/tactile nor velocity sensors. An estimation of such quantities can be obtained by a model-based state observer for which the inputs are only the robot joint positions and its commanded joint torques. The estimated velocities and forces can then be employed for closed-loop force control, force reflection, and mechanical parameters estimation. In this work, a closed-loop force control is proposed based on the estimated contact forces to avoid any tissue damage. Then, the information from the estimated forces and velocities is used in a least squares estimator of the mechanical parameters. Moreover, the estimated biomechanical parameters are employed in a Bayesian classifier to provide further help for the physician to make a diagnosis. We have found that a combination of the parameters of both linear and nonlinear viscoelastic models provide better classification results: 0% misclassifications against 50% when using a linear model, and 3.12% when using only a nonlinear model, for the case in which the samples have very similar mechanical properties. Full article
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<p>Experimental setup: <span class="html-italic">3D Systems Omni Touch</span> robot, <span class="html-italic">ATI Nano 17</span> force sensor (only for validation), and the silicone samples <span class="html-italic">Ecoflex Gel</span>, <span class="html-italic">Ecoflex 00-50</span>, <span class="html-italic">Dragon Skin 10</span>, and <span class="html-italic">Dragon Skin 30</span> silicone samples.</p>
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<p>Force tracking and estimation for the Ecoflex 00-50 sample. (<b>a</b>) Forces: desired (- - -), measured (<span style="color:red">—</span>), and estimated (<span style="color:blue">—</span>). (<b>b</b>) Force tracking error. (<b>c</b>) Force estimation error.</p>
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<p>Measured displacement of the tissue for the Eco Flex 00-50 sample.</p>
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<p>Estimation of the linear elasticity coefficients <math display="inline"><semantics> <msub> <mi>k</mi> <mi mathvariant="normal">l</mi> </msub> </semantics></math> in model (<a href="#FD3-sensors-22-08670" class="html-disp-formula">3</a>) for the four different rubber samples and their normal probability density functions.</p>
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<p>Estimation of the nonlinear elasticity coefficients <math display="inline"><semantics> <msub> <mi>k</mi> <mi>nl</mi> </msub> </semantics></math> in model (<a href="#FD4-sensors-22-08670" class="html-disp-formula">4</a>) for the four different rubber samples and their normal probability density functions.</p>
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16 pages, 4159 KiB  
Article
Dynamic Measurement of Patellofemoral Compression Forces: A Novel Method for Patient-Specific Patella Resurfacing in Total Knee Replacement
by Angela Brivio, David Barrett, Matthew F. Gong, Annabel Watson, Susie Naybour and Johannes F. Plate
Appl. Sci. 2022, 12(20), 10584; https://doi.org/10.3390/app122010584 - 20 Oct 2022
Cited by 1 | Viewed by 2089
Abstract
Functional dissatisfaction following total knee replacement (TKR) is recorded as high as 20%. The majority of these patients report anterior knee pain (AKP) as the main source of dissatisfaction. Elevated patellofemoral compression forces and soft tissue extensor hood strain have been implicated in [...] Read more.
Functional dissatisfaction following total knee replacement (TKR) is recorded as high as 20%. The majority of these patients report anterior knee pain (AKP) as the main source of dissatisfaction. Elevated patellofemoral compression forces and soft tissue extensor hood strain have been implicated in the generation of significant AKP. A novel method of assessing and measuring patellofemoral compression forces dynamically in the native and resurfaced patella for TKR in four different quadrants of the patella is described. Results are reported from an in vitro model and cadaveric studies in the native and resurfaced knee. Patellofemoral compression forces are shown to be characteristic and consistent over repeated assessments in the native knee. Placement of a TKR significantly alters this pattern. Furthermore, over-stuffing or under-stuffing the resurfaced patella also significantly alters the nature and magnitude of patellofemoral compression forces. These studies may lead to an improved understanding of the nature of AKP following TKR, and using this assessment tool presents an opportunity to more effectively balance the third space, reproduce the native patellofemoral forces, and subsequently reduce AKP following TKR. Full article
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<p>Quad sense force sensor. (<b>a</b>) 6 mm neutral shim applied to the sensor paddle. Each circle corresponds to one of four quadrants containing a separate sensor (Medial, Lateral, Superior, Inferior). (<b>b</b>) Tines on the undersurface of the sensor paddle are used to attach the sensor to the resected patella to hold it in place during range of motion trials.</p>
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<p>Demonstration of application of quad sense force sensor. (<b>a</b>) Placement of the quad sense sensor along undersurface of everted patella. Position of the sensor paddle is marked with electrocautery or a skin marker. (<b>b</b>) View of the quad sense sensor positioning during trialing with the patella reduced. Two towel clips are used for provisional reduction and closure of the extensor mechanism during range of motion trialing.</p>
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<p>CAD layout of the scanned femoral and tibial components, altered to be compatible with the Medical Model Left Cadaver Leg set-up when 3D printed and utilized for the in vitro experiment.</p>
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<p>Surgical technique for performing initial patella cut. Patellar clamp with attached 6 mm custom cutting guide is applied to the patella, and resection is performed with a standard oscillating saw.</p>
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<p>Flow diagram demonstrating planned steps of the cadaveric study, including when PFJ load measurements were obtained at key timepoints during the TKR procedure.</p>
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<p>Technique is shown for performing an adjustment cut to the manual left TKR set at 3 mm with a 2.5° inferiorly angled orientation guide. (<b>a</b>) Patellar clamp and custom adjustment cutting guide set. (<b>b</b>) Adjustment cut performed with patellar clamp and custom guide for a 3 mm 2.5° inferiorly angled cut in place. (<b>c</b>) Resected patella is shown after adjustment cut is completed.</p>
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<p>Graph of dynamic time warping analysis performed for the superior sensor load measurements obtained in an in vitro experiment using a model knee. Trials performed for the 6 mm to 9 mm neutral shims were consolidated and standardized by time, demonstrating the increase in measured force observed with each incremental increase in shim size. Purple = 6 mm neutral shim, Blue = 7 mm neutral shim, Green = 8 mm neutral shim, Red = 9 mm neutral shim. X-axis denotes Time Index (total of 12 s), and Y-axis denotes Average Force (in mV).</p>
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<p>Graph of dynamic time warping analysis performed for the medial sensor load measurements obtained in an in vitro experiment using a model knee. Trials performed for the 6 mm 2.5° angled shims were consolidated and standardized by time, demonstrating the changes in measured force based on shim angle orientation. Red = 6 mm neutral shim, Green = 6 mm 2.5° medial angle shim, Blue = 6 mm 2.5° lateral angle shim. X-axis denotes Time Index (total of 12 s), and Y-axis denotes Average Force (in mV).</p>
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<p>Comparison of visual graphs generated for PFJ load obtained before and after TKR in the right knee of Specimen 1. Three peaks seen correspond to the three knee flexion-extension cycles obtained in a 12 s recording period. Green = Medial sensor; Red = Lateral; Blue = Superior; Orange = Inferior. (<b>a</b>) PFJ load measured in native (natural) right knee with 6 mm neutral shim. (<b>b</b>) PFJ load measured in right knee with trial TKR components in place with 6 mm neutral shim. * Note: X-axis delineates time (total of 12 s) in which the three flexion-extension trials were performed. Y-axis (in mV) has different scales generated: (<b>a</b>) 2000 units of load (<b>b</b>) 2400 units of load.</p>
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<p>Graph of dynamic time warping analysis performed comparing the native and TKR load measurement in the right knee of Specimen 1. (<b>a</b>) Graph for lateral sensor loads measured in the right knee. Blue = Native 6 mm neutral shim; Red = TKR 6 mm neutral shim. (<b>b</b>) Graph for superior sensor loads measured in the right knee. Blue = Native 6 mm neutral shim; Red = TKR 6 mm neutral shim. X-axis denotes Time Index (total of 12 s), and Y-axis denotes Average Force (in mV).</p>
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15 pages, 3397 KiB  
Article
Investigation of the Durability of Gaskets in Contact with Bio- and Aviation Fuels
by Grzegorz Romanik, Janusz Rogula and Paweł Regucki
Materials 2022, 15(18), 6288; https://doi.org/10.3390/ma15186288 - 9 Sep 2022
Viewed by 1661
Abstract
Care for the natural environment, which can be observed in the tightening of emission standards, has enforced the search for new fuels, especially renewable sources of natural origin. The article presents the results of theoretical and experimental considerations on the impact of aviation [...] Read more.
Care for the natural environment, which can be observed in the tightening of emission standards, has enforced the search for new fuels, especially renewable sources of natural origin. The article presents the results of theoretical and experimental considerations on the impact of aviation biofuels on the materials used for sealing flange joints. The fuel type selected for the test is compatible with aviation fuels. Fuels have been enriched with a bio-additive that changes the technical and physical properties of the fuel. The tested gaskets were made of soft, aramid-elastomeric materials that were flat in shape and without reinforcement. Their commercial names are AFO and AFM. Tests were carried out with the use of a simple flange joint with a fuel reservoir at 373 K. Both fuel loss and the pressure drop on the gasket were measured during a 1000 h period of time. The experiments showed that the seals preserved the technical parameters in the presence of the tested fuels. The fuel loss did not exceed the accepted limits, which demonstrates the suitability of the tested materials for utilization with new types of fuel. However, no unequivocal conclusions can be drawn about the positive or negative impact of bio-additives on the sealing material due to the fact that both an improvement and deterioration in tightness under certain circumstances were observed. Based on the experimental data, a mathematical model was proposed that makes it possible to predict the service life of the gaskets in flange joints in contact with the investigated types of fuel. The potential application of the research results is practical information about the impact of biofuel on the gasket, and hence the information about the possibility of using traditional sealing materials in a new application—for sealing installations for the production, transmission and storage of biofuels. Full article
(This article belongs to the Special Issue Materials in Energy Technology)
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<p>Scheme of the test stand: 1—dial gauge, 2—upper flange, 3—lower flange, 4—nut, 5—gasket, 6—bolt, 7—20 mL chamber with fuel.</p>
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<p>View of the tested gaskets; AFM—on the left, AFO—on the right.</p>
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<p>Research stages: (<b>a</b>) gasket assembly, (<b>b</b>) tension control of bolts, (<b>c</b>) weight control.</p>
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<p>The creep process in the function of time [<a href="#B9-materials-15-06288" class="html-bibr">9</a>].</p>
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<p>Contact pressure for the AFO gasket and the tested fuels—theoretical characteristics and experimental data.</p>
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<p>Contact pressure for the AFO gasket and the tested fuels—theoretical characteristics and experimental data.</p>
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<p>Contact pressure for the AFM gasket and the tested fuels—theoretical characteristics and experimental data.</p>
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<p>Time <span class="html-italic">t</span><sub>0</sub> from when the secondary creep starts, which is marked in the contact pressure graph (red curve) for the AFO gasket and all the tested fuels. The blue line indicates the linear decrease of constant pressure.</p>
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<p>Time <span class="html-italic">t</span><sub>0</sub> from when the secondary creep starts, which is marked on the contact pressure graph (red curve) for the AFM gasket and all the tested fuels. The blue line indicates the linear decrease of constant pressure.</p>
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16 pages, 6286 KiB  
Article
Design of a Lightweight and Deployable Soft Robotic Arm
by Pierpaolo Palmieri, Matteo Melchiorre and Stefano Mauro
Robotics 2022, 11(5), 88; https://doi.org/10.3390/robotics11050088 - 31 Aug 2022
Cited by 14 | Viewed by 7770
Abstract
Soft robotics represents a rising trend in recent years, due to the ability to work in unstructured environments or in strict contact with humans. Introducing soft parts, robots can adapt to various contexts overcoming limits relative to the rigid structure of traditional ones. [...] Read more.
Soft robotics represents a rising trend in recent years, due to the ability to work in unstructured environments or in strict contact with humans. Introducing soft parts, robots can adapt to various contexts overcoming limits relative to the rigid structure of traditional ones. Main issues of soft robotics systems concern the relatively low force exertion and control complexity. Moreover, several fields of application, as space industry, need to develop novel lightweight and deployable robotic systems, that can be stored into a relatively small volume and deployed when required. In this paper, POPUP robot is introduced: a soft manipulator having inflatable links and rigid joints. Its hybrid structure aims to match the advantages of rigid robots and the useful properties of having a lightweight and deployable parts, ensuring simple control, low energy consumption and low compressed gas requirement. The first robot prototype and the system architecture are described highlighting design criteria and effect of internal pressure on the performances. A pseudo-rigid body model is used to describe the behavior of inflatable links looking forward to control design. Finally, the model is extended to the whole robot: multi-body simulations are performed to highlight the importance of suitable sensor equipment for control development, proposing a visual servoing solution. Full article
(This article belongs to the Special Issue Frontiers in Bionic and Flexible Robotics)
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<p>POPUP robot prototype: inflatable links, rigid joints, electric motors and pneumatic line.</p>
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<p>Exploded view drawing of POPUP link 2 prototype.</p>
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<p>System architecture scheme: motors (M), links, inertial measurements units (IMUs), flex sensors, pneumatic line, main and link boards, computer.</p>
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<p>Deployment stage of the POPUP robot prototype. Deflated configuration (<b>a</b>), start of the inflation of link 1 (<b>b</b>), continuation of the inflation (<b>c</b>), inflation of link 1 completed and start of inflation od the link 2 (<b>d</b>), links inflated and stabilizing (<b>e</b>), robot deflated and in working configuration (<b>f</b>).</p>
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<p>Pseudo-rigid body model of the link, considered as cantilever.</p>
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<p>Experimental set-up of dynamic tests on the inflatable link prototype.</p>
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<p>Static characteristic of the link prototype depending on the internal pressure level, averaged data based on 3 measures. Loading (solid line) and unloading (dashed line).</p>
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<p>Robot kinematic scheme considering pseudo-rigid body model with virtual joints (PRBM, solid line) and rigid body model (RBM, dashed line).</p>
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<p>Graphic output of multi-body model.</p>
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<p>Scheme of the simulated task.</p>
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<p>Block scheme of POPUP control using RBM.</p>
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<p>Trajectory of PRBM compared to RBM reaching the target using RBM-based control.</p>
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<p>EE position difference of PRBM with respect to RBM during the task.</p>
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<p>Block scheme of POPUP control using VS with camera mounted on EE.</p>
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<p>Trajectory of PRBM compared to RBM reaching the target using VS.</p>
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<p>Motor joint position during the two simulations with RBM-based and VS control.</p>
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20 pages, 4731 KiB  
Article
Analytical Solution and Shaking Table Test on Tunnels through Soft-Hard Stratum with a Transition Tunnel and Flexible Joints
by Gaoming Yan and Boming Zhao
Appl. Sci. 2022, 12(6), 3151; https://doi.org/10.3390/app12063151 - 19 Mar 2022
Cited by 8 | Viewed by 2191
Abstract
Tunnels, where they pass through soft-hard strata, are severely damaged during earthquakes. These issues have not yet been well understood. In this study, the seismic performances of a tunnel passing through soft-hard stratum with a transition tunnel and flexible joints under earthquake motion [...] Read more.
Tunnels, where they pass through soft-hard strata, are severely damaged during earthquakes. These issues have not yet been well understood. In this study, the seismic performances of a tunnel passing through soft-hard stratum with a transition tunnel and flexible joints under earthquake motion were investigated by proposed analytical solutions and scaled shaking table tests. First, a mechanical model of a tunnel passing through soft-hard stratum with flexible joints is proposed, and it is derived by the Green’s function method. Then, a parametric analysis is conducted to investigate the effects of important variables on tunnels through soft-hard stratum. Finally, shaking table tests are conducted to verify the proposed solution and further investigate the seismic behaviors of the tunnel. The results show that: (1) the analytical solutions are workable and effective; (2) the influence of the soft-hard stratum junction on the tunnel responses is remarkable—the largest bending moment is located at the side of soft rock near the sharp contact area and the maximum shear force appears at the contact; (3) the joints and the transition tunnel could mitigate the potential adverse effects of the sharp contact area—the region affected by the joint is approximately 4.5 times the tunnel diameter on both sides of the stratum interface; and (4) the influence of sharp change of ground layers is more remarkable with a larger excitation amplitude. Full article
(This article belongs to the Special Issue Tunneling and Underground Engineering: From Theories to Practices)
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<p>Longitudinal profile of the tunnel.</p>
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<p>Analytical model.</p>
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<p>Input earthquake wave.</p>
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<p>Responses of a tunnel buried in non-homogeneous ground (case 1).</p>
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<p>Influences of the soft-hard stratum junction on normalized internal forces. (<b>a</b>) Normalized bending moment; (<b>b</b>) normalized shear force.</p>
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<p>Displacement along the tunnel axis.</p>
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<p>Influence of the joint on tunnel responses. (<b>a</b>) Bending moment; (<b>b</b>) shear force.</p>
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<p>Effect of the length of the transition zone on internal forces. (<b>a</b>) Bending moment; (<b>b</b>) shear force.</p>
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<p>Model soil interface control.</p>
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<p>Jointed lining segments.</p>
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<p>Instrumentation for the shaking table tests.</p>
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<p>Input earthquake wave. (<b>a</b>) Acceleration time histories; (<b>b</b>) Fourier spectra.</p>
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<p>Comparison of bending moments of lining between the theoretical analysis and the model tests.</p>
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<p>Peak strains of measuring points for excitations with different amplitudes. (<b>a</b>) 0.2 g; (<b>b</b>) 0.4 g.</p>
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<p>Acceleration records and Fourier spectra of monitoring positions in the shaking table tests. (<b>a</b>) Acceleration time history and Fourier spectrum of monitoring point A1; (<b>b</b>) acceleration time history and Fourier spectrum of monitoring point A3; (<b>c</b>) acceleration time history and Fourier spectrum of monitoring point A4; (<b>d</b>) acceleration time history and Fourier spectrum of monitoring point A5.</p>
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14 pages, 2157 KiB  
Article
Estimation of Tibiofemoral and Patellofemoral Joint Forces during Squatting and Kneeling
by Ulrich Glitsch, Kai Heinrich and Rolf Peter Ellegast
Appl. Sci. 2022, 12(1), 255; https://doi.org/10.3390/app12010255 - 28 Dec 2021
Cited by 2 | Viewed by 4762
Abstract
This study examined the differences of knee joint forces between lowering to, or rising from squat, and typical final postures of squatting and kneeling. A biomechanical model of the lower limb was configured considering large knee flexion angles, multiple floor contact points, and [...] Read more.
This study examined the differences of knee joint forces between lowering to, or rising from squat, and typical final postures of squatting and kneeling. A biomechanical model of the lower limb was configured considering large knee flexion angles, multiple floor contact points, and the soft tissue contact between the thigh and calf. Inverse dynamics were used to determine muscle and compressive joint forces in the tibiofemoral and patellofemoral joints. Data were obtained from a group of 13 male subjects by means of 3D motion capturing, two force plates, a pressure-sensitive pad, and electromyography. During lowering into the kneeling/squatting positions and rising from them, the model exhibited the anticipated high maximum forces of 2.6 ± 0.39 body weight (BW) and 3.4 ± 0.56 BW in the tibiofemoral and patellofemoral joints. Upon attainment of the static terminal squatting and kneeling positions, the forces fell considerably, remaining within a range of between 0.5 and 0.7 BW for the tibiofemoral joint and 0.9 to 1.1 BW for the patellofemoral joint. The differences of the knee joint forces between the final postures of squatting and kneeling remained on average below 0.25 BW and were significant only for the tibiofemoral joint force. Full article
(This article belongs to the Special Issue Biomechanical and Biomedical Factors of Knee Osteoarthritis)
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<p>External forces to be considered in the analysis of (<b>a</b>) deep squatting, (<b>b</b>) upright kneeling, and (<b>c</b>) deep kneeling.</p>
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<p>Segments model of the lower limb and applied forces–foot ground contact (<span class="html-italic">F</span><sub>f</sub>), knee ground contact (<span class="html-italic">F</span><sub>t</sub>), thigh-calf contact (<span class="html-italic">F</span><sub>tc</sub>), patellar ligament (<span class="html-italic">F</span><sub>pl</sub>), quadriceps tendon (<span class="html-italic">F</span><sub>qt</sub>), hamstrings tendon (<span class="html-italic">F</span><sub>ht</sub>), tibiofemoral joint (<span class="html-italic">F</span><sub>tf</sub>), and patellofemoral joint (<span class="html-italic">F</span><sub>pf</sub>). The inertial and gravitational forces are not explicitly depicted. The local segment coordinate systems of the tibia and the femur are indicated by (x<sub>t</sub>, y<sub>t</sub>, z<sub>t</sub>) and (x<sub>f</sub>, y<sub>f</sub>, z<sub>f</sub>), respectively.</p>
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<p>Angles of the patellar ligament and quadriceps tendon with respect to knee flexion angle. The angles of the patellar ligament from the model of Eijden van et al. [<a href="#B27-applsci-12-00255" class="html-bibr">27</a>] are depicted for comparison purposes.</p>
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<p>(<b>a</b>) Schematic representation of the patellar ligament mechanism and of the sloped tibial weight-bearing surface; (<b>b</b>) Piecewise approximation of the moment arm length of the patellar with respect to knee flexion angle compared the data of O’Connor et al. [<a href="#B29-applsci-12-00255" class="html-bibr">29</a>] and Zheng et al. [<a href="#B30-applsci-12-00255" class="html-bibr">30</a>]. The data from Zheng et al. [<a href="#B30-applsci-12-00255" class="html-bibr">30</a>] are depicted for comparison purposes only and have not been used in the IFA-model.</p>
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<p>Comparison of the ratio of <span class="html-italic">F</span><sub>pl</sub>/<span class="html-italic">F</span><sub>qt</sub> between patellar ligament force and quadriceps vs knee flexion angle from the IFA model with data presented by Eijden van et al. [<a href="#B27-applsci-12-00255" class="html-bibr">27</a>].</p>
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<p>A typical example of the time histories of dynamic parameters and quadriceps muscle activity (%MVC) during lowering into a squat.</p>
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<p>Examples of force vector graphics of (<b>a</b>) squatting and (<b>b</b>) deep kneeling. For further details see <a href="#applsci-12-00255-f002" class="html-fig">Figure 2</a>.</p>
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