scholarly journals Interpreting and predicting tactile signals for the SynTouch BioTac

2021 ◽  
pp. 027836492110476
Author(s):  
Yashraj S. Narang ◽  
Balakumar Sundaralingam ◽  
Karl Van Wyk ◽  
Arsalan Mousavian ◽  
Dieter Fox
Keyword(s):  
Center Of Pressure ◽  
High Density ◽  
Human Hand ◽  
Fe Model ◽  
Tactile Sensors ◽  
Diverse Range ◽  
Contact Information ◽  
Grasping And Manipulation ◽  
The Rich ◽  
Low Dimensional

In the human hand, high-density contact information provided by afferent neurons is essential for many human grasping and manipulation capabilities. In contrast, robotic tactile sensors, including the state-of-the-art SynTouch BioTac, are typically used to provide low-density contact information, such as contact location, center of pressure, and net force. Although useful, these data do not convey or leverage the rich information content that some tactile sensors naturally measure. This research extends robotic tactile sensing beyond reduced-order models through (1) the automated creation of a precise experimental tactile dataset for the BioTac over a diverse range of physical interactions, (2) a 3D finite-element (FE) model of the BioTac, which complements the experimental dataset with high-density, distributed contact data, (3) neural-network-based mappings from raw BioTac signals to not only low-dimensional experimental data, but also high-density FE deformation fields, and (4) mappings from the FE deformation fields to the raw signals themselves. The high-density data streams can provide a far greater quantity of interpretable information for grasping and manipulation algorithms than previously accessible. Datasets, CAD files for the experimental testbed, FE model files, and videos are available at https://sites.google.com/nvidia.com/tactiledata .

STAND-UP MOTION SUPPORT BY A MOBILE MANIPULATOR SYSTEM WITH HIGH SPEED TACTILE SENSORS

2011 ◽  
Vol 08 (03) ◽  
pp. 181-195
Author(s):  
ZHAOXIAN XIE ◽  
HISASHI YAMAGUCHI ◽  
MASAHITO TSUKANO ◽  
AIGUO MING ◽  
MAKOTO SHIMOJO
Keyword(s):  
Fuzzy Logic ◽  
Feedback Control ◽  
Experimental Verification ◽  
High Speed ◽  
Center Of Pressure ◽  
Tactile Sensor ◽  
Mobile Manipulator ◽  
Tactile Sensing ◽  
Robot Arm ◽  
Tactile Sensors

As one of the home services by a mobile manipulator system, we are aiming at the realization of the stand-up motion support for elderly people. This work is charaterized by the use of real-time feedback control based on the information from high speed tactile sensors for detecting the contact force as well as its center of pressure between the assisted human and the robot arm. First, this paper introduces the design of the tactile sensor as well as initial experimental results to show the feasibility of the proposed system. Moreover, several fundamental tactile sensing-based motion controllers necessary for the stand-up motion support and their experimental verification are presented. Finally, an assist trajectory generation method for the stand-up motion support by integrating fuzzy logic with tactile sensing is proposed and demonstrated experimentally.

Improved interactive color visualization approach for hyperspectral images

2021 ◽  
pp. 147387162110481
Author(s):  
Haijun Yu ◽  
Shengyang Li
Keyword(s):  
Real Time ◽  
Hyperspectral Images ◽  
High Dimensional ◽  
Interactive Control ◽  
Output Image ◽  
Dr Method ◽  
The Rich ◽  
Low Dimensional ◽  
Color Visualization ◽  
Fusion Coefficient

Hyperspectral images (HSIs) have become increasingly prominent as they can maintain the subtle spectral differences of the imaged objects. Designing approaches and tools for analyzing HSIs presents a unique set of challenges due to their high-dimensional characteristics. An improved color visualization approach is proposed in this article to achieve communication between users and HSIs in the field of remote sensing. Under the real-time interactive control and color visualization, this approach can help users intuitively obtain the rich information hidden in original HSIs. Using the dimensionality reduction (DR) method based on band selection, high-dimensional HSIs are reduced to low-dimensional images. Through drop-down boxes, users can freely specify images that participate in the combination of RGB channels of the output image. Users can then interactively and independently set the fusion coefficient of each image within an interface based on concentric circles. At the same time, the output image will be calculated and visualized in real time, and the information it reflects will also be different. In this approach, channel combination and fusion coefficient setting are two independent processes, which allows users to interact more flexibly according to their needs. Furthermore, this approach is also applicable for interactive visualization of other types of multi-layer data.

scholarly journals A High-Density EST-SSR-Based Genetic Map and QTL Analysis of Dwarf Trait in Cucurbita pepo L.

2018 ◽  
Vol 19 (10) ◽  
pp. 3140 ◽  
Author(s):  
Chenggang Xiang ◽  
Ying Duan ◽  
Hongbo Li ◽  
Wei Ma ◽  
Sanwen Huang ◽  
...  
Keyword(s):  
Genetic Map ◽  
Simple Sequence Repeats ◽  
Cucurbita Pepo ◽  
Economic Value ◽  
High Density ◽  
Oxidase Gene ◽  
Phenotypic Polymorphism ◽  
The Rich ◽  
Expressed Sequence ◽  
Simple Sequence

As one of the earliest domesticated species, Cucurbita pepo (including squash and pumpkin) is rich in phenotypic polymorphism and has huge economic value. In this research, using 1660 expressed sequence tags-simple sequence repeats (EST-SSRs) and 632 genomic simple sequence repeats (gSSRs), we constructed the highest-density EST-SSR-based genetic map in Cucurbita genus, which spanned 2199.1 cM in total and harbored 623 loci distributed in 20 linkage groups. Using this map as a bridge, the two previous gSSR maps were integrated by common gSSRs and the corresponding relationships around chromosomes in three sets of genomes were also collated. Meanwhile, one large segmental inversion that existed between our map and the C. pepo genome was detected. Furthermore, three Quantitative Trait Loci (QTLs) of the dwarf trait (gibberellin-sensitive dwarf type) in C. pepo were located, and the candidate region that covered the major QTL spanned 1.39 Mb, which harbored a predicted gibberellin 2-β-oxidase gene. Considering the rich phenotypic polymorphism, the important economic value in the Cucurbita genus species and several advantages of the SSR marker were identified; thus, this high-density EST-SSR-based genetic map will be useful in Pumpkin and Squash breeding work in the future.

scholarly journals Artificial Finger Skin having Ridges and Distributed Tactile Sensors used for Grasp Force Control

2002 ◽  
Vol 14 (2) ◽  
pp. 140-146 ◽  
Author(s):  
Daisuke Yamada ◽  
◽  
Takashi Maeno ◽  
Yoji Yamada ◽  
Keyword(s):  
Reaction Force ◽  
Frictional Coefficient ◽  
Dynamic Contact ◽  
Fe Model ◽  
Tactile Sensors ◽  
Fe Method ◽  
Stick Slip ◽  
Grasping Force ◽  
Grasp Force ◽  
Finger Skin

An artificial elastic finger skin for robot fingers has been developed for controlling grasp force when weight and frictional coefficient of the grasped object are unknown. The elastic finger skin has ridges at the surface to divide the stick/slip area. It also has a pair of tactile sensors embedded per ridge similar to human fingertips. The surface of the whole finger is curved so that reaction force distributes. A Finite Element (FE) model of the elastic finger skin was made to conduct dynamic contact analysis using a FE method to design the elastic finger skin in detail. Then the elastic finger skin was made. We confirmed by calculation and experiment that incipient slippage of the ridge occurring near the edge of contact is detected. Then, grasp was controlled using the finger. Arbitrary objects were lifted by incipient slippage near the edge of contact. We found that artificial finger skin is useful for controlling grasping force when the weight and friction coefficient between the elastic finger skin and grasping object are unknown.

Highly sensitive capacitive flexible 3D-force tactile sensors for robotic grasping and manipulation

2020 ◽  
Vol 53 (44) ◽  
pp. 445109
Author(s):  
Tianyang Yao ◽  
Xiaohui Guo ◽  
Cuicui Li ◽  
Haiqiang Qi ◽  
Huai Lin ◽  
...  
Keyword(s):  
Robotic Grasping ◽  
Tactile Sensors ◽  
Highly Sensitive ◽  
Grasping And Manipulation

scholarly journals Finite Element Model of the Knee for Investigation of Injury Mechanisms: Development and Validation

2013 ◽  
Vol 136 (1) ◽  
Author(s):  
Ali Kiapour ◽  
Ata M. Kiapour ◽  
Vikas Kaul ◽  
Carmen E. Quatman ◽  
Samuel C. Wordeman ◽  
...  
Keyword(s):  
Experimental Data ◽  
Finite Element ◽  
Knee Joint ◽  
Center Of Pressure ◽  
Fe Model ◽  
Loading Conditions ◽  
Tibiofemoral Rotation ◽  
Injury Mechanisms ◽  
Model Predictions ◽  
Tibiofemoral Kinematics

Multiple computational models have been developed to study knee biomechanics. However, the majority of these models are mainly validated against a limited range of loading conditions and/or do not include sufficient details of the critical anatomical structures within the joint. Due to the multifactorial dynamic nature of knee injuries, anatomic finite element (FE) models validated against multiple factors under a broad range of loading conditions are necessary. This study presents a validated FE model of the lower extremity with an anatomically accurate representation of the knee joint. The model was validated against tibiofemoral kinematics, ligaments strain/force, and articular cartilage pressure data measured directly from static, quasi-static, and dynamic cadaveric experiments. Strong correlations were observed between model predictions and experimental data (r > 0.8 and p < 0.0005 for all comparisons). FE predictions showed low deviations (root-mean-square (RMS) error) from average experimental data under all modes of static and quasi-static loading, falling within 2.5 deg of tibiofemoral rotation, 1% of anterior cruciate ligament (ACL) and medial collateral ligament (MCL) strains, 17 N of ACL load, and 1 mm of tibiofemoral center of pressure. Similarly, the FE model was able to accurately predict tibiofemoral kinematics and ACL and MCL strains during simulated bipedal landings (dynamic loading). In addition to minimal deviation from direct cadaveric measurements, all model predictions fell within 95% confidence intervals of the average experimental data. Agreement between model predictions and experimental data demonstrates the ability of the developed model to predict the kinematics of the human knee joint as well as the complex, nonuniform stress and strain fields that occur in biological soft tissue. Such a model will facilitate the in-depth understanding of a multitude of potential knee injury mechanisms with special emphasis on ACL injury.

scholarly journals Electromyography Classification during Reach-to-Grasp Motion using Manifold Learning

2020 ◽  
Author(s):  
Elnaz Lashgari ◽  
Uri Maoz
Keyword(s):  
Dimensionality Reduction ◽  
Cost Effective ◽  
Human Hand ◽  
Reach To Grasp ◽  
K Nearest Neighbors ◽  
Laplacian Eigenmaps ◽  
Non Invasive ◽  
Reduction Methods ◽  
Machine Interface ◽  
Low Dimensional

AbstractElectromyography (EMG) is a simple, non-invasive, and cost-effective technology for sensing muscle activity. However, EMG is also noisy, complex, and high-dimensional. It has nevertheless been widely used in a host of human-machine-interface applications (electrical wheelchairs, virtual computer mice, prosthesis, robotic fingers, etc.) and in particular to measure reaching and grasping motions of the human hand. Here, we developd a more automated pipeline to predict object weight in a reach-and-grasp task from an open dataset relying only on EMG data. In that we shifted the focus from manual feature-engineering to automated feature-extraction by using raw (filtered) EMG signals and thus letting the algorithms select the features. We further compared intrinsic EMG features, derived from several dimensionality-reduction methods, and then ran some classification algorithms on these low-dimensional representations. We found that the Laplacian Eigenmap algorithm generally outperformed other dimensionality-reduction methods. What is more, optimal classification accuracy was achieved using a combination of Laplacian Eigenmaps (simple-minded) and k-Nearest Neighbors (88% for 3-way classification). Our results, using EMG alone, are comparable to others in the literature that used EMG and EEG together. They also demonstrate the usefulness of dimensionality reduction when classifying movement based on EMG signals and more generally the usefulness of EMG for movement classification.

scholarly journals The effects of motor modularity on performance, learning and generalizability in upper-extremity reaching: a computational analysis

2020 ◽  
Vol 17 (167) ◽  
pp. 20200011
Author(s):  
Mazen Al Borno ◽  
Jennifer L. Hicks ◽  
Scott L. Delp
Keyword(s):  
Upper Extremity ◽  
Realistic Model ◽  
Computational Time ◽  
Muscle Synergies ◽  
Performance Deficits ◽  
Reaching Task ◽  
Learning Rates ◽  
The Central Nervous System ◽  
The Rich ◽  
Low Dimensional

It has been hypothesized that the central nervous system simplifies the production of movement by limiting motor commands to a small set of modules known as muscle synergies. Recently, investigators have questioned whether a low-dimensional controller can produce the rich and flexible behaviours seen in everyday movements. To study this issue, we implemented muscle synergies in a biomechanically realistic model of the human upper extremity and performed computational experiments to determine whether synergies introduced task performance deficits, facilitated the learning of movements, and generalized to different movements. We derived sets of synergies from the muscle excitations our dynamic optimizations computed for a nominal task (reaching in a plane). Then we compared the performance and learning rates of a controller that activated all muscles independently to controllers that activated the synergies derived from the nominal reaching task. We found that a controller based on synergies had errors within 1 cm of a full-dimensional controller and achieved faster learning rates (as estimated from computational time to converge). The synergy-based controllers could also accomplish new tasks—such as reaching to targets on a higher or lower plane, and starting from alternative initial poses—with average errors similar to a full-dimensional controller.

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