scholarly journals A Vision-Based Sensing Approach for a Spherical Soft Robotic Arm

2021 ◽  
Vol 8 ◽  
Author(s):  
Matthias Hofer ◽  
Carmelo Sferrazza ◽  
Raffaello D’Andrea
Keyword(s):  
Motion Capture ◽  
Network Architecture ◽  
Degrees Of Freedom ◽  
Sensory Feedback ◽  
Ground Truth ◽  
Robotic Systems ◽  
Robotic Arm ◽  
Ground Truth Data ◽  
Rotational Degrees Of Freedom ◽  
Soft Robotic Arm

Sensory feedback is essential for the control of soft robotic systems and to enable deployment in a variety of different tasks. Proprioception refers to sensing the robot’s own state and is of crucial importance in order to deploy soft robotic systems outside of laboratory environments, i.e. where no external sensing, such as motion capture systems, is available. A vision-based sensing approach for a soft robotic arm made from fabric is presented, leveraging the high-resolution sensory feedback provided by cameras. No mechanical interaction between the sensor and the soft structure is required and consequently the compliance of the soft system is preserved. The integration of a camera into an inflatable, fabric-based bellow actuator is discussed. Three actuators, each featuring an integrated camera, are used to control the spherical robotic arm and simultaneously provide sensory feedback of the two rotational degrees of freedom. A convolutional neural network architecture predicts the two angles describing the robot’s orientation from the camera images. Ground truth data is provided by a motion capture system during the training phase of the supervised learning approach and its evaluation thereafter. The camera-based sensing approach is able to provide estimates of the orientation in real-time with an accuracy of about one degree. The reliability of the sensing approach is demonstrated by using the sensory feedback to control the orientation of the robotic arm in closed-loop.

Integrating laser profile sensor to an industrial robotic arm for improving quality inspection in manufacturing processes

2020 ◽  
pp. 095440622094255
Author(s):  
Toufik Al Khawli ◽  
Muddasar Anwar ◽  
Dongming Gan ◽  
Shafiqul Islam
Keyword(s):  
Degrees Of Freedom ◽  
Ground Truth ◽  
Calibration Procedure ◽  
Robotic Arm ◽  
Manufacturing Processes ◽  
Quality Inspection ◽  
Six Degrees Of Freedom ◽  
Tool Positioning ◽  
Sharp Object ◽  
Laser Profile

This paper investigates the integration of laser profile sensor to an industrial robotic arm for automating the quality inspection in manufacturing processes that requires a manual labour intensive work. The aim was to register the measurements from a laser profile sensor mounted on a six degrees-of-freedom robot with respect to the robot base frame. The registration is based on a six degrees-of-freedom calibration, which is an essential step for several automated manufacturing processes that require high level of accuracy in tool positioning and alignment on one hand, and quality inspection systems that require flexibility and accurate measurements on the other hand. The investigation compromises of two calibration procedures namely the calibration using a sharp object and the planar constraints. The solution of the calibration procedures estimated from both iterative and optimization solvers is thoroughly discussed. By implementing a simulation platform that generates virtual data for the two procedures with additional levels of noise, the six-dimensional poses are estimated and compared to the ground truth. Finally, an experimental test using a laser profile from Acuity mounted on Mitsubishi RV-6SDL manipulator is presented to investigate the measurement accuracy with four estimated laser poses. The calibration procedure using a sharp object shows the most accurate simulation and experimental results under the effect of noise.

Design of a Robotic Cameraman With Three Actuators for Laparoscopic Surgery

2006 ◽  
Author(s):  
A. Mirbagheri ◽  
F. Farahmand ◽  
A. Meghdari ◽  
H. Sayyaadi ◽  
L. Savoj ◽  
...  
Keyword(s):  
Minimally Invasive Surgery ◽  
Laparoscopic Surgery ◽  
Minimally Invasive ◽  
Invasive Surgery ◽  
Degrees Of Freedom ◽  
Personal Control ◽  
Robotic Systems ◽  
Robotic Arm ◽  
And Control ◽  
Novel Design

Laparoscopic surgery is a specific branch of minimally invasive surgery (MIS) that is performed on the abdomen and endoscopic tools are passed through the incision points and trocars on the abdominal wall, so they can reach the surgical site [1]. Robotic systems have been proved to be very useful as a cameraman in laparoscopic surgery; they are more stable with no fatigue and inattention and reduce the supernumerary staff required, provide excellent geometrical accuracy and improved personal control for the surgeon over the procedure, etc. The available robots for handling and control of laparoscopic lens include at least 4 actuators to fulfill the surgeon’s requirements [2]. The purpose of the present study was to develop a novel design for the laparoscope robotic arm in which while the systems move ability is maintained its active degrees of freedom are reduced.

scholarly journals Simulation of cryo-EM ensembles from atomic models of molecules exhibiting continuous conformations

2019 ◽  
Author(s):  
Evan Seitz ◽  
Francisco Acosta-Reyes ◽  
Peter Schwander ◽  
Joachim Frank
Keyword(s):  
Single Molecule ◽  
Conformational Changes ◽  
Degrees Of Freedom ◽  
Ground Truth ◽  
Free Energies ◽  
Ground Truth Data ◽  
Metabolic Functions ◽  
In Vitro Systems ◽  
Low Dimensional

AbstractMolecular machines visit a continuum of conformational states as they go through work cycles required for their metabolic functions. Single-molecule cryo-EM of suitable in vitro systems affords the ability to collect a large ensemble of projections depicting the continuum of structures and assign occupancies, or free energies, to the observed states. Through the use of machine learning and dimension reduction algorithms it is possible to determine a low-dimensional free energy landscape from such data, allowing the basis for molecular function to be elucidated. In the absence of ground truth data, testing and validation of such methods is quite difficult, however. In this work, we propose a workflow for generating simulated cryo-EM data from an atomic model subjected to conformational changes. As an example, an ensemble of structures and their multiple projections was created from heat shock protein Hsp90 with two defined conformational degrees of freedom. All scripts for reproducing this workflow are available online. 1

scholarly journals The accuracy of several pose estimation methods for 3D joint centre localisation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Laurie Needham ◽  
Murray Evans ◽  
Darren P. Cosker ◽  
Logan Wade ◽  
Polly M. McGuigan ◽  
...  
Keyword(s):  
Motion Capture ◽  
Pose Estimation ◽  
High Speed ◽  
Large Scale ◽  
Image Data ◽  
Ground Truth ◽  
Human Movement ◽  
Estimation Methods ◽  
Ground Truth Data ◽  
Estimation Algorithms

AbstractHuman movement researchers are often restricted to laboratory environments and data capture techniques that are time and/or resource intensive. Markerless pose estimation algorithms show great potential to facilitate large scale movement studies ‘in the wild’, i.e., outside of the constraints imposed by marker-based motion capture. However, the accuracy of such algorithms has not yet been fully evaluated. We computed 3D joint centre locations using several pre-trained deep-learning based pose estimation methods (OpenPose, AlphaPose, DeepLabCut) and compared to marker-based motion capture. Participants performed walking, running and jumping activities while marker-based motion capture data and multi-camera high speed images (200 Hz) were captured. The pose estimation algorithms were applied to 2D image data and 3D joint centre locations were reconstructed. Pose estimation derived joint centres demonstrated systematic differences at the hip and knee (~ 30–50 mm), most likely due to mislabeling of ground truth data in the training datasets. Where systematic differences were lower, e.g., the ankle, differences of 1–15 mm were observed depending on the activity. Markerless motion capture represents a highly promising emerging technology that could free movement scientists from laboratory environments but 3D joint centre locations are not yet consistently comparable to marker-based motion capture.

scholarly journals EVALUATING CONTINUOUS-TIME SLAM USING A PREDEFINED TRAJECTORY PROVIDED BY A ROBOTIC ARM

2017 ◽  
Vol IV-2/W4 ◽  
pp. 91-98
Author(s):  
B. Koch ◽  
R. Leblebici ◽  
A. Martell ◽  
S. Jörissen ◽  
K. Schilling ◽  
...  
Keyword(s):  
Continuous Time ◽  
Point Cloud ◽  
Degrees Of Freedom ◽  
Laser Scanner ◽  
Ground Truth ◽  
Robotic Arm ◽  
Laser Sensor ◽  
Test Environment ◽  
Previous State ◽  
Slam Algorithm

Recently published approaches to SLAM algorithms process laser sensor measurements and output a map as a point cloud of the environment. Often the actual precision of the map remains unclear, since SLAMalgorithms apply local improvements to the resulting map. Unfortunately, it is not trivial to compare the performance of SLAMalgorithms objectively, especially without an accurate ground truth. This paper presents a novel benchmarking technique that allows to compare a precise map generated with an accurate ground truth trajectory to a map with a manipulated trajectory which was distorted by different forms of noise. The accurate ground truth is acquired by mounting a laser scanner on an industrial robotic arm. The robotic arm is moved on a predefined path while the position and orientation of the end-effector tool are monitored. During this process the 2D profile measurements of the laser scanner are recorded in six degrees of freedom and afterwards used to generate a precise point cloud of the test environment. For benchmarking, an offline continuous-time SLAM algorithm is subsequently applied to remove the inserted distortions. Finally, it is shown that the manipulated point cloud is reversible to its previous state and is slightly improved compared to the original version, since small errors that came into account by imprecise assumptions, sensor noise and calibration errors are removed as well.

On the Use of Quantum Thermal Bath in Unimolecular Fragmentation Simulations

2019 ◽  
Author(s):  
Riccardo Spezia ◽  
Hichem Dammak
Keyword(s):  
Degrees Of Freedom ◽  
Molecular Simulations ◽  
Zero Point ◽  
Thermal Bath ◽  
Unimolecular Dissociation ◽  
Point Energy ◽  
Rotational Degrees Of Freedom ◽  
The Difference ◽  
Nuclear Effects

<div> <div> <div> <p>In the present work we have investigated the possibility of using the Quantum Thermal Bath (QTB) method in molecular simulations of unimolecular dissociation processes. Notably, QTB is aimed in introducing quantum nuclear effects with a com- putational time which is basically the same as in newtonian simulations. At this end we have considered the model fragmentation of CH4 for which an analytical function is present in the literature. Moreover, based on the same model a microcanonical algorithm which monitor zero-point energy of products, and eventually modifies tra- jectories, was recently proposed. We have thus compared classical and quantum rate constant with these different models. QTB seems to correctly reproduce some quantum features, in particular the difference between classical and quantum activation energies, making it a promising method to study unimolecular fragmentation of much complex systems with molecular simulations. The role of QTB thermostat on rotational degrees of freedom is also analyzed and discussed. </p> </div> </div> </div>

The Development of a Myoelectric Training Tool for Above-Elbow Amputees

2012 ◽  
Vol 6 (1) ◽  
pp. 5-15 ◽  
Author(s):  
Michael R Dawson ◽  
Farbod Fahimi ◽  
Jason P Carey
Keyword(s):  
Degrees Of Freedom ◽  
Learning Task ◽  
Robotic Arm ◽  
Signal Acquisition ◽  
Myoelectric Prosthesis ◽  
Training Tool ◽  
Myoelectric Prostheses ◽  
Training Systems ◽  
Targeted Muscle Reinnervation

The objective of above-elbow myoelectric prostheses is to reestablish the functionality of missing limbs and increase the quality of life of amputees. By using electromyography (EMG) electrodes attached to the surface of the skin, amputees are able to control motors in myoelectric prostheses by voluntarily contracting the muscles of their residual limb. This work describes the development of an inexpensive myoelectric training tool (MTT) designed to help upper limb amputees learn how to use myoelectric technology in advance of receiving their actual myoelectric prosthesis. The training tool consists of a physical and simulated robotic arm, signal acquisition hardware, controller software, and a graphical user interface. The MTT improves over earlier training systems by allowing a targeted muscle reinnervation (TMR) patient to control up to two degrees of freedom simultaneously. The training tool has also been designed to function as a research prototype for novel myoelectric controllers. A preliminary experiment was performed in order to evaluate the effectiveness of the MTT as a learning tool and to identify any issues with the system. Five able-bodied participants performed a motor-learning task using the EMG controlled robotic arm with the goal of moving five balls from one box to another as quickly as possible. The results indicate that the subjects improved their skill in myoelectric control over the course of the trials. A usability survey was administered to the subjects after their trials. Results from the survey showed that the shoulder degree of freedom was the most difficult to control.

scholarly journals Assessing Wildfire Burn Severity and Its Relationship with Environmental Factors: A Case Study in Interior Alaska Boreal Forest

2021 ◽  
Vol 13 (10) ◽  
pp. 1966
Author(s):  
Christopher W Smith ◽  
Santosh K Panda ◽  
Uma S Bhatt ◽  
Franz J Meyer ◽  
Anushree Badola ◽  
...  
Keyword(s):  
Boreal Forest ◽  
Ground Truth ◽  
Burn Severity ◽  
Classification Methods ◽  
Spectral Indices ◽  
Ground Truth Data ◽  
Burn Scar ◽  
Interior Alaska ◽  
Remote Sensing Methods ◽  
The Relationship

In recent years, there have been rapid improvements in both remote sensing methods and satellite image availability that have the potential to massively improve burn severity assessments of the Alaskan boreal forest. In this study, we utilized recent pre- and post-fire Sentinel-2 satellite imagery of the 2019 Nugget Creek and Shovel Creek burn scars located in Interior Alaska to both assess burn severity across the burn scars and test the effectiveness of several remote sensing methods for generating accurate map products: Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), and Random Forest (RF) and Support Vector Machine (SVM) supervised classification. We used 52 Composite Burn Index (CBI) plots from the Shovel Creek burn scar and 28 from the Nugget Creek burn scar for training classifiers and product validation. For the Shovel Creek burn scar, the RF and SVM machine learning (ML) classification methods outperformed the traditional spectral indices that use linear regression to separate burn severity classes (RF and SVM accuracy, 83.33%, versus NBR accuracy, 73.08%). However, for the Nugget Creek burn scar, the NDVI product (accuracy: 96%) outperformed the other indices and ML classifiers. In this study, we demonstrated that when sufficient ground truth data is available, the ML classifiers can be very effective for reliable mapping of burn severity in the Alaskan boreal forest. Since the performance of ML classifiers are dependent on the quantity of ground truth data, when sufficient ground truth data is available, the ML classification methods would be better at assessing burn severity, whereas with limited ground truth data the traditional spectral indices would be better suited. We also looked at the relationship between burn severity, fuel type, and topography (aspect and slope) and found that the relationship is site-dependent.

scholarly journals Automatic Evaluation of Wheat Resistance to Fusarium Head Blight Using Dual Mask-RCNN Deep Learning Frameworks in Computer Vision

2020 ◽  
Vol 13 (1) ◽  
pp. 26
Author(s):  
Wen-Hao Su ◽  
Jiajing Zhang ◽  
Ce Yang ◽  
Rae Page ◽  
Tamas Szinyei ◽  
...  
Keyword(s):  
Fusarium Head Blight ◽  
Ground Truth ◽  
Wheat Breeding ◽  
Head Blight ◽  
Detection Rates ◽  
Ground Truth Data ◽  
Resistant Cultivars ◽  
Feature Pyramid ◽  
Rater Error ◽  
Wheat Lines

In many regions of the world, wheat is vulnerable to severe yield and quality losses from the fungus disease of Fusarium head blight (FHB). The development of resistant cultivars is one means of ameliorating the devastating effects of this disease, but the breeding process requires the evaluation of hundreds of lines each year for reaction to the disease. These field evaluations are laborious, expensive, time-consuming, and are prone to rater error. A phenotyping cart that can quickly capture images of the spikes of wheat lines and their level of FHB infection would greatly benefit wheat breeding programs. In this study, mask region convolutional neural network (Mask-RCNN) allowed for reliable identification of the symptom location and the disease severity of wheat spikes. Within a wheat line planted in the field, color images of individual wheat spikes and their corresponding diseased areas were labeled and segmented into sub-images. Images with annotated spikes and sub-images of individual spikes with labeled diseased areas were used as ground truth data to train Mask-RCNN models for automatic image segmentation of wheat spikes and FHB diseased areas, respectively. The feature pyramid network (FPN) based on ResNet-101 network was used as the backbone of Mask-RCNN for constructing the feature pyramid and extracting features. After generating mask images of wheat spikes from full-size images, Mask-RCNN was performed to predict diseased areas on each individual spike. This protocol enabled the rapid recognition of wheat spikes and diseased areas with the detection rates of 77.76% and 98.81%, respectively. The prediction accuracy of 77.19% was achieved by calculating the ratio of the wheat FHB severity value of prediction over ground truth. This study demonstrates the feasibility of rapidly determining levels of FHB in wheat spikes, which will greatly facilitate the breeding of resistant cultivars.

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