scholarly journals Real-time vision-based grasping randomly placed object by low-cost robotic arm using surf algorithm

2020 ◽  
Vol 938 ◽  
pp. 012008
Author(s):  
A Beyhan ◽  
N G Adar
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Robotic Arm

scholarly journals A Deep Learning Approach for Robotic Arm Control using Brain-Computer Interface

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Brain Computer Interface ◽  
Low Cost ◽  
Hand Movement ◽  
Substantial Improvement ◽  
Electrode System ◽  
Computer Interface ◽  
Robotic Arm ◽  
Robotic Hand

Brain-Computer Interface (BCI) is atechnology that enables a human to communicate with anexternal stratagem to achieve the desired result. This paperpresents a Motor Imagery (MI) – Electroencephalography(EEG) signal based robotic hand movements of lifting anddropping of an external robotic arm. The MI-EEG signalswere extracted using a 3-channel electrode system with theAD8232 amplifier. The electrodes were placed on threelocations, namely, C3, C4, and right mastoid. Signalprocessing methods namely, Butterworth filter and Sym-9Wavelet Packet Decomposition (WPD) were applied on theextracted EEG signals to de-noise the raw EEG signal.Statistical features like entropy, variance, standarddeviation, covariance, and spectral centroid were extractedfrom the de-noised signals. The statistical features werethen applied to train a Multi-Layer Perceptron (MLP) -Deep Neural Network (DNN) to classify the hand movementinto two classes; ‘No Hand Movement’ and ’HandMovement’. The resultant k-fold cross-validated accuracyachieved was 85.41% and other classification metrics, suchas precision, recall sensitivity, specificity, and F1 Score werealso calculated. The trained model was interfaced withArduino to move the robotic arm according to the classpredicted by the DNN model in a real-time environment.The proposed end to end low-cost deep learning frameworkprovides a substantial improvement in real-time BCI.

Robotic arm- Automated real time object detection

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Low Cost ◽  
Robotic System ◽  
Human Operator ◽  
Robotic Arm ◽  
Time Out ◽  
Precision And Accuracy ◽  
Increase Efficiency

In robotic industry, today, interactions between human and machine usually consists of programming and maintaining machine using human operator. Using a robotic system in any industry for work provides precision and a certain level of accuracy. A robotic entity such as a robotic arm will not ask for time out and can work efficiently day and night which will in turn increase efficiency in workplace. In this paper, we have explained about an arm created, which works in such a way that while the robotic arm is working, camera is able to identify any object it sees which is taken care by the worker looking over the arm. The major outcome and result is the increased efficiency in workplace, precision and accuracy in low cost which can also be used for house hold chores too.

scholarly journals An Autonomous Laser Kirigami Method With Low-Cost Real-Time Vision-Based Surface Deformation Feedback System

2021 ◽  
Author(s):  
Zhujiang Wang ◽  
Zimo Wang ◽  
Woo-Hyun Ko ◽  
Ashif Sikandar Iquebal ◽  
Vu Nguyen ◽  
...  
Keyword(s):  
Control System ◽  
Real Time ◽  
Monitoring System ◽  
Surface Deformation ◽  
Low Cost ◽  
Laser System ◽  
Deformation Monitoring ◽  
Robotic Arm ◽  
Geometric Feature ◽  
Conventional Laser

Abstract We introduce an autonomous laser kirigami technique, a novel custom manufacturing machine system which functions somewhat similar to a photocopier. This technique is capable of creating functional freeform shell structures using cutting and folding (kirigami) operations on sheet precursors. Conventional laser kirigami techniques are operated manually and rely heavily on precise calibrations. However, it is unrealistic to design and plan out the process (open loop) to realize arbitrary geometric features from a wide variety of materials. In our work, we develop and demonstrate a completely autonomous system, which is composed of a laser system, a 4-axis robotic arm, a real-time vision-based surface deformation monitoring system, and an associated control system. The laser system is based on the Lasersaur, which is a 120-Watt CO2 open source laser cutter. The robotic arm is employed to precisely adjust the distance between a workpiece and the laser lens so that a focused and defocused laser beam can be used to cut and fold the workpiece respectively. The four-axis robotic arm provides flexibility for expanding the limits of possible shapes, compared to conventional laser machine setups where the workpiece is fixed on rigid holders. The real-time vision-based surface deformation monitoring system is composed of four low-cost cameras, an integrated AI-assisted algorithm, and the sensors (detachable planar markers) mounted on the polymer-based sheet precursors, and allows real-time monitoring of the sheet forming process and geometric evolution with a geometric feature estimation error less than 5 % and delay time around 100ms. The developed control system manages the laser power, the laser scanning speed, the motion of the robotic arm based on the designed plan as well as the close-loop feedback provided by the vision-based surface deformation monitoring system. This cyber-physical kirigami platform can operate a sequence of cutting and folding processes in order to create kirigami objects. Hence, complicated kirigami design products with various different polygonal structures can be realized by undergoing sequential designed laser cuts, and bends (at any folding angles within designed geometric tolerance) using this autonomous kirigami platform.

scholarly journals Motion tracking glove for augmented reality and virtual reality

2019 ◽  
Vol 10 (1) ◽  
pp. 160-166 ◽  
Author(s):  
Vu Trieu Minh ◽  
Nikita Katushin ◽  
John Pumwa
Keyword(s):  
Virtual Reality ◽  
Remote Control ◽  
Augmented Reality ◽  
Real Time ◽  
Motion Tracking ◽  
Low Cost ◽  
Robotic Arm ◽  
Measurement Unit ◽  
Movement Data ◽  
Internal Measurement

AbstractThis project designs a smart glove, which can be used for motion tracking in real time to a 3D virtual robotic arm in a PC. The glove is low cost with the price of less than 100 € and uses only internal measurement unit for students to develop their projects on augmented and virtual reality applications. Movement data from the glove is transferred to the PC via UART DMA. The data is set as the motion reference path for the 3D virtual robotic arm to follow. APID feedback controller controls the 3D virtual robot to track exactly the haptic glove movement with zero error in real time. This glove can be used also for remote control, tele-robotics and tele-operation systems.

LOW COST REAL TIME ROCKET TELEMETRY SYSTEM DESIGN

2019 ◽  
Author(s):  
Gabriel de Almeida Souza ◽  
Larissa Barbosa ◽  
Glênio Ramalho ◽  
Alexandre Zuquete Guarato
Keyword(s):  
System Design ◽  
Real Time ◽  
Low Cost ◽  
Telemetry System

Low-Cost High-Speed Techniques for Real-Time Simulation of Power Electronic Systems

2007 ◽  
Author(s):  
R. E. Crosbie ◽  
J. J. Zenor ◽  
R. Bednar ◽  
D. Word ◽  
N. G. Hingorani
Keyword(s):  
Real Time ◽  
High Speed ◽  
Low Cost ◽  
Electronic Systems ◽  
Power Electronic ◽  
Real Time Simulation ◽  
Time Simulation

scholarly journals Application of Deep Learning in Integrated Pest Management: A Real-Time System for Detection and Diagnosis of Oilseed Rape Pests

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Yong He ◽  
Hong Zeng ◽  
Yangyang Fan ◽  
Shuaisheng Ji ◽  
Jianjian Wu
Keyword(s):  
Deep Learning ◽  
Integrated Pest Management ◽  
Pest Management ◽  
Real Time ◽  
Oilseed Rape ◽  
Data Augmentation ◽  
Low Cost ◽  
Response Speed ◽  
Original Model ◽  
Real Time System

In this paper, we proposed an approach to detect oilseed rape pests based on deep learning, which improves the mean average precision (mAP) to 77.14%; the result increased by 9.7% with the original model. We adopt this model to mobile platform to let every farmer able to use this program, which will diagnose pests in real time and provide suggestions on pest controlling. We designed an oilseed rape pest imaging database with 12 typical oilseed rape pests and compared the performance of five models, SSD w/Inception is chosen as the optimal model. Moreover, for the purpose of the high mAP, we have used data augmentation (DA) and added a dropout layer. The experiments are performed on the Android application we developed, and the result shows that our approach surpasses the original model obviously and is helpful for integrated pest management. This application has improved environmental adaptability, response speed, and accuracy by contrast with the past works and has the advantage of low cost and simple operation, which are suitable for the pest monitoring mission of drones and Internet of Things (IoT).

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