scholarly journals Vision-Guided Recognition Method for Working State of Robot Arm based on Machine Learning

CONVERTER ◽  
2021 ◽  
pp. 761-769
Author(s):  
Peibo Li, Peixing Li
Keyword(s):  
Machine Learning ◽  
Control Method ◽  
Parametric Method ◽  
Recognition System ◽  
Robot Arm ◽  
Positioning Control ◽  
State Recognition ◽  
Design Requirements ◽  
Control Instruction ◽  
Inverse Operation

To realize the capture of the target in any position within the working range of the robot arm, a vision-guided target recognition and positioning control method based on machine learning was proposed to improve the accuracy of the working state recognition. The system grabs four different contours of workpieces (triangle, pentagon, round, square and place in the designated area as the task, and by using the MATLAB to process the image information, and all the connected domains are marked bythe neighborhood areamarking algorithm. Later, the logarithmic coordinates-Fourier transform template matchingmethod is adopted to identify workpiece types and extract their centroid as the positioning reference coordinates.Combined with the 3-DOF robot arm, the standard D-H parametric method is usedto establishthe robot arm kinematics model, and through the inverse operation of the robot armand according to the position of the workpiece coordinates,the joint angle of each robot arm can be obtained. Later, it is sent to the lower microcontroller Arduino through a serial port, and then the control instruction is completed by the Arduino torealize the capture and placement of the workpiece and complete the work state recognition task.The experimental results show that the working state recognition system can meet the design requirements.

scholarly journals Development of Continuum Robot Arm and Gripper for Harvesting Cherry Tomatoes

2019 ◽  
Author(s):  
Azamat Yeshmukhametov ◽  
Koichi Koganezawa ◽  
Zholdas Buribayev ◽  
Yedilkhan Amirgaliyev ◽  
Yoshio Yamamoto
Keyword(s):  
Machine Learning ◽  
Recognition System ◽  
Stem Cutting ◽  
Robot Arm ◽  
Design And Development ◽  
Challenging Issue ◽  
Continuum Robot ◽  
Unstructured Environment ◽  
Backbone Structure ◽  
Cherry Tomatoes

Designing and development of agricultural robot is always a challenging issue, because of robot intends to work an unstructured environment and at the same time, it should be safe for the surrounded plants. Therefore, traditional robots cannot meet the high demands of modern challenges, such as working in confined and unstructured workspaces. Based on current issues, we developed a new tomato harvesting wire-driven discrete continuum robot arm with a flexible backbone structure for working in confined and extremely constrained spaces. Moreover, we optimized a tomato detaching process by using newly designed gripper with passive stem cutting function. Moreover, by designing the robot we also developed ripe tomato recognition by using machine learning. This paper explains the proposed continuum robot structure, gripper design, and development of tomato recognition system.

scholarly journals Local mortality estimates during the COVID-19 pandemic in Italy

2021 ◽  
Author(s):  
Augusto Cerqua ◽  
Roberta Di Stefano ◽  
Marco Letta ◽  
Sara Miccoli
Keyword(s):  
Machine Learning ◽  
Excess Mortality ◽  
Control Method ◽  
Local Level ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Mortality Data ◽  
Official Method ◽  
Learning Techniques ◽  
Mortality Estimates

AbstractEstimates of the real death toll of the COVID-19 pandemic have proven to be problematic in many countries, Italy being no exception. Mortality estimates at the local level are even more uncertain as they require stringent conditions, such as granularity and accuracy of the data at hand, which are rarely met. The “official” approach adopted by public institutions to estimate the “excess mortality” during the pandemic draws on a comparison between observed all-cause mortality data for 2020 and averages of mortality figures in the past years for the same period. In this paper, we apply the recently developed machine learning control method to build a more realistic counterfactual scenario of mortality in the absence of COVID-19. We demonstrate that supervised machine learning techniques outperform the official method by substantially improving the prediction accuracy of the local mortality in “ordinary” years, especially in small- and medium-sized municipalities. We then apply the best-performing algorithms to derive estimates of local excess mortality for the period between February and September 2020. Such estimates allow us to provide insights about the demographic evolution of the first wave of the pandemic throughout the country. To help improve diagnostic and monitoring efforts, our dataset is freely available to the research community.

scholarly journals Feasibility of Using Floor Vibration to Detect Human Falls

2020 ◽  
Vol 18 (1) ◽  
pp. 200
Author(s):  
Yu Shao ◽  
Xinyue Wang ◽  
Wenjie Song ◽  
Sobia Ilyas ◽  
Haibo Guo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Human Body ◽  
Nearest Neighbor ◽  
Body Movement ◽  
Modern Society ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
K Nearest Neighbor ◽  
Study Results ◽  
Floor Vibration

With the increasing aging population in modern society, falls as well as fall-induced injuries in elderly people become one of the major public health problems. This study proposes a classification framework that uses floor vibrations to detect fall events as well as distinguish different fall postures. A scaled 3D-printed model with twelve fully adjustable joints that can simulate human body movement was built to generate human fall data. The mass proportion of a human body takes was carefully studied and was reflected in the model. Object drops, human falling tests were carried out and the vibration signature generated in the floor was recorded for analyses. Machine learning algorithms including K-means algorithm and K nearest neighbor algorithm were introduced in the classification process. Three classifiers (human walking versus human fall, human fall versus object drop, human falls from different postures) were developed in this study. Results showed that the three proposed classifiers can achieve the accuracy of 100, 85, and 91%. This paper developed a framework of using floor vibration to build the pattern recognition system in detecting human falls based on a machine learning approach.

Study and Implement on Key Technologies of Ship-Welding Mobile Robot

2010 ◽  
Vol 44-47 ◽  
pp. 321-325
Author(s):  
Liang Hua ◽  
Lin Lin Lv ◽  
Ju Ping Gu ◽  
Yu Jian Qiang
Keyword(s):  
Mobile Robot ◽  
Control Method ◽  
Structure Design ◽  
Seam Tracking ◽  
Precision Positioning ◽  
Positioning Control ◽  
Controlling System ◽  
Welding Torch ◽  
Driven System ◽  
And Control

The key technilogies of ship-welding mobile robot applied to ship-building in plane block production line were researched and realized. The mechanical structure design of the robot was completed. The motion-controlling system of of two-wheel differential driving mobile robot was developed. A novel precision positioning control method of welding torch using ultrasonic motors was putforward. The mechanism and control-driven system of precision positioning system for welding torch were completed. The platform of obstacle avoidance navigation system was designed and the strategies of seam tracking, trajectory and posture adjustment were preliminary studied. The methods and results put forward in the paper could act as the base of deep research on the theories and technologies of ship-welding mobile robot.

A compliant control method for disassembly of non-elastic parts using realised motion

Robotica ◽  
1995 ◽  
Vol 13 (6) ◽  
pp. 591-598 ◽  
Author(s):  
Yagmur Denizhan
Keyword(s):  
Control Method ◽  
Recognition System ◽  
Automatic Recognition ◽  
Human Operator ◽  
Control Methods ◽  
Compliant Control ◽  
On Line

SummaryIn disassembly tasks, due to the large variety of objects and the different positions and orientations in which they appear, the disassembly trajectories supplied on-line by a human operator or an automatic recognition system can contain large errors. The classical compliant control methods turn out to be insufficient to eliminate sticking which is due to these errors. This paper presents a compliant control method for disassembly of non-elastic parts in non-elastic environments which adopts the trajectories according to realised motion. In case of sticking a new direction of motion is searched for until the manipulated part is set into motion.

scholarly journals Long-Stroke Nanopositioning Stage Driven by Piezoelectric Motor

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yong Wang ◽  
Fujun Sun ◽  
Junhui Zhu ◽  
Ming Pang ◽  
Changhai Ru
Keyword(s):  
High Speed ◽  
Control Method ◽  
Laser Vibrometer ◽  
Piezoelectric Motor ◽  
Backward Error ◽  
Positioning Control ◽  
Ac Drive ◽  
Variable Friction ◽  
Long Stroke ◽  
Speed Characteristic

This paper reported a biaxial nanopositioning stage single-driven by piezoelectric motor. The employed piezoelectric motor can perform two different driving modes, namely, AC drive mode to drive in long-stroke and at high-speed and DC scanning mode with the high-resolution of several nanometers, which satisfies the requirements of both long-stroke and nanoresolution. To compensate for the effects of the variable friction force and some unpredictable disturbances, a novel backward error compensation (BEC) positioning control method integrated of the two driving modes and a double closed-loop PID controller system are proposed to obtain a high-accuracy positional motion. The experiment results demonstrate that the nanopositioning stage with large travel range of 300 mm × 300 mm has a fine speed characteristic and resolution is 5 nm. In the experiments of different travels up to 15 mm, calibrated by a commercial laser vibrometer, the positioning accuracy is proved within 55 nm inx-axis and 40 nm iny-axis with standard deviation less than 40 nm inx-axis and 30 nm iny-axis and the final position locking can be limited to 10 nm, meeting the requirements of micromanipulation technology.

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