Safe robot operation using force tracking impedance control The scope of industrial robot applications was established from the c
I have some comments for things I would like to be changed in/added to my presentation:
1) Add real picture of a robot in slide 1 & 2
2) Slide 5 should have the title (Problem Statement), and Slide 6&7 are just continuation
3) Specify that the used method will be Optimization
4) Missing Point should be included that is the Input Data
5) In Slide 8 , the last point is good but the others needs review
6) In Slide 10 change the title to (Literature Review), This Part should be longer with more info
7) In Slide 11, I need some questions to make the methodology clear
8) In Slide 12, some results(Graphs) should be added from other authors and analyse them
9) If there was any extra references used then it should be added to the ppt
Lastly I provided examples along with (( My Presentation(Luna) ))
Safe robot operation using force tracking impedance control
Luna
1
contents
Introduction
Background
Impedance Control
Problem Statement
Challenges
Research Question
Aim
Objectives
Significance of the study
2
Expected Outcomes
Literature Review
Methodology
Expected Results and Discussion
Conclusion
Introduction
The scope of industrial robot applications was established from the conventional handling, assembly and welding tasks leading to a wide range of production.
Such applications involves robot end-effector interaction with environment.
In interaction case, insufficient compliance for manipulator is always a key problem.
Only position control is not sufficient to control and handle the interaction.
It is necessary to develop an interaction control method that achieves position tracking and reliably adapts the force exerted (force tracking) on the environment in order to avoid damage to both environment and the manipulator.
Impedance control is a feasible solution to overcome position uncertainties while robot-environment interaction and avoid large impact forces
3
3
Impedance control
Impedance controller resembles a virtual mass-spring-damper system between the environment and robot end-effector
This allows the robot to safely interacts with the environment.
Mechanical impedance is the ratio of force input to position output.
Impedance shows that how much the body resist the applied forces.
Mass-spring-damper system while interaction
4
Challenges in impedance control
Impedance model is second order equation with desired impedance parameters.
Require optimal tuned parameters to achieve the desired interactive impedance.
To avoid parameters tuning, additional control algorithm is implemented.
This algorithm helps in minimizing the force tracking error.
5
Problem Statement
Research Question
How the impedance control can be improved to enhance robot operation safety while environment interaction.
Research Aim
The aim of this research is to
Improve the force tracking impedance control structure for enhanced end-effector force tracking.
Furthermore, optimally tune the impedance parameter to make the system more sensitive for small force change.
6
Problem Statement
Research Objective
To perform the proposed research, the research objectives are
Review the literature to understand the impedance model
Then update the impedance model in a way that will improve the end-effector force tracking.
Implement machine learning or optimization algorithm to optimally tune the impedance parameters.
Implement and compare the proposed and existing algorithms.
7
Significance of study
For robust robot operation while interaction, integrated position and force control is utilized.
Usually, the position control is focused for improved performance. Whereas for force control, traditional impedance model is used.
Therefore, sometimes impedance results are not precise in the presence of external disturbance.
This research will then contribute specially to impedance control.
This will help in enhance the robot safety specially when human and robot are working together.
8
Expected outcomes
Improvement and Implementation of Impedance Control
Will enhance the robot end-effector trajectory tracking while interaction.
This will improve the safety of robot operation
Resulting in avoiding damage to both the robot and the environment.
9
Optimal impedance parameters
Will make system more sensitive to small end-effector forces.
This will help the system to easily work in sensitive environments.
Impedance Control literature
| Year | Author | Contribution |
| 1985 | Hogan | Hybrid position/force control |
| 1985 | Hogan | Impedance control |
| 1991 | Chan et al. | SMC-based impedance control |
| 1997 | Seraji et al. | Force tracking in impedance control |
| 2003 | Iwasaki et al. | Adaptive force control using sliding mode control |
| 2004 | Jung et al. | Adaptive impedance force tracking under unknown environment |
| 2008 | Lee et al. | Force tracking with variable target stiffness |
| 2018 | Liang et al. | Force tracking with unknown environment via iterative learning algorithm |
| 2019 | Yang et al. | PID-Based force tracking with nonlinear velocity observer |
10
References
N. Hogan, "Impedance control: An approach to manipulation: Part I—Theory," 1985.
N. Hogan, "Impedance control: An approach to manipulation: Part II—Implementation," 1985.
F. J. Abu-Dakka, and M. Saveriano, "Variable impedance control and learning – a review" frontiers in Robotics and AI, vol. 7, 590681, 2020
S. Chan, B. Yao, W. Gao et al., "Robust impedance control of robot manipulators," Robots Automation, vol. 6, no. 4, pp. 220–227, 1991.
J. Peng, Z. Yang, and T. Ma, "Position/force tracking impedance control for robot systems with uncertainties based on adaptive jacobian and neural network," complexity, vol. 2019, p. 1406534.
11
methodology
The methodology involves the following steps
Impedance Model Improvement: Analysis of different control algorithm and their comparison will help in the selection of best algorithm to improve the impedance model.
Optimization of Impedance Parameters: Utilize the machine learning or optimization algorithm such as Particle Swarm Optimization (PSO) for optimal impedance parameters.
Implementation and Comparison: Implement the final algorithms and compare with existing algorithms.
12
Expected Results and discussion
After the research, the results and discussions are expected as
The improved impedance control enhanced the robot end-effector force tracking by converging the force tracking error to zero.
This is because the control algorithm integrated in impedance control provides more energy to the robot.
Furthermore, the optimization technique helped the robot detect the small change in the end-effector force based on changing environment.
This is because the algorithm has optimally tuned the parameters based on the environment dynamics.
13
conclusion
Insufficient compliance while robot-environment contact is always a key problem.
This research presented a basic and easy review about the impedance control.
Mainly, conventional impedance control with adaptive algorithm and impedance control with modified position control loop is utilized.
Improved force control loop was very useful in making the robot operation safer as required.
Optimized desired impedance parameters avoided sudden force impact by detecting small force change.
14
References
N. Hogan, "Impedance control: An approach to manipulation: Part I—Theory," 1985.
N. Hogan, "Impedance control: An approach to manipulation: Part II—Implementation," 1985.
S. Chan, B. Yao, W. Gao et al., "Robust impedance control of robot manipulators," Robots Automation, vol. 6, no. 4, pp. 220–227, 1991.
H. Seraji and R. Colbaugh, "Force tracking in impedance control," The International Journal of Robots Research, vol. 16, no. 1, pp. 97-117, 1997.
M. Iwasaki, N. Tsujiuchi, and T. Koizumi, "Adaptive force control for unknown environment using sliding mode control with variable hyperplane," JSME International Journal Series C Mechanical Systems, Machine Elements and Manufacturing, vol. 46, no. 3, pp. 967-972, 2003.
S. Jung, T. C. Hsia, and R. G. Bonitz, "Force tracking impedance control of robot manipulators under unknown environment," IEEE Transactions on Control Systems Technology, vol. 12, no. 3, pp. 474-483, 2004.
X. Liang, H. Zhao, X. Li, and H. Ding, "Force tracking impedance control with unknown environment via an iterative learning algorithm," in 2018 3rd International Conference on Advanced Robots and Mechatronics (ICARM), 2018: IEEE, pp. 158-164.
J. Peng, Z. Yang, and T. Ma, "Position/force tracking impedance control for robot systems with uncertainties based on adaptive jacobian and neural network," complexity, vol. 2019, p. 1406534.
15
Thank You
16
,
Prediction of Early Childhood Obesity in Saudi Arabia using Machine Learning
1
Outline
Significance of the Study
03
Expected Outcomes
04
01
Introduction and Background
02
Problem Statement
Literature Review
05
Draft Conclusion
08
2
Methodology
06
Expected Results and Discussion
07
Research Question, Aim, Objectives
March 4, 2021 CS3172
3
Introduction and Background
Obesity
Effects
Type 2 Diabetes
Heart
Diseases
Cancers
Strokes
2.1 billion people are obese.
42 million children under the age of 5 were overweight in 2013.
30% of the global population.
41% increase by 2030 if current trend persists.
Risk Factors:
Unhealthy eating patterns
Lack of exercise
Genetics
Psychological factors
Socioeconomic factors
Obesity, a global epidemic.
March 4, 2021 CS3172
M. H. M. Adnan, W. Husain, & Rashid, N. A. (). A framework for childhood obesity classifications and predictions using NBtree. 2011 7th International Conference on Information Technology in Asia, (), 1–6. https://doi.org/10.1109/CITA.2011.5999502
Child Health – Childhood Obesity. (2019, November 21). Www.moh.gov.sa. https://www.moh.gov.sa/en/HealthAwareness/EducationalContent/BabyHealth/Pages/003.aspx
Al-Hussaini, A., Bashir, M., Khormi, M., AlTuraiki, M., Alkhamis, W., Alrajhi, M., & Halal, T. (2019). Overweight and obesity among Saudi children and adolescents: Where do we stand today? Saudi Journal of Gastroenterology, 25(4), 229. https://doi.org/10.4103/sjg.sjg_617_18
Obesity In Saudi Arabia
4
Introduction and Background
Pediatric obesity
Adult obesity
Prevention of childhood obesity is urgently required for reduction in obesity prevalence.
Childhood obesity prevalence was 18.2% in 2019.
March 4, 2021 CS3172
Obesity – Adults (18+ years) | EMRO Regional Health Observatory. (2017). Rho.emro.who.int. https://rho.emro.who.int/ThemeViz/TermID/146
Graph from:
GHO | By category | Prevalence of obesity among adults, BMI ≥ 30, age-standardized – Estimates by country. (2017). WHO. https://apps.who.int/gho/data/node.main.A900A?lang=en
Al-Hussaini, A., Bashir, M., Khormi, M., AlTuraiki, M., Alkhamis, W., Alrajhi, M., & Halal, T. (2019). Overweight and obesity among Saudi children and adolescents: Where do we stand today? Saudi Journal of Gastroenterology, 25(4), 229. https://doi.org/10.4103/sjg.sjg_617_18
^ for childhood rate
5
Introduction and Background
Early Childhood Obesity Prediction System
Diagnosis
Prediction
Classification
Indication
Genetics
BMI
ML
How can we predict?
Machine learning is the study of computer algorithms that improve automatically through experience. It is seen as a part of artificial intelligence.
March 4, 2021 CS3172
High Body Mass Index in Infancy May Predict Severe Obesity in Early Childhood
The prevalence of overweight and obesity has risen alarmingly among Saudi children and adolescents over the past decade and should make a strong case to initiate and monitor effective implementation of obesity prevention measures.
2. Problem Statement
How can Machine Learning be used to predict early childhood obesity in
Saudi Arabia?
6
2.1 Research Question
March 4, 2021 CS3172
2. Problem Statement
7
Construct a Machine Learning model to predict the risk of early childhood obesity in Saudi Arabia.
2.2 Research Aim
March 4, 2021 CS3172
Aim: The aim of the study is to predict early childhood obesity and the reduce it risk.
2. Problem Statement
8
Implement a machine learning algorithm suitable for prediction.
Collect and Analyze the clinical data including weight, height, BMI, socioeconomic conditions.
Compare and contrast developed prediction model with existing ones.
2.3 Research Objectives
Develop a prediction model for early children obesity.
March 4, 2021 CS3172
Three simple factors can predict whether a child is likely to be overweight or obese by the time they reach adolescence: the child's body mass index (BMI), the mother's BMI and the mother's education level, according to our new research.
Predicting obesity in children using clinical data before the age of two using machine learning techniques.
Investigate the ability of the child’s BMI and the mother’s BMI to predict. To relate the calculated BMI of the child and mother.
Advance our understanding towards the development of smart and effective interventions for childhood obesity care. (from another paper)
Develop a prediction model to help clinicians identify candidate children for early obesity interventions, thereby targeting at risk children at a critical age of development related to establishing eating and lifestyle habits.
About data?
Identify
evaluate
3. Significance of the Study
Contribution to vision 2030 goal of Focus on Wellbeing and Preventive Care.
Contribution to SDG 17 goal of Good Health and Well-being.
Helps in early medical intervention to prevent obesity in early stages leading to a healthier society.
9
Client
March 4, 2021 CS3172
Vision 2030: Focus on Wellness and Preventive Care: The Saudi Government has rolled out initiatives focusing on fitness and preventive care. KSA is aiming for a 3 per cent reduction in obesity by 2030.
Contribution to vision 2030 goal of Focus on Wellbeing and Preventive Care.
Contribution to SDG 17 goal of Good Health and Well-being.
Targeting a large group of children suffering from the most widely spread illnesses in the world,
Children who have obesity are more likely to have: High blood pressure and high cholesterol, which are risk factors for cardiovascular disease.Childhood obesity is associated with a higher chance of premature death and disability in adulthood.
Dont let the adults to reached this sitution.
The numbers are increasing
Preventing is better then treatment.
Focusing on these causes may, over time, decrease childhood obesity and lead to a healthier society as a whole.
For healthcare people
4. Expected Outcomes
10
Development of a computer code for early childhood obesity prediction using machine learning techniques
Identification of potentially high-risk children to whom future obesity prevention strategies should be applied
March 4, 2021 CS3172
2. For the clients which are doctors so that they can stop
5. Literature Review
11
Machine Learning
Predicting obesity is a difficult task and machine learning techniques are powerful for this task.
Prediction Models
Data, BMI and genetics, are good predictors and childhood obesity models can be valuable assets to healthcare applications.
Increasing Obesity Rates
Rates of obesity among Saudi children are significantly increasing.
Current models have low accuracy
Several approaches to early childhood obesity are proposed, but they are inaccurate and no research targets Saudi Arabia.
March 4, 2021 CS3172
Several models have been developed
Current models are inaccurate
No prediction models targeting Saudi Arabia’s children.
Only 2 models are currently in use in the US.
Several approaches to early childhood obesity are proposed, but they are inaccurate.
Prediction models can be deduced from risk factors and genetics.
Childhood obesity models can be valuable assets to healthcare applications.
5. Literature Review
12
Research is limited.
Poor prediction rates.
Early-stage point.
Zhang, S., Tjortjis, C., Zeng, X. et al.:
Evaluated: well-known data mining algorithms
Result: SVM and Bayesian algorithms are the best algorithms for predicting obesity.
Universiti Sains Malaysia:
Proposed: hybrid approach using Naïve Bayes and Genetic Algorithm
Result: 75% accuracy improvement over Naive Bayes.
Indiana University:
Test: six ML algorithms
Result: NaiveBayes and BayesNet high accuracy at 85% and sensitivity at 90% respectively.
Universiti Sains Malaysia:
Data mining techniques on data of ages 9 to 11
Proposed: more significant parameters on an existing solution
Result: 21% accuracy improvement.
Gaps
March 4, 2021 CS3172
That after adding more parameters to an existing solution, the algorithm’s result predictions of childhood obesity were 21% more accurate than before adding them
Research is limited: and data used are of ages where obesity is well established and harder to remediate.
Poor prediction rates that differ from test to test.
Early stage point : Research is at an early point as they only recently started predicting obesity using ML where more works have to be done in the future.
6. Methodology
Utilize the amount of clinical data including BMI, family history, and lifestyle habits.
Data
Prediction Model
13
Analysis of different machine learning methods such as RandomTree, RandomForest, Naïve Bayes, and others trained on collected data.
Develop an algorithm to accurately predict obesity of children based on Naive Bayes using Python.
Machine Learning
(Singh & Tawfik, 2020)
March 4, 2021 CS3172
Database and Machine learning
Program???
Singh, B., & Tawfik, H. (2020). Machine Learning Approach for the Early Prediction of the Risk of Overweight and Obesity in Young People. Lecture Notes in Computer Science, 523–535. https://doi.org/10.1007/978-3-030-50423-6_39
Naive Bayes is a simple but surprisingly powerful algorithm for predictive modeling and based on the literature review, Naive Bayes was food to have the highest levels of accuracy among others.
Some intervention measures such as physical activity and healthy eating can be a fundamental component to maintain a healthy lifestyle. Therefore, it is absolutely essential to detect childhood obesity as early as possible.
https://data.humdata.org/dataset/who-data-for-saudi-arabia
https://childmortality.org/data/Saudi%20Arabia
https://data.unicef.org/country/sau/
7. Expected Results and Discussion
14
Submission of a coded program runned using a machine learning algorithm that takes as input children's data before two years old and predict if they are at risk of developing obesity when they get older.
A comparison of accuracy results between proposed developed solution with existing ones that use different machine learning algorithms.
Implementation of the developed program in hospitals to help physicians predict future occurrences of obesity in children.
March 4, 2021 CS3172
Such machine learning models may be valuable as part of a healthcare application that alerts individuals with high risk of obesity incidence in the future.
8. Draft Conclusion
Predicting obesity at an early age is both useful and important because preventive
measures and proper interventions can be applied if the children indicated a high risk of obesity.
The proposed early childhood obesity prediction model:
Implement an ML algorithm called Naive Bayes.
Predict the risk of obesity in early childhood.
15
March 4, 2021 CS3172
Limitations:
Difficult to get data from Saudi Arabia (trying to find global database and maybe late try to localize it)
Thank You
Any Questions?
16
References
Al-Hussaini, A., Bashir, M., Khormi, M., AlTuraiki, M., Alkhamis, W., Alrajhi, M., & Halal, T. (2019). Overweight and obesity among Saudi children and adolescents: Where do we stand today? Saudi Journal of Gastroenterology, 25(4), 229. https://doi.org/10.4103/sjg.sjg_617_18
CDC. (2019). Defining Childhood Obesity. Centers for Disease Control and Prevention. https://www.cdc.gov/obesity/childhood/defining.html
Child Health – Childhood Obesity. (2019, November 21). Www.moh.gov.sa. https://www.moh.gov.sa/en/HealthAwareness/EducationalContent/BabyHealth/Pages/003.aspx
GHO | By category | Prevalence of obesity among adults, BMI ≥ 30, age-standardized – Estimates by country. (2017). WHO. https://apps.who.int/gho/data/node.main.A900A?lang=en
M. H. M. Adnan, W. Husain, & Rashid, N. A. (). A framework for childhood obesity classifications and predictions using NBtree. 2011 7th International Conference on Information Technology in Asia, (), 1–6. https://doi.org/10.1109/CITA.2011.5999502
Obesity – Adults (18+ years) | EMRO Regional Health Observatory. (2017). Rho.emro.who.int. https://rho.emro.who.int/ThemeViz/TermID/146
Saad, A. (2018). Prevention of Childhood Obesity in Saudi Arabia. J Child Obes, 2-002. https://doi.org/10.21767/2572-5394.100057
Singh, B., & Tawfik, H. (2020). Machine Learning Approach for the Early Prediction of the Risk of Overweight and Obesity in Young People. Lecture Notes in Computer Science, 523–535. https://doi.org/10.1007/978-3-030-50423-6_39
Smego, A., Woo, J. G., Klein, J., Suh, C., Danesh Bansal, Bliss, S., Daniels, S. R., Bolling, C., & Crimmins, N. A. (2017). High body mass index in infancy may predict severe obesity in early childhood. The Journal of Pediatrics, 183, 87-93.e1. https://doi.org/https://doi.org/10.1016/j.jpeds.2016.11.020
,
Presenting a prototype of Li-Fi for smart cities
Why Li-Fi and how to improve it ?
Outline
2
Introduction & Background
Problem Statement
Research Question & aim
Significance
Contribution
Methodology
Expected Results
Literature Revi
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