Self Learning Robot-Arm-de all basice AI,Kinematics

Robotic Arm: An Overview

A robotic arm is an advanced mechanical device designed to perform tasks with precision and efficiency, mimicking the functions of a human arm. These systems have become integral to various industries, including manufacturing, healthcare, and research, thanks to their ability to automate repetitive tasks, improve accuracy, and enhance productivity.

Components of a Robotic Arm
Base: The foundation of the robotic arm, typically fixed to a surface. It provides stability and support.

Joints: These connect the different segments of the arm, allowing for movement. There are various types of joints:

Revolute joints: Allow rotational movement.
Prismatic joints: Enable linear movement.
Links: The segments of the arm between joints. They can vary in length and design based on the application.

End Effector: The tool or device at the end of the robotic arm, which can be a gripper, a welding torch, a camera, or any other tool required for specific tasks.

Actuators: These are responsible for movement and can be electric motors, hydraulic systems, or pneumatic devices that drive the joints.

Sensors: Integrated sensors provide feedback about the arm’s position, force, and environmental conditions. Common sensors include encoders, accelerometers, and proximity sensors.

Control System: The brain of the robotic arm, usually a computer or microcontroller that processes input from sensors and sends commands to the actuators.

Types of Robotic Arms
Industrial Robotic Arms: Used in manufacturing for tasks like assembly, welding, and painting. They are highly specialized and optimized for efficiency.

Collaborative Robots (Cobots): Designed to work alongside humans, these arms are equipped with safety features to minimize risks.

Medical Robotic Arms: Used in surgeries and rehabilitation. They offer high precision and can assist surgeons in performing complex procedures.

Research and Development Arms: Employed in laboratories for experiments, these arms can be customized for various tasks, including material handling and testing.

Applications
Manufacturing: Robotic arms streamline production lines, reduce labor costs, and enhance product quality through consistent performance.

Healthcare: In surgical procedures, robotic arms enable minimally invasive techniques, improving recovery times and precision.

Logistics: Automated arms in warehouses help with sorting, packing, and transporting goods efficiently.

Agriculture: Used for planting, harvesting, and monitoring crops, increasing productivity and reducing the need for manual labor.

Entertainment and Art: Robotic arms are utilized in creating art, performing in shows, and even animating characters in films.

Technological Advances
Recent advancements in artificial intelligence and machine learning have significantly enhanced the capabilities of robotic arms. These technologies allow for:

Adaptive Learning: Robotic arms can learn from their environments and improve their performance

1. Self-Learning Robot Arm Gujarat Technological University, Chandkheda, Ahmedabad L D College of Engineering,  Ahmedabad Sr. no Name Enrollment no. 1 Jadav harsh (Team Leader) 220280141014 2 Milan Isac 220280141026 3 Purvin Desai 220280141007 4 Kaklotar Rushabh 220280141018 Prepared as a part of subject : Design  Engineering 1-B (3140005) Prof. M. Y.  Patil (Faculty Guide) Prof. V. B. Patel (Head of Department)

2. Introduction of Robot-Arm

3. What is Robotics Arm ? A Robotic Arm is a type of mechanical arm, usually programmable, that functions similarly to a human arm. It consists of multiple segments connected by joints that allow movement along various axis. Robotic arms can be used for a wide range of applications, from industrial automation to medical procedures.​ Ex: welding, etc.​

4. Types of Robot-Arm Articulated robot arm  Cartesian robot arm SCARA (Selective Compliance Assembly Robot Arm) Robotic Arms Delta robotic arm Collaborative robot arm

5. Applications Industrial Automation Assembly : Welding :  Painting :   Material Handling : Medical Field Surgery : Rehabilitation : Pharmacy :

6. Research and Development Laboratory Automation :  Prototyping : Agriculture Harvesting : Planting Monitoring :  Service Industry Customer Service :  Housekeeping :  Entertainment Animatronics :  Art and Sculpture : 

7. Advantages Precision and Accuracy Robotic arms can perform tasks with a level of precision that surpasses human capability, leading to higher quality products and outcomes. Efficiency and Productivity They operate continuously without fatigue, significantly increasing productivity and throughput in various industries. Safety By taking over dangerous tasks, robotic arms reduce the risk of injury to human workers, improving workplace safety.

8. Consistency and Reliability Robotic arms perform repetitive tasks consistently without variation, ensuring uniformity in production processes. Flexibility and Adaptability Programmable for a wide range of tasks, robotic arms can be reconfigured for different applications, providing flexibility in manufacturing and other fields.

9. Disadvantages High Initial Cost The upfront investment for robotic arms, including purchase, setup, and integration, can be significant, which may be prohibitive for small businesses. Maintenance and Downtime Robotic arms require regular maintenance to ensure optimal performance. Unplanned downtime due to failures can disrupt operations and lead to financial losses. Complexity and Technical Expertise Designing, programming, and maintaining robotic arms require specialized skills and knowledge, necessitating investment in training or hiring skilled personnel.

10. Limited Adaptability in Unstructured Environments While highly effective in controlled environments, robotic arms can struggle with adaptability in unpredictable or unstructured settings where human dexterity and decision-making are advantageous. Job Displacement Automation of tasks traditionally performed by humans can lead to job displacement, necessitating workforce retraining and social adjustments.

11. Some problems which can be solved by with AI Traditional sorting systems based on color sensors struggle with objects of similar shades or complex shapes. Programming complex tasks for robot arms requires specialized knowledge and can be time-consuming. Traditional robot arms struggle to adapt to changes in their environment, like misplaced objects or unexpected obstacles. Robot arms often struggle with delicate tasks requiring precise manipulation of objects. Complex assembly tasks often require both human and robot collaboration, but communication and coordination can be challenging in surgical robots.

12. Literature and Survey

13. While there aren't likely to be surveys directly asking people why robot arms are efficient, there is plenty of data and research available that highlights these benefits. Here are some resources you can explore: Studies and Research Papers: Robot Industries Association (RIA): The RIA publishes research papers and studies on the economic impact of robotics. You can search their website for studies related to robot arm efficiency. ( https://www.automate.org/companies/robotic-industries-association ). Academic Journals: Search for research papers in engineering or robotics journals that focus on robot arm applications and their impact on various factors like productivity and energy consumption. Look for reputable databases like ScienceDirect or IEEE Xplore ( https://www.sciencedirect.com/ ,  https://www.ieee.org/ ).

14. Industry Reports: Manufacturing Industry Reports: Organizations like McKinsey & Company or Boston Consulting Group publish reports on trends in manufacturing automation. These reports often discuss the economic benefits and efficiency gains achieved through robot arm adoption ( https://www.mckinsey.com/ ,  https://www.bcg.com/ ). Manufacturer Websites: Robot Arm Manufacturers: Major robot arm manufacturers like ABB, KUKA, or Yaskawa often have white papers or case studies on their websites that showcase the efficiency benefits of their products in various applications ( https://new.abb.com/products/robotics ,  https://www.kuka.com/ ,  https://www.yaskawa.com/ ).

15. Here are some research papers you might find helpful that explore how AI is changing the landscape of hard-coded robot arms: "AI and Robotics: The New Symbiosis" by Robotics: Science and Systems (RSS) (2018): This paper delves into the integration of AI with traditional robotics, specifically focusing on how AI can enhance the capabilities of robot arms by enabling features like: Learning from experience: Robot arms can learn and improve their performance over time by analyzing data from past tasks. Adaptability to changing environments: AI-powered robots can adjust their movements and strategies based on real-time sensor data from their surroundings. Enhanced decision-making: AI algorithms can enable robot arms to make complex decisions in dynamic environments. Human-robot collaboration: AI can facilitate safer and more efficient collaboration between humans and robots in shared workspaces.

16. Literature and Surveys on Economic Efficiency of Robot Arms: Here are some resources exploring the economic impact of AI-powered robot arms: Economic Efficiency and Cost Analysis: "The Future of Jobs Report 2020" by World Economic Forum: This report analyzes the impact of automation on jobs and the workforce. While not specifically focused on robot arms, it provides valuable insights into the potential cost savings and economic benefits of automation technologies ( https://www.weforum.org/publications/the-future-of-jobs-report-2020/ ). "Robot Industries Association (RIA) Annual Robotics Report": The RIA publishes yearly reports on the state of the robotics industry, including data on robot arm sales, deployment trends, and economic impact. You can find these reports on the RIA website ( https://www.automate.org/robotics ).

17. "Studies by Manufacturing Industry Analyst Firms":  Firms like Boston Consulting Group (BCG) or Deloitte publish reports on trends in manufacturing automation. These reports often discuss the cost-justification and economic benefits of robot arm implementation.  ( https://www.bcg.com/ ,  https://www2.deloitte.com/ ). Literature on Application of AI, Timings, Employee Cost, Maintenance, and Ease of Use: "A Survey on Deep Learning for Robot Arms" by Robotics and Autonomous Systems (2020):  This paper explores how AI is being applied to improve robot arm capabilities. It discusses areas like:  Reduced programming time:  AI algorithms can enable robots to learn tasks autonomously, reducing the need for extensive programming by human engineers. Simplified operation:  AI can facilitate user-friendly interfaces and intuitive control systems for robot arms, making them easier to operate with minimal training. ( https://www.mdpi.com/2414-4088/2/3/57 )

18. "AI for Robotics: A Primer" by Fabio Grasso et al. (2021):  This paper offers a broader perspective on AI in robotics. It highlights:  Reduced maintenance needs:  AI-powered robots can potentially self-diagnose problems and even perform basic maintenance tasks, reducing reliance on human technicians. ( https://link.springer.com/article/10.1186/s41469-019-0050-0 ) Research paper:  ResearchGate Website:  Applied Sciences | Free Full-Text | Vision-Based Robotic Arm Control Algorithm Using Deep Reinforcement Learning for Autonomous Objects Grasping (mdpi.com) Articles:  Microsoft Word - 4029882021273344ICMNWC320.docx (arxiv.org)

19. Objectives

20. Explore the Potential of AI for Automation:   This project aims to investigate how artificial intelligence can be used to enhance the capabilities of robot arms, making them more versatile and user-friendly. Develop a Basic Prototype:  This project will focus on building a simple, AI-powered robot arm that can perform pre-programmed tasks with improved accuracy. Demonstrate the Economic Benefits:   The project will analyse the potential cost savings and efficiency gains achievable by utilizing AI-powered robot arms in specific applications. (For example, faster sorting of recycled materials)

21. Enhance User-friendliness:   This project will explore ways to design an intuitive interface for controlling and interacting with the AI-powered robot arm, making it accessible to users with varying skillsets. Promote Future Development:   This project aims to serve as a stepping stone for further exploration of AI and robotics.  The knowledge gained will be used to develop more advanced functionalities in future iterations.

22. Construction and Working Principle

23. Mechanical structure of Robotic arm  Base : The foundation that supports the arm and allows for rotation if needed. Joints : Points of rotation or translation between segments. Common types include revolute joints (rotational) and prismatic joints (linear). Links : The segments between joints. These act as the "bones" of the arm. End Effector : The tool or device at the end of the arm, such as a gripper, welder, or drill, used to interact with the environment.

24. Control System: Controllers:  These electronic brains receive instructions and translate them into signals for the actuators. - raspberry pi. Actuators:  These are motors or hydraulic/pneumatic systems that generate the physical force to move the arm's joints according to controller commands. - servo mechanisms. Sensors:  These gather data from the environment (like joint positions, object proximity, or force applied) and feed it back to the control system. - camera and encoders.

25. AI Integration: AI Hardware:  Additional processing units might be incorporated to handle complex AI algorithms. Like, raspberry pi model 2_b with external camera. AI Software:  This software implements the AI algorithms that analyze sensor data, make decisions, and send control signals to the actuators. And some deep learning  algorithams . Pre-written code with  openCV ,  tensorflow  and  gpiozero  in python.

26. The site mentioned below taken as reference explains the working principle of the arm and types of the arm used in industries Site: Robotic Arm: Components, Types, Working, Applications & More (universal-robots.com) Path Planning of Robotic arm using Deep Reinforcement Learning: Path planning of robotic arm based on deep reinforcement learning algorithm (wiley.com)

27. Design Calculations

28. Machine learning algorithm Robot-arm kinematics

29. Machine Learning linear regression algorithm  The goal of the linear regression algorithm is to get the  best values for B  and B 1  to find the best fit line. The best fit line is a line that has the least error which means the error should be minimum.

30. Let, m = B1  &  c = B0 then the mean squared error is

31. Robot Kinematics Robot kinematics studies the relationship between the dimensions and connectivity of kinematic chains and the position, velocity and acceleration of each of the links in the robotic system, in order to plan and control movement and to compute actuator forces and torques 

32. Results

33. Training results of linear system. b0  b1

34. Conclusion

35. Conclusion The Robotic arm that is implemented using Raspberry Pi, uses branches of machine learning to pick and place specified objects and aims at avoiding human intervention in the industries where automation can be achieved in a very efficient manner. Due to the implementation of machine learning in the Robotic Arm, it can be used for various purposes. In conclusion, AI-based robotic arms are revolutionizing automation, making industrial processes more efficient, safer, and cost-effective

36. References

37. "AI and Robotics: The New Symbiosis" by Robotics: Science and Systems (RSS) (2018):  https://link.springer.com/article/10.1007/s10506-021-09289-1 "A Survey on Deep Learning for Robot Arms" by Robotics and Autonomous Systems (2020):  https://www.sciencedirect.com/topics/engineering/robotic-arm "Learning Dexterous Manipulation for Industrial Robots with Deep Reinforcement Learning" by Robotics: Science and Systems (2017):  https://arxiv.org/abs/1810.06045 ScienceDirect:  https://www.sciencedirect.com/ IEEE Xplore:  https://www.ieee.org/ JSTOR:  https://www.jstor.org/ SpringerLink:  https://link.springer.com/