The document outlines a project on the design and development of an autonomous mobile robotic arm aimed at performing tasks in various industries with minimal human intervention. It emphasizes the importance of path planning, computer vision, and kinematics in ensuring the robot can operate efficiently and safely in different environments. The project includes objectives such as CAD modeling, simulation, and the integration of sensors for object detection and motion control.

1. "Therationalapproachtolifeistounderstandthat everythingisamatterofdegree,
thatthereareshadesofgrayineverything,andthatnothingiseitherblackorwhite.“
- Steven Novella
‫الرحيم‬ ‫الرحمن‬ ‫هللا‬ ‫بسم‬
(Department of Mechatronics and Biomedical Engineering) Page 1 of 21

2. Project Supervisor
Engr. Imran Shabkhez Sarwar
Team
AHTISHAM AHMED (201108)
SHEHRYAR ALI (201126)
FURQAN KAKAR (201153)
Department of Mechatronics and Biomedical Engineering
Mobile Robotic Arm
Design, Modeling and Motion Control of
Project Number: 2022
Page 2 of 21

3. INTRODUCTION
Autonomous mobile roboticarmsisa devicewith
a mobilebase anda roboticarm designedto perform
taskswithouthuman intervention. They move autonomously,
makingthem highly adaptableto a varietyof tasks
such as:
 Manufacturing plants
 Logistics and Warehouses
 Healthcare
 Military and Security
(Department of Mechatronics and Biomedical Engineering) Page 3 of 21

4. (Department of Mechatronics and Biomedical Engineering) Page 4 of 21

5. Motivation
Minimize Human Intervention required
for the robot to operate
Use AI algorithms to allow the robot to
make intelligent decisions
Reduce costs associated with human
labor
Increase Efficiency, Safety, and
Productivity
Enhance overall Performance of the
robotic arm
Page 5 of 21

6. LITERATURE REVIEW
+ +
+
Page 6 of 21

7. Path Planning
“Path planning is a critical process in autonomous robotics that involves
identifying an optimal path for a robot to follow from its current location
to a desired destination in a given environment”
 Dijkstra’s
 A* algorithm
 SLAM algorithm
 RRT algorithm
Small or Moderately sized environments
Mapping of unknown environment
Large and Complex environments
Page 7 of 21

8. COMPUTER VISION
"Computer Vision is a method for detecting and classifying
objects by using a camera”
Selected Techniques for Object Detection:
 R-CNN
 SSD
 YOLO
 Open CV
 R-CNN is regional based CNN which uses search algorithm to
generate candidate regions of image.
 SSD is Single Shot Detector used for real time object detection.
 YOLO is a algorithm which is based on regression it predicts
classes and bounding boxes of whole image.
 OpenCV (Open Source Computer Vision) is a popular open-
source computer vision library that provides a wide range of
image and video processing functions.
Page 9 of 21

9. Kinematics
Forward Kinematics:
 Denavit-Hartenberg (DH)
 Homogeneous transformations
Inverse Kinematics:
 Geometric methods
 Iterative methods
(Department of Mechatronics and Biomedical Engineering) Page 8 of 21

10. Problem Statement:
“In industries, such as manufacturing and logistics there is a need to
perform repetitive tasks such as material handling and sorting. These
tasks require manual labour, which is time-consuming, prone to errors.
The use of stationary robotic arms has proven to be effective in
automating such tasks; however, it is limited in terms of mobility,
flexibility, and adaptability to different work environments.”
Page 10 of 21

11. Proposed Solution
“A Mobile Robotic Arm which autonomously detects obstacles and
follow the Obstacle Free Path during Pick and Place Operation”
SOLIDWORKS 3D Model
Page 11 of 21

13. Objectives of the Project
 CAD module of Autonomous mobile robotic arm
 Mathematical Analysis on MATLAB
 Simulation on SOLIDWORKS and VREP
 Manufacturing of Robotic Arm
 Manufacturing of Robotic Base
 Integration of Robotic Arm and Base
 Sensors interfacing with Micro-controller
 Object Detection with Image Processing
 Avoiding Obstacle using Path Planning Algorithms
 Motion Control of Robot
Page 12 of 21

14. Page 13 of 21
Page 12 of 21
Flow Chart

15. Page 14 of 21

16.  Dimension of robot chassis = 15*10 inch
 Robotic Arm height = 395mm (15.56
inches)
 Total Number of links = 2
 Total Number of Servo motors = 4
 Degree of Freedom = 4 DOF
 Weight to be carried = (0.5 – 1)kg
 It will do object detection of only One box
 Air vacuum suction for gripping
 The environment for hardware
implementation is room having some
specific obstacles in path.
PRODUCT SPECIFICATIONS:
Page 15 of 21

17. Components Cost in Rs/-
Camera 12K
LIDAR 28K
DC Servo motors 8K
Robotic arm hardware 20K
Battery 4K
Raspberry Pi 4 55K
Mecanum Wheels 5K
Miscellaneous 13k
Body Manufacturing 10K
Total Cost 155K
COST ANALYSIS
Page 16 of 21

18. Page 17 of 21

19. •SDG 3: Good Health and Well-being
•SDG 8: Decent Work and Economic
Growth
•SDG 9: Industry, Innovation, and
Infrastructure
•SDG 11: Sustainable Cities and
Communities
•SDG 13: Climate Action
•SDG 16: Peace, Justice, and Strong
Institutions
Sustainable Development Goals
Page 18 of 21

20. Team INTEGRATED MEN
Ahtisham Ahmed
• Path Planning
• Computer CV
• Mathematical Analysis,
Simulink
• Sensors Interfacing and
object detection
• Accuracy and modification
Shehryar Ali
• Mechanical Design
• Design Calculations
• CAD Model
• Chassis Manufacturing
• Accuracy and
modification
•
Furqan Kakar
• Mathematical Analysis,
Simulink
• Chassis Manufacturing
• Path Planning
• Accuracy and
modification
Page 19 of 21

21. W. Zhiqiang and L. Jun, "A review of object detection based on convolutional network," 2017 36th Chinese Control Conference (CCC), Dalian,
na, 2017, pp. 11104-11109, doi: 10.23919/ChiCC.2017.8029130.
A. Womg, M. J. Shafiee, F. Li and B. Chwyl, "Tiny SSD: A Tiny Single-Shot Detection Deep Convolutional Neural Network for Real-Time Embedded
ject Detection," 2018 15th Conference on Computer and Robot Vision (CRV), Toronto, ON, Canada, 2018, pp. 95-101, doi:
1109/CRV.2018.00023.
Shafiee, Mohammad Javad, et al. "Fast YOLO: A fast you only look once system for real-time embedded object detection in video." arXiv
print arXiv:1709.05943 (2017).
R. Leinhart and J. Maydt, “An extended set of haar-like features for rapid object detection”, in Proc. ICIP, 2002, vol.1, pp. 900 -910.
R. Leinhart and J. Maydt, “An extended set of haar-like features for rapid object detection”, in Proc. ICIP, 2002, vol.1, pp. 900 -910.
M. Nakashima, K. Yano, Y. Maruyama, and H. Yakabe, "The hot line work robot system phase ii and its human-robot interface mos", IEEE, vol. 2,
116-123, 1995.
Karur, K.; Sharma, N.; Dharmatti, C.; Siegel, J.E. A Survey of Path Planning Algorithms for Mobile Robots. Vehicles 2021, 3, 448-468.
ps://doi.org/10.3390/vehicles3030027
Xuexi Zhang, Jiajun Lai, Dongliang Xu, Huaijun Li, Minyue Fu, "2D Lidar-Based SLAM and Path Planning for Indoor Rescue Using Mobile Robots",
rnal of Advanced Transportation, vol. 2020, Article ID 8867937, 14 pages, 2020. https://doi.org/10.1155/2020/8867937
https://robotnik.eu/
] https://www.kuka.com/en-de/products/mobility/mobile-robots
] https://www.mobile-industrial-robots.com/mir-go-applications/robot-arms/astech-projects-robotic-lab-assistant/
] Craig, J. J. (2009). Introduction To Robotics: Mechanics And Control, 3/E. India: Pearson Education.
] Islam, Raza Ul, et al. "An autonomous image-guided robotic system simulating industrial applications." 2012 7th International Conference on
tem of systems engineering (SoSE). IEEE, 2012.
] Iqbal, Jamshed, R. Ul Islam, and Hamza Khan. "Modeling and analysis of a 6 DOF robotic arm manipulator." Canadian Journal on Electrical and
ctronics Engineering 3.6 (2012): 300-306. 27 | P a g e
] Agustian, Indra, et al. "Robot Manipulator Control with Inverse Kinematics PD-Pseudoinverse Jacobian and Forward Kinematics Denavit
rtenberg." arXiv preprint arXiv:2103.10461 (2021).
References
Page 20 of 21

22. Thank you for your
attention!
Any Questions?
Page 21 of 21