PFE presentation

1. Graduation Project
Academic supervisor: Mr. Amir GARGOURI
Industrial supervisor: Mr. Zied LOUKIL
Presented by: Hani YOUSFI 2017-2018
Intelligent Navigation Aid
System (NAS)
Telecommunications Engineering

2. Plan
1
2
6
5
4
3
Introduction
Presentation of the company
Hardware and Software environment
Conceptual study
Implementation & Realization
Conclusion and Perspectives
2

3. Introduction
3

4. The number of visually impaired is increasing
Need to rehabilitate them
Classic solutions
Electronic solutions
4
Introduction

5. Guide Dogs White Canes
Classic
Solutions
5
Introduction

6. UltraCane Teletact
Electronic Solutions
6
Introduction

7. The number of visually impaired is increasing
Need to rehabilitate
Available
Solutions
Intelligent Navigation
Aid System
7
Introduction

8. Presentation
of the company
8

9. Founded in 2013
Located in Sfax
Robotics Internet of things
9
Presentation of the company

10. Hardware and Software
environment
10

11. The Navigation Aid System architecture
11
Hardware and Software environment

12. LV-MaxSonar EZ sensors
-6 m detection range
-40 Hz reading rate
-3 output formats: analog, pulse width and serial
12
Hardware environment

13. Raspberry Pi 3
model B
-Cheap
-Small Dimensions
-High Performances
13
Hardware environment

14. MCP3008
-10 bit ADC
-8 different chanels
-Cheap
14
Hardware environment

15. Power voltage regulator
-Input voltage up to 12 V
-Output voltage 5 V
15
Hardware environment

16. 16
Software environment
Raspbian: Official Operating System for the Raspberry
Python: Open source, flexible and easy to use
Weka: Open source Software, Data Analysis and classification

17. Conceptual Study
17

18. Requirements
analysis
Functional
requirements
Non-functional
requirements
Obstacle
detection
Form
recognition
Alerting the
user
Availability
High
performances
Ergonomic
constraints
Non-functional
requirements
18
Conceptual Study

19. Use case Diagram of the Navigation Aid System
19

20. Class diagram of the NAS
20

21. Sequence diagram of the Navigation Aid System
21

22. Realization
22

23. Principal steps of
the realization
1
2
3
4
5
• Take the measures
• Clean the Data
• Extract the features
• Classify the Data
• Build a ML model
23

24. 24
Realization Steps
1 meter

25. Analog Data
25
Realization Steps
Digital Data

26. Principal steps of
the realization
1
2
3
4
5
• Take the measures
• Clean the Data
• Extract the features
• Classify the Data
• Build a ML model
26

27. Data + Noise
Hilbert
filter
Signal’s
envelope
27
Realization Steps

28. Principal steps of
the realization
1
2
3
4
5
• Take the measures
• Clean the Data
• Extract the features
• Classify the Data
• Build a ML model
28

29. Signal’s
envelope
Continuous
Wavelet
Transform
29
Realization Steps

30. Principal steps of
the realization
1
2
3
4
5
• Take the measures
• Clean the Data
• Extract the features
• Classify the Data
• Build a ML model
30

31. Data Classified
Data
31
Realization Steps
Weka

32. 32
Realization Steps

33. Principal steps of
the realization
1
2
3
4
5
• Take the measures
• Clean the Data
• Extract the features
• Classify the Data
• Build a ML model
33

34. 34
Realization Steps
 Create a Python-based model using Scikit Learn
 Feed it with enough data to train it
 Save our trained model using joblib
 Test the model with different data
 Deploy the model in the Raspberry Pi

35. 35

36. 36

37. 37

38. 38

39. Conclusion and
Perspectives
39

40. 40
Developped Conceptual study of the NAS
Took the measures
Processed them within the Raspberry
Tested the Machine Learning algorithms
Built our python-model and deployed it to the Raspberry
Conclusion and perspectives
Conclusion

41. Use the NAS as an extension to other mobile robots
Extend the approach to detect the hardness and
types of materials
Integrate the camera to reinforce the precision
41
Conclusion and perspectives
Perspectives

42. Thank you for your
attention!
42