1. Vision Based Automation of Steering using Artificial Neural Network Team Members: Sriganesh R. Prabhu Raj Kumar T. Senthil Prabu K. Raghuraman V. Guide: Mr.V. Sugumaran Sr. Lecturer Department of Mechanical Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Ettimadai, Coimbatore.

2. Problem Description The objective of the project is to use a vision based approach to impart intelligence to a vehicle to facilitate automated steering using Artificial Neural Network.

3. Need for Steering Automation Material Handling systems To reduce driver’s mental stress thereby, prevent accidents To mobilise the physically challenged For defense purposes, to avoid damage to human life Planetary exploration Surveillance

4. Why Neural Network? Closest alternative to human intelligence Faster learning compared to other intelligence algorithms Most reliable means of automation High pattern recognition capability Lower processing time after training

5. Project Assumptions The road has a divider line The road is fairly smooth No obstacles on the road The vehicle travels at a constant, low speed

6. Flowchart of the Proposed System Road profile Camera Image MATLAB Black & White Image ANNANN Trained Output Stepper motor driver Steering control

7. Image Processing – RGB to Gray Reading the road image using MATLAB Converting the image to grayscale Gray scale imageJPEG Image

8. Image Processing – Gray to B/W Threshold : 0.75 Threshold : 0.95 Grayscale ImageAdjusted Image

9. Angle Calculation Pixel co-ordinates for the white divider line was obtained Slope was calculated thereby, angle through which steering has to turn was calculated This value would serve as the desired value for training the neural network

10. Image Matrix

11. Online Image Processing A new video input variable was created using adapter of the image acquisition device Object properties such as image grab interval and number of frames per trigger were set The angle was calculated for simultaneous images while training

12. Artificial Neural Network Biological Neuron Artificial Neuron

13. Artificial Neural Network Feed Forward Neural Network Algorithm used : Back propagation algorithm No. of Neurons used :  Input layer : “no. of pixels in image”  Hidden layer: 4  Output layer : 1 Transfer function used:  Input layer : log sigmoid = where,  Output layer : pure linear

14. Network Design Perform function : Sum of Squared Errors Training Parameters:  Goal : 0  Epochs : 50  Bias : +1 Training of network using training sets Simulation of Neural network Adjusting the weights and epochs Interfacing Image processing and ANN

15. Performance Plot Plotting the simulated neural network

16. Network Adjustment Network is adjusted using ‘adapt’ command ‘adapt’ command readjusts weight and bias value to match the desired value Online images are tested to further train the network

17. Result Output Target:-20.8056

18. Results Target : -24.0055 Output : -23.9791 Error : 0.0264 Target : 3.0373 Output : 3.0259 Error : -0.0114 Target : 14.7924 Output : 14.7660 Error : -0.0251

19. Stepper Motor Driver

21. Stepper Motor Specifications Voltage : 24 V Current : 1.3 A Step angle : 7.5 degree Torque : 0.1962 N m

22. Parallel Port Programming First four pins for data writing The sequence of binary numbers determine the direction of rotation The series for anti-clockwise motion of the motor is as follows: 0101 2 (5) 1001 2 (9) 1010 2 (10) 0110 2 (6)

23. System Testing 1 Testing the system with online images Fitting the motor to the vehicle’s steering rod Balancing the vehicle by providing side wheels Tuning of acceleration DC supply circuit for motor Calculation of torque  Torque required : 0.235 N m

24. System Testing 2 Result 1 Result 2

25. Future Scope Obstacles could be sensed and the vehicle guided accordingly The acceleration and braking systems could be controlled in accordance with the road conditions Manual overriding can be adopted in case of road junctions to make decisions

26. Thank You!