1. Using Neural Networks to Improve the Performance of an Autonomous Vehicle By Jon Cory and Matt Edwards

2. Our Senior Design Project  Miniature autonomous vehicle that navigates an indoor maze  For this project we decided to study how neural networks could be used to improve performance  Neural networks may be useful to the project but too complex for our current application

3. Current Block Diagram without Neural Networks  Takes in data from sensors on front and both sides  Processes data and determines course of action  Sends PWM signal to stepper motors

4. Capabilities of NN vs. Typical Computers Typical Computer Design Neural Network Design Fast arithmetic Interacting with noisy data or data from the environment Doing precisely what the programmer wants them to do Massive Parallelism Fault tolerance Adapts to circumstances Table taken from: http://www.cs.stir.ac.uk/~lss/NNIntro/InvSlides.html

5. Basics of Neural Networks  Artificial neural networks resemble the brain  Defined by many simple processors (units) running in parallel  Units operate only on local info  Each element operates asynchronously therefore there is no overall system clock  In the end the value is chosen with the greatest degree of confidence

6. Rosenblatt’s Paradigm  Describe NN in terms of sensory unit, association unit, response unit  Perceptrons model artificial neurons, the simplest level of brain function  Perceptrons are trained to recognize patterns

7. Vehicle Behavior  Current plan  Follow the right-hand wall  Slow trial and error  No improvement with repeated trips through the maze  Simple logic  Using neural networks  New method, add a CCD camera  Landmarks must be present in maze  Vehicle reads landmarks and follows them  Must be able to recognize landmarks

8. Modifications Add landmarks to maze Add landmarks to maze Add a b/w CCD camera to vehicle Add a b/w CCD camera to vehicle Camera views landmark (sign) Camera views landmark (sign) Compares to known possible signs Compares to known possible signs Makes turn based on sign Makes turn based on sign

9. Depth Perception Based on Size  Vehicle must determine landmark type and size to make the turn at the right intersection  Sign must appear to be the right size, indicating that the car is the proper distance away  Actual sign size can be manipulated to direct car to proper intersection

10. Landmark Recognition  Multi-layer perceptron used to recognize landmarks  Input layers receive data  Hidden layers make intermediate calculations  Different weights given to each input  Activation value calculated at each neuron and passed on  Landmark is interpreted according to which signal is weighted the heaviest

11. Maze Example  Car enters hall way and sees sign  Circle is too small so it carries on  At first junction, circle is still too small, car moves on  When car reaches second intersection, circle is proper size  Neural network takes action and determines the type of sign  Car turns in accordance with sign