Loading presentation...

Present Remotely

Send the link below via email or IM


Present to your audience

Start remote presentation

  • Invited audience members will follow you as you navigate and present
  • People invited to a presentation do not need a Prezi account
  • This link expires 10 minutes after you close the presentation
  • A maximum of 30 users can follow your presentation
  • Learn more about this feature in our knowledge base article

Do you really want to delete this prezi?

Neither you, nor the coeditors you shared it with will be able to recover it again.


Make your likes visible on Facebook?

Connect your Facebook account to Prezi and let your likes appear on your timeline.
You can change this under Settings & Account at any time.

No, thanks

Autonomous Vehicle

No description

M Manoj Rahul

on 5 May 2015

Comments (0)

Please log in to add your comment.

Report abuse

Transcript of Autonomous Vehicle

The goal of the project is to design and build a low cost, small scale autonomous vehicle using a modified radio control car powered by an IC engine. The vehicle must be able to navigate from a start point to a target destination at its limits and avoid obstacles, while maintaining its dynamic stability at all times, and prevent itself from getting out of control, even when the environment conditions change.
What is an Autonomous Vehicle ?
This thesis will define an autonomous vehicle as a mobile robot that can intelligently navigate and control itself within an environment without any human interaction.
Approach and Solution
Why Autonomous Vehicles ?
Reduced Accidents
Comfortable Ride
Fuel Efficient
Less Traffic
Environment Friendly
Space Exploration
Literature Review
Our Approach
Race Car Driver Approach
One of the inspirations came from learning how race car drivers control a vehicle at its limits.

Race car drivers breakdown their driving tasks into, generating a path that they want to follow and controlling the vehicle at its limits to follow that path.
General Control Structure
Our Control Structure
General Layout
Ultrasonic Sensor
for Terrain Detection
Ultrasonic Sensor
for Terrain Detection
Arduino Mega 2560
Infrared Range Sensor
, mounted on a
is used for Obstacle Detection
Inertial Measurement Unit
provides the Vehicle's Orientation and Acceleration
The Model Car
Adjustable Suspensions
Nitro-Powered, 0.28 Pro 4.6 cc, 2-Stroke Internal Combustion Engine
Terrain Detection
Slows Down
Speeds Up
Object Detection
Occupancy Grid
Triangulation Method
First Prototype
Semi-Automation in Cars
As expected the final prototype looks like this .......
M Manoj Rahul

Satyarth Shankar
We, human beings have always sought new inventions that make our lives simpler. We have always aimed to explore and go where we have not gone before. From these desires the study of autonomous vehicles was born.

This project aims at building an autonomous vehicle on a small budget. The recent invention and advancement of devices like microprocessors and sensors, as well as their decreasing costs, have made the study of autonomous vehicles open to everyone. Vehicle navigation and stabilization is accomplished using an on-board microprocessor, various sensors and actuators mounted on the modified model radio control car chassis. Although, this project is done on a small budget and a small scale, the same concepts and ideas can be applied to larger scale applications. The result from this work provides a platform for developing advanced vehicle control systems and also provides a useful tool for robotics education.
Military Applications
Autonomous Vehicles
Vehicle Architecture
Path Planning
Path Tracking Spaces
Stability Control
Mathematical Modeling
The kinematic model for the all wheel drive, front wheel steered robotic car is derived. Using this model, the controller is given inputs to perform path following.

The model used throughout this work is a kinematic model. This type of model allows for the decoupling of vehicle dynamics from its movement. Therefore, the vehicle’s dynamic properties, such as mass, center of gravity, etc. do not enter into the equations. To derive this model, the non-holonomic constraints of the system are utilized.
Path Curvature
The navigation and vehicle stabilization controller based on the kinematic model performed very well on the car itself. Further, the dynamic model of the vehicle can be constructed. Using this mathematical model of the vehicle dynamics, a more precise vehicle stability control algorithm can be developed and implemented on the model car.

Major changes to the algorithm are not necessary. However, improvements can still be made in the smoothness of operation. Adjusting the algorithm so that driving comfort is the primary objective may result in smoother performance.
Commercial Use
Extruded Aluminum Tunnel Chassis
Differential Unit
2-Speed Automatic Transmission
Sensors, Actuators and the Microprocessor
Infrared Range Sensor
Ultrasonic Sensor
Inertial Measurement Unit
Servo Motor
Control Board Connections
Continuous Space Problem
Discrete Space Problem
Standard A* Algorithm
Assumptions used in this approach,

The size of the space is known.
The space is already divided into grids.
The vehicle has a short sensing range compared to the size of the region of interest.
The vehicle senses radially from its position, i.e., obstacles can block the sensing in some directions.
The vehicle knows its coordinates and orientation.
Real Time A* Algorithm
Object and Terrain Detection
Non-holonomic Constraints
The Velocity Constraints on a Rolling Wheel with No Slippage
The Global Coordinate System for the Vehicle
The Path Coordinates for the Vehicle
Global Coordinate Model
Path Coordinate Model
Future Scope
The online path planning of the autonomous vehicle in unknown environment with static obstacles is performed using Heuristic real time A* algorithm. The use of A* algorithm can meet the rapid and real-time requirements of path planning which otherwise is not possible in some path planning methods using advanced algorithms. However, only static obstacles were considered in the present study. A robust autonomous navigation algorithm has been created for the autonomous vehicle.

Obstacle detection has been successfully implemented using various sensors, and the vehicle is able to detect objects around it up to a certain distance. Despite the low resolution of the chosen sensor, the perception block was able to generate a representation of the surrounding environment with which decisions could be made towards a safe autonomous navigation.

The hardware and sensors required to implement the navigation and stabilization control schemes have been discussed at the beginning of the thesis. Further, the software and hardware integration needed to physically implement the autonomous vehicle is presented. However, the overall theory for developing and implementing an autonomous vehicle has been successfully presented in this work.
The autonomous vehicle controller presented in this dissertation demonstrates its ability to follow a desired path at the friction limits. Combining the vehicle stabilization controller's ability with the path planning technology provides many exciting future opportunities for driver assistance systems and autonomous vehicles, where the systems can maximize the tire forces to track desired paths during emergency maneuvers.

This project shows that an autonomous vehicle can be built on a small budget. Vehicle navigation and stabilization is accomplished using an on-board micro-controller, various sensors and actuators mounted on the modified model radio control car chassis. Although, this project will be done on a small budget and a small scale, the same concepts and ideas can be applied to a larger scale application. The result from this work provides a platform for developing advanced vehicle controls and also provides a useful tool for robotics education.

Obviously, there is an abundance of work still to be done in order to physically implement the autonomous vehicles in real life applications. It is well known that there is a fine line between theory and implementation of a system. A whole new bunch of problems arise, and much more testing and debugging would be required to actually build a full scale autonomous vehicle.
Thank You :)
Full transcript