We are envisioning a system where an Unmanned Ground Vehicle (Rover) is able to autonomously navigate in an unknown indoor environment and generate a 2D map at realtime. We aim to develop this system cost-effectively by using only visual sensors. The rover shall capture visual input and send it back
Scout Rover
We are envisioning a system where an Unmanned Ground Vehicle (Rover) is able to autonomously navigate in an unknown indoor environment and generate a 2D map at realtime. We aim to develop this system cost-effectively by using only visual sensors. The rover shall capture visual input and send it back to a server. The server shall process the visual input by implementing SLAM algorithms. Based on this processing, the server will then autonomously control the rover to navigate and explore the unknown environment. Meanwhile, the server will also generate the 2D Map which shall be visible on a screen.
Set up the Rover hardware: 4 DC Motors, Arduino chip, RaspberryPi, and Kinect Vision Sensor (all mounted on the rover).
Set up a Server on some Cloud Platform/Local Machine.
Establish communication channels between Rover and Server:
Arduino - RaspberryPi Bluetooth (RFCOMM)
RaspberryPi - Server WiFi (TCP/IP)
Implement SLAM algorithms and techniques on the server:
Localization
Motion Planning
Generate and display 2D Map on some GUI.
Our rover has both the Arduino and Raspberry Pi. Arduino controls motor driver - which is responsible for powering wheel motors. The connection between Raspberry Pi and Arduino is established through Bluetooth using HC-06 module.
Raspberry Pi gets video feed from the Visual Sensor which is also mounted on the rover and has a wired connection with the Raspberry Pi. This feed is forwarded to the Server through Wifi where all the computations are done.
As we have a distributed system, different software modules are running on different hardware components. The Server is responsible for running the main SLAM algorithms while the Raspberry Pi only forwards the video feed to the Server.
Referring to the Class Diagram, Rover class is running on the Raspberry Pi and the Server class is running on the Server. NetworkHandler is a common class which is deployed on both systems.
For the implementation of SLAM Algorithm, Map, Landmark, Particle, Localization and are running on the server side. The landmark class holds the information about the predefined information about the landmarks. Map class translates the environment into classes. Particle class holds the probability of location of the rover. Localization class holds the algorithm for localizing rover against the provided environment map.
Although we are only envisioning a proof of concept for the scope of this project, there are tons of opportunities in the real world in which such a system could help improve human life. These are not only business opportunities but also provide a chance to make some positive impact for humanity. Some of these opportunities include:
Search and Rescue Missions
Assistant for Vision Impaired People
Automated Map Generation in Game Development
Although the system shall be scalable, however, for the purpose of this project, we limit the scope to only the following features:
Indoor Environments only
Single Floor i.e., rover shall be able to move on one plane only
The following constraints will not be handled in this project:
Poor Wi-Fi
Low Battery
Bluetooth range
Bad Lighting Condition
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Rover | Equipment | 1 | 6000 | 6000 |
| Raspberry Pi | Equipment | 1 | 9000 | 9000 |
| ESP 32 | Equipment | 1 | 1200 | 1200 |
| Microsoft Kinect | Equipment | 1 | 7000 | 7000 |
| Lipo 12V battery | Equipment | 2 | 2500 | 5000 |
| Motor Driver | Equipment | 1 | 300 | 300 |
| Total in (Rs) | 28500 |
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