1. Group 3 Corey Jamison, Joel Keeling, & Mark Langen
Mapping Robot
Group 3
Corey Jamison, Joel Keeling, & Mark Langen

2. Overview
Robot on wheels that drives around a room and produces a 2d map of the room to be displayed on a phone

3. Roles (so far) Mark: Mapping algorithm Corey: Simulation, Mobile App
Joel: Hardware integration

4. Motivation: Interesting software problem
Integration of a variety of different systems (software & hardware)

5. Functionality Wheeled robot with rotating laser mounted on top
Generates distance + angle measurements
Software algorithm builds 2D best-guess map of surroundings
Map transmitted via Bluetooth to Android app & displayed to the user
Vehicle movement controlled by Android app

6. Design

7. Design

8. Design Challenges: Finding an affordable, accurate sensor
Complexity & performance of software algorithm

9. Design
Calculations:
FPGA usage (~30% using high-performance processor)
Torque required for wheels, rotating rangefinder

10. Algorithm
General Algorithm:
Backup plan: Corey – Simulator

11. Algorithm
Some “SLAM” (Simultaneous Location and Mapping) algorithms already exist, but they are typically for mapping 3D spaces, making use of much more data than a single 2D slice like ours does, and require much more processing power.
Rotation works similarly, but using a histogram of rotation differences instead of distance differences.
Map is build up out of the line segments once the absolute position has been determined. They are added to a quad-tree structure of line segments in space which can be extended and refined in positioning as more points are considered.

12. Testing Testing each module independently Map-building algorithm:
tested on-the-fly throughout development using the simulator
Unit testing with predefined data and checking error tolerance of results
Bluetooth module:
Thorough testing across various conditions: distance, through/around obstacles, bandwidth usage, etc.
Laser Distance & Rotation data:
Data generated can be manually measured (tape measure, protractor, etc.)
Accuracy & biases will be recorded
Data transmission rates and synchronization between both data sources

13. Testing Chassis wheels: Integration Testing:
Mapping algorithm already assumes that wheels are unreliable and there will be drift & slipping
Motor driver will be calibrated to minimize drift, but perfect accuracy is not needed
Integration Testing:
Modules integrated in steps
laser & stepper motor
Bluetooth
Mapping algorithm
Vehicle movement

14. Optional Features
In minimum viable product, user will manually control the vehicle’s movement
Additional modes of control:
Semi-autonomous: User selects waypoints, vehicle navigates to them autonomously
Autonomous: Vehicle decides where to go next, produces full map of room with no user input
Custom operations to speed up mapping algorithm

15. Questions?