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

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

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

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

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

Design

Design

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

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

Algorithm General Algorithm: Backup plan: Corey – Simulator

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.

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

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

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

Questions?