OBSTACLE AVOIDANCE ROBOT PROPELLER PROJECT BY ALEX COLBOURN

OBSTACLE AVOIDANCE ROBOT (PROPELLER PROJECT) BY: ALEX COLBOURN ALEXANDER GEORGE RYAN TANG

PROJECT GOAL • Improve the implementation of sensors and motors to the forklift project. • Navigate around obstacles obstructing robot path. • Implementation of LCD and object detection using cogs.

SOLUTION Utilize the Parallax PING))) sensor to detect obstacles. • Robot navigation off path to navigate around obstacle. Search for openings around obstacle • Navigate off course to maneuver around obstacle • Return to the line path and continue forward movement.

Parallax PING))) Sensor MATERIALS Elegoo Line Tracking Module Arduino Uno DC Motors Elegoo Smart Robot Car Frame 4 AA Battery Case SG 90 Servo Propeller Microcontroller with Activity Board AA Batteries Parallax LCD Module Elegoo 18650 Battery Adafruit Stepper/Motor Shield V 2. 3

MECHANICAL DESIGN

WIRING DIAGRAM

PROGRAM • The Arduino Uno stores the commands to actuate the motors. • The Propeller microcontroller utilizes sensory data and sends commands to the Arduino Uno. • The Line tracking sensor allows the robot to follow a defined path. • PING))) sensor is used to detect unknown obstacles in path. • If obstacle is detected, the servo performs a 180 degree sweep with PING))) • Using the data values obtained, the robot is able to decide whether to maneuver around the left or right side of the obstacle.

PROBLEMS ENCOUNTERED • Our biggest challenge was timing and sharing of resources between cogs. • The activity board is unable to perform I 2 C and SPI Communications. • Solution: We used analog voltage setpoints to control modes on our Arduino. • For example, 0. 5 volts = Drive Forward, 1 Volt = Turn Left, etc… • Using just a few lines of code on each controller we were able to reliably communicate modes to the arduino

FUTURE PLANS • Add an I 2 C laser distance sensor • Full redesign of chassis and forklift mechanism. • Switch to stepper motors for better control. • Replace line following with path planning using picam (and our new obstacle avoidance algorithms).

VIDEO PROOF