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Autonomous Surface Vehicle Project

Autonomous Surface Vehicle Project. MAE 435 Project Design and Management II 19 October, 2011. ASV MAE Team Members . Advisors. Team A. Team B. Dr Gene Hou (Faculty Advisor) Justin Selfridge (Graduate Advisor) Stanton Coffey (Graduate Advisor). Brian Skoog John Lee Jeff Roper

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Autonomous Surface Vehicle Project

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  1. Autonomous Surface Vehicle Project MAE 435 Project Design and Management II 19 October, 2011

  2. ASV MAE Team Members Advisors Team A Team B • Dr Gene Hou (Faculty Advisor) • Justin Selfridge (Graduate Advisor) • Stanton Coffey (Graduate Advisor) • Brian Skoog • John Lee • Jeff Roper • Paul Hart • Stephanie Mccarthy • Andrew Vaden • John Bernas • Eric Starck • Jason Putman • Kevin Mcleod

  3. ASV ECE Team Members Advisors Students • Dr Chung-Hao Chen (Faculty Advisor) • Nimish Sharma • Justin Maynard • Robert Tolentino • Bibek Shrestha • Sushil Khadka

  4. Autonomous Surface Vehicle-ASV • What is it? • Vehicle (boat) that can operate with no human interaction • Why do we need them? • ASVs can operate in environments that are dangerous to humans (nuclear, biological, space, etc)

  5. Objective • Improve current ASV for the Summer 2012 Association for Unmanned Vehicle Systems International annual RoboBoat Competition

  6. RoboBoat Competition • Primary Tasks • Speed Test • Locate and complete a straight course as fast as possible • Navigation Test • Navigate a course of buoys with several turns and obstacles • Secondary Tasks

  7. Solution Approach • Determine/purchase sensors that provide competitive performance • Determine a navigation logic • Integrate all sensors • Test and evaluate sensors and navigation logic • Debug and modify as required • Install electronics on boat • Test and evaluate ASV

  8. Upgrades in Progress • Computer Vision code • LiDAR • Sensor gimbal mount • Navigation Logic • New onboard computer • Arduino integration

  9. Computer Vision • Primarily for buoy color detection • Inputs directly to onboard computer • Vision information only extracted when LiDAR detects object

  10. LiDAR • Light Detection And Ranging • Primary Navigation Sensor • Inputs directly to onboard computer • 240 degree FOV • 5.2 meter radius

  11. Sensor Gimbal Mount • Required to keep LIDAR and cameras level • Uses Ardupilot gyro and accelerometer sensors to detect motion

  12. Navigation Logic • Defined scenarios based on: • Distance to buoys • Color of buoys • Approach angle • LiDAR as primary sensor • Computer Vision as secondary sensor

  13. Navigation Logic Flow Chart

  14. New Onboard Computer • Custom build/Watercooled • Intel Core i3-2100T • Low Power consumption • Dual core/Hyperthreading Technology • M4-ATX-HV DC-DC Power Converter • 250 Watts maximum • 6-34v DC wide input • Will run on boat battery

  15. Onboard Computer Cont. Not to Scale Inside Waterproof Box HDD Pump/ Reservoir Power CPU RAM Radiator Motherboard Wireless

  16. Arduino Integration • Ardupilot integrated sensors • GPS • Gyro • Compass • Accelerometer

  17. Sensor Schematic

  18. Gantt Chart

  19. Summary • Improve current ASV in order to be more competitive in RoboBoat competition primary tasks • Integrate LiDAR as primary navigation sensor • Build gimbal mount for navigation sensors • Integrate Ardupilot • Upgrade computer hardware to improve processing speed and electronics case cooling

  20. Questions?

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