1. Design and Development of an Autonomous Surface Watercraft
Sponsor: Dr. D. Dunlap
Advisor: Dr. J. Clark
Instructors: Dr. N. Gupta, Dr. C. Shih
Team 18: Donald Gahres, Kyle Ladyko, Samuel Nauditt, Teresa Patterson
Presenters: Donald Gahres, Kyle Ladyko
30 March 2017

2. Competition Recap
Annual Roboboat competition has boats autonomously navigate through a lake course
Competition Challenges:
Weight/Thrust Measurement
Basic Navigation
Obstacle Avoidance
Automated Docking
Interoperability
Acoustic Beacon/Pinger Location
Return to Dock
Sam
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3. Real World Applications of Competition Tasks
Types of Mines
Contact Mines
Influence/Acoustic Mines
Buoy Navigation
Mimics field of contact mines
Acoustic Pinger & Interoperability
Deployment of mine-hunting sonar systems
AN/AQS-20A
AMNS
2017 Predicted Drone Task
Northrop Grumman ALMDS
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4. Project Scope
Goal Statement: Create a lightweight surface vehicle capable of maneuvering a course autonomously with a focus on obstacle avoidance, waypoint navigation, and color and shape recognition while remaining versatile for later subsystems to be added.
Insert competition layout gfraphic and colored bullets
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5. Recap: Hull, Electronics, and Weight
Access hatches installed
8020 platform, threaded rod, and plates installed for sturdy pontoon connection
Camera housing installed
Electronics housing installed
Thrusters installed
Electronics
Clean layout of components.
Large space allows for integration of future subsystems
Weight
Current weight: 65 lbs.
Estimated final weight: 85 lbs.
Design can be iterated so that a second year team can aim for the bonus points offered by the competition for a watercraft weighing less than 70 lbs.
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6. Recap: Testing Preparation and Results
Construction of components for course simulations
Electronics dry tested for successful integration from prototype
Procedures laid out for necessary tests to ensure efficiency
Mobile testing setup organized
Test Results
Accomplishments
Weight/Thrust measurement
Manual control operable
Basic autonomous navigation
Robustness of design
Weaknesses identified for immediate address
Test Buoy Height
Field of view
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7. Mapping and Planning: Vector Field Histogram
Maps robot position and obstacles to NxN grid
Calculates robot's path based on obstacle density and target location
RTK-GPS and LIDAR integrated, magnetometer and thruster control functions in process
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8. Why the Switch?
Previously planned to utilize SLAM for obstacle avoidance challenge
Discussion with sponsor led to decision to change method to Vector Field Histogram
Computational cost is much lower
No need for separate path planning algorithm
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9. Challenges and Future Addresses
Tuning VFH threshold and constants during testing for optimal results
Group availability for testing
Future
Size constraints to keep in mind for drone task for second year
Continue testing autonomy
Mobility of testing setup
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10. Spring 2017 Gantt Chart
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11. Testing Status Testing Status Test Date - Completed
Test
Date
Weight Measurement
17 FEB
Thrust Measurement
19 FEB
Buoyancy
Structural/Watertight Integrity
Object Detection using Camera
MONTHLY
Autonomously Navigate Channel Markers
Autonomous Buoy Navigation
01 APR
Remote Control
BI-WEEKLY
GPS and LIDAR Dry Test
DAILY
Object Avoidance Coding
Daily
- Completed
- Accomplished, but will undergo further tests
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12. Conclusion Completed Items Immediate Plans Future Plans
Final modifications made on boat
Organized layout of electrical components
Weight/Thrust measurement
Basic navigation
VFH algorithm written
Immediate Plans
Implementation of VFH
Testing VFH algorithm
LIDAR installation
Future Plans
Test and debug autonomous obstacle avoidance
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13. Questions?
Sam
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