What Students Learn
- Perception, autonomy, and controls
- Embedded systems, sensors, and power
- Requirements definition and verification testing
- Design reviews and full vehicle integration
RAPTOR
Tue 4:00 – 6:00 PM Fri 6:00 – 8:00 PM
Robotics & Autonomous Platforms for Tournaments, Optimization, & Research
RAPTOR is IEEE at USF’s autonomous vehicle competition team. Members design, build, and field a complete autonomous platform and compete in the RTX Autonomous Vehicle Competition (RTX AVC). The competition is organized regionally, and RAPTOR competes in the Southeast.
The team forms each September and competes in April, working out of the IEEE at USF Lab in ENG 208.
Goals for the Year
- Compete for 1st place at the RTX AVC
- Deliver a PDR, CDR, and validated MBSE model
- Train juniors to lead next season
- Expand in-house testing and integration capability
Season Timeline
Each year, RAPTOR follows the same competition cycle from team formation through the April RTX AVC event.
| Phase | Focus | Expected Output |
|---|---|---|
| September | Team formation and onboarding | Roster set, roles assigned, and season requirements reviewed. |
| Fall–Spring | Design, build, and verification | Platform integration, design reviews, and in-house testing. |
| April | RTX AVC competition | Fielded autonomous vehicle and competition performance. |
2025–2026 Competition
RTX Autonomous Vehicle Competition — Southeast Regional
Competition Challenge
Teams built a coordinated UAV–UGV platform and competed across three progressively harder rounds.
- Round 1: UAV takeoff, straight-line flight, and landing on the UGV
- Round 2: ArUco marker detection and coordinated UGV transport
- Round 3: Obstacle detection and avoidance during flight
How the Team Approached It
- Split work across UAV/UGV hardware, software, and sensor integration
- Validated each subsystem independently before full integration
- Standardized vehicle communication with MAVLink
- Tested incrementally, from single-system checks to full multi-level runs
Platform Design
UGV
Mobile landing and transport platform for GPS-denied coordination with the UAV.
- Traxxas 1/10-scale crawler chassis with custom 3D-printed mounts
- Raspberry Pi 5 for navigation and coordination
- Cube Orange+ with ArduPilot for low-level control
- UWB positioning, ultrasonic sensing, encoders, and ArUco camera tracking
Also used: Holybro SiK Telemetry Radio V3, 6S LiPo, AKD1000 M.2 card, DroneCAN RM3100 compass
UAV
Flight platform focused on precise relative positioning and reliable UGV landing.
- Raspberry Pi 5 for navigation, vision, and coordination
- Cube Orange+ with ArduPilot for flight control and sensor fusion
- UWB localization for centimeter-level GPS-denied positioning
- OpenCV ArUco detection and closed-loop precision landing
Also used: Tarot Iron Man 650 frame, MicroAir P1 Optical Flow, T-Motor MN-4006 380kV, Radiolink AT10II, Holybro SiK Telemetry Radio V3, 6S LiPo
Key Takeaways
- UWB localization enabled stable relative positioning without GPS
- Modular design and early testing helped catch perception, communication, and landing issues
- The integrated UAV–UGV system demonstrated reliable autonomous coordination
Team
Leadership
- UAV Lead: Matthew Moore
- UGV Lead: Elijah Korostishevski
Hardware & Software
- UAV HW: Ernesto Rodriguez
- UGV HW: Ryan Leigh
- UAV SW: Nathan Frost
- UGV SW: Adler Montero Padilla
- HW Support: Adrian Gonzalez
Support
- Documentation: Karina Burns
- AI Implementation: Zoe Mutiu
Advisors
- Chung Seop Jeong | USF Faculty
- Chris Ferekides | USF Faculty
- Jay Schroder | RTX Advisor
- Sylvia Traxler | RTX Advisor