VEX U Robotics
AI/software R&D for Queen's VEX U team: real-time object detection with OpenCV, sensor fusion across hardware input streams, Rust autonomy stack, and cross-team coordination with mechanical.
Rust onboarding
2 weeks
Team documentation
Detection
Real-time
Codebase
Stack
Rust
Stack
Python
Stack
OpenCV
Stack
Sensor fusion
Stack
Real-time object detection
Problem
Competitive robotics demands real-time perception and autonomous decision-making with no tolerance for latency or hesitation.
- Sensor noise corrupts position estimates.
- Autonomy decisions need sub-100ms cycles.
- Cross-team hardware/software integration adds constraint.
System
The software stack targets real-time object detection and sensor fusion inside a Rust-first autonomy codebase.
- OpenCV handles visual object detection.
- Sensor fusion merges hardware input streams.
- Rust enforces low-latency, memory-safe execution.
Shipped proof
Onboarded an unfamiliar Rust codebase at production pace and contributed to perception and autonomy systems.
- Rust codebase up to speed in 2 weeks.
- Real-time detection integrated.
- Mechanical/software coordination maintained across team.
Lesson
Fast language acquisition under real constraints is a skill. Hardware-constrained real-time systems teach you to care about every cycle.
- Learn the codebase, then improve it.
- Sensor fusion forces you to own your error model.
- Cross-team constraints are design inputs, not blockers.
Evidence links
Public-safe role summary only. Internal team code and docs stay private.