Overview

We built a full-stack Android/Java control system for our Freight Frenzy FTC season. The code lives in the standard FTC Robot Controller SDK, extended with:

  • A custom TeamCode module for our hardware mappings and behaviors
  • MeepMeep path visualization to simulate and fine-tune autonomous routes
  • Tunable force coefficients for drivetrain motors
  • State-machine control for ring/puck intake and shipping hub scoring

This repo powers both teleop driver controls and a multi-stage autonomous routine optimized for shipping hub cycles.

Motivation

Freight Frenzy challenges teams to pick up blocks and freight, navigate warehouse zones, and score on a shipping hub under 30 seconds of autonomy. We needed:

  • Precise path‐following to avoid obstacles
  • Tunable power models for battery aging/drivetrain friction
  • A fast-recovery state machine in case of mis-alignment
  • Easy simulation of trajectories before hitting the field

This drove us to integrate MeepMeep and develop a robust on-robot state architecture.

Development Setup

  1. Clone the FTC SDK and our fork:
    git clone https://github.com/theoeriata/FTC_FreightFrenzy.git
cd FTC_FreightFrenzy
  2. Open in Android Studio (“Import Project (Gradle)”).
  3. Plug in the REV Expansion Hub over USB-OTG.
  4. Tune motor force coefficients in TeamCode/src/main/java/constants/DriveConstants.java.
  5. Deploy via “Run > Deploy” to your Control Hub or Driver Hub.

Software Architecture

Module Breakdown

  • FtcRobotController (root SDK)
  • TeamCode
    • Hardware mappings: IMU, DC motors, servos, distance sensors
    • AutoStateMachine: sequences marker drop, freight intake, hub scoring
    • DriveSubsystem: kinematic wrapper with feedforward + PID
    • TeleOpOpMode: field-centric joystick drive + intake controls
  • MeepMeepPathVisualizer
    • JSON-exported trajectories from on-robot code
    • Real-time visualization in desktop window

Key Features

  • Unpowered path planner with JSON export for simulation
  • Force coefficient adjustments to match real motor torque curves
  • Recovery states if element detection via optical distance sensor fails
  • Field-centric mecanum drive for intuitive TeleOp handling

Autonomous Routine

  1. Start at pre-load shipping hub level (determined by AprilTag scan).
  2. Deliver the duck marker.
  3. Cycle:
    • Navigate to warehouse
    • Intake freight (3 blocks)
    • Return to shipping hub
    • Deposit on designated level
  4. Park in warehouse to maximize endgame points.
Top-down view (meters)
  ┌─────────────────────────────┐
  │ [Shipping Hub]   [Carousel]│
  │     ↑         →            │
  │   Start → → →   → ← ← ← ←  │
  │   ↓                      ↓ │
  │ [Warehouse Entrance]       │
  └─────────────────────────────┘

Path Visualization

We integrated the MeepMeep SDK to simulate robot paths:

PhaseWaypointsSimulated Time
Pre-load drop(0,0) → (1.2,0.8) → (1.0,1.6)4.1s
Warehouse run(1.0,1.6) → (3.0,2.5)3.8s
Return score(3.0,2.5) → (0.8,1.4)3.2s

This allowed on-field tuning of power curves and obstacle avoidance without repeated hardware trials.

TeleOp Controls

  • Left stick: field-centric translation
  • Right stick X: rotation
  • A/B: intake in/out
  • X/Y: dumper up/down
  • Bumpers: speed scaling (0.5×, 1×, 1.5×) for delicate / fast maneuvers

Testing & Results

  • 20+ hardware runs to tune force coefficients
  • 95% success on first-cycle hub deposition
  • Average 28s complete cycle time (target: <30s)
  • Robust recovery from dropped freight in 2.3s on average

Future Work

  • Machine-vision alignment to shipping hub using OpenCV
  • Dynamic trajectory replanning if blocked by opponent robot
  • Integration with Road Runner for spline-based paths
  • Expansion of MeepMeep plugin to visualize sensor error envelopes