Full Project Spec

Type: Architecture

Concept

An autonomous ground rover that drags a cloth to collect ticks, captures images of that cloth, and maps tick density over space and time.

Vehicle Layer — Traxxas TRX-4

• Mobility platform for uneven terrain, grass, roots
• Handles towing the drag cloth
• Includes ESC (motor control) and steering servo

Compute Layer — Raspberry Pi 4

• The brain: runs navigation, capture, return-to-home, and streaming
• MicroSD stores OS, captured images, and GPS logs
• Powered by independent USB battery pack (prevents electrical noise and motor spike crashes)

Control Layer — PCA9685 PWM Driver

• Converts Pi commands to RC signals
• Controls throttle (ESC) and steering servo
• Replaces the handheld controller

Navigation Layer — u-blox GPS Module

• Provides latitude/longitude for image tagging, return-to-home, and tick density mapping
• GPS-only for MVP (~2-3m precision)
• Path recording: user walks/drives the route first, rover replays it autonomously
• Boundary defined by the recorded path, no separate geofence

Sensing Layer

• ADS1115 ADC — reads analog signals (Pi can't natively)
• Voltage divider — scales battery voltage safely
• Together: monitor crawler battery and trigger return-to-home

Vision Layer

• Raspberry Pi Camera Module 3 (Standard) — captures images of drag cloth, autofocus
• Camera cable — flexibility in mounting position
• Camera mount + ball head — precise angle adjustment for consistent framing

Sampling Layer

• Drag bar (PVC/dowel) — holds cloth behind rover
• White flannel cloth — collects ticks, treated with permethrin before each use cycle
• Optional dark backing — improves contrast for detection
• Ticks incapacitated/killed on contact (nymphs <1 min, adults 1-3 hrs)

Physical + Mounting Layer

• Waterproof enclosure — protects Pi, ADC, wiring
• Mount plate — base for enclosure, battery, components
• Zip ties + Velcro — secure mounting

Wiring Layer

• Servo extension cables, connector taps, terminal block, ground wire

Optional Enhancements

• Buzzer — alerts on low battery or mission complete
• LED strip — consistent lighting for image capture

---

How the System Operates

1. Start

Rover placed at "home", GPS position saved.

2. Patrol

Rover drives along yard edge, drag cloth collects ticks.

3. Sampling Loop

move → stop → capture image → move → repeat

Each capture includes: image, timestamp, GPS location.

4. Detection (Offline or Later)

Process images: count ticks, filter debris, build dataset.

5. Mapping

Generate tick density heatmap and time-based trends.

6. Return-to-Home

When battery low: rover navigates back and alerts you.

---

Design Decisions

Software

• Pi OS: Standard Raspberry Pi OS
• Language: Python
• Data upload: WiFi preferred; offline queue on SD card, syncs when back in range
• Central app: This Next.js/Convex project — parts, build docs, test runs, data logging, vision/ML, public how-to guide

Vision / Detection

• TBD — depends on image quality from real captures
• May start with manual labeling, possibly train ML later

Hardware

• Drag bar mount raised slightly, dragging as far back as possible
• No obstacle avoidance for MVP — yard assumed mostly clear
• Run time unknown; smart return-to-home on low battery

Operations

• Daily use: set it off each morning, runs as long as battery allows
• Won't run in rain; handles morning dew (~9am starts)
• App should warn about rain/weather before deploying

Data Pipeline

Rover (Pi) → WiFi upload → Convex backend → Next.js dashboard
                ↓ (if no WiFi)
         Local queue on SD → sync when reconnected

---

Open Questions

• Exact drag bar attachment to TRX-4
• Battery run time (determines mission length)
• Image quality/resolution needed to resolve ticks (~3mm)
• On-device vs offline detection
• Cloth maintenance strategy mid-run