EnviroBot — Autonomous Terrain Survey Robot

00 Competition Quick Reference

Scoring Table (table totals 100; authoritative)

8-Minute Time Budget

01 Mission

Navigate an octagonal 4-sector arena. In each sector locate 1 water pond and 2 soil patches at random positions. Water ponds are large: robot must sample from edge and reroute around them. Track position via encoder + IMU dead-reckoning. Maintain exclusion zones to avoid re-sampling. Operator can override at any time via mobile touch UI.

02 Arena Definition

Terrain Types

03 Hardware Rev 5

Controller — ESP32-WROOM-32 (from kit)

Dual-core Xtensa LX6, 240MHz, 4MB flash, WiFi b/g/n, 30-pin. Runs navigation, dead-reckoning fusion, color classification, measurement, HTTP + WebSocket server concurrently.

Sensors

Actuation

Power Architecture — Single Buck

Physical Spec

04 Why 3 TCS34725 Sensors

Arm centering logic

Strip coverage

05 Chassis Layout

06 Pin Assignment Rev 5

07 Dead-Reckoning 2.0 — Encoder + IMU Fusion

Encoder constants

cpp#define WHEEL_DIAM_MM   65.0f
#define PULSES_PER_REV  20
#define MM_PER_TICK     (3.14159f * WHEEL_DIAM_MM / PULSES_PER_REV)  // ≈ 10.2mm
#define WHEEL_BASE_MM   120.0f
#define EXCL_RADIUS     80.0f   // mm — tighter than Rev 4 (120mm) due to ±20mm accuracy

volatile int32_t enc_left = 0, enc_right = 0;
void IRAM_ATTR isr_left()  { enc_left++;  }
void IRAM_ATTR isr_right() { enc_right++; }

Fused position update (call every 20ms)

cppvoid update_pos_v2() {
    noInterrupts();
    int32_t dl_ticks = enc_left;
    int32_t dr_ticks = enc_right;
    enc_left = enc_right = 0;
    interrupts();

    float dl = dl_ticks * MM_PER_TICK;
    float dr = dr_ticks * MM_PER_TICK;
    float dc = (dl + dr) * 0.5f;
    float dtheta_enc = (dr - dl) / WHEEL_BASE_MM;
    float dtheta_imu = get_imu_delta_yaw();  // MPU6050 DMP
    float dtheta = 0.6f * dtheta_imu + 0.4f * dtheta_enc;

    pos.heading += dtheta;
    pos.x += cosf(pos.heading) * dc;
    pos.y += sinf(pos.heading) * dc;
}  // origin (0,0) = arena center

Exclusion zones

cppbool already_sampled(float x, float y) {
    for (int i = 0; i < sample_count; i++) {
        float d = hypotf(x - past_samples[i].x, y - past_samples[i].y);
        if (d < EXCL_RADIUS) return true;
    }
    return false;
}

Accuracy comparison

08 Water Pond — Sampling and Rerouting

cpp#define TURN_90_MS  1885  // π/2 × WHEEL_BASE_MM / SPEED_MM_S × 1000
                          // = 1.5708 × 120 / 100 × 1000 = 1885ms

void on_water_detected() {
    stop_motors();
    if (already_sampled(pos.x, pos.y)) { state = POND_REROUTE; return; }
    arm_water_down(); delay(1500);
    float ntu = read_turbidity(); arm_neutral();
    char val[16]; snprintf(val, 16, "%.1f NTU", ntu);
    record_sample(current_sector, WATER, val);
    state = POND_REROUTE;
}

void reroute_around_pond() {
    reverse(100, 800);
    turn_right(100, TURN_90_MS);
    while (true) {
        bool wR = in_water(sensors[RIGHT]);
        bool wC = in_water(sensors[CENTER]);
        if (wC) { turn_right(60, 200); }
        else if (!wR) { forward(100, 800); break; }
        else          { forward(100, 100); }
        update_pos_v2();
    }
    turn_left(100, TURN_90_MS);
    state = SWEEP;
}

09 Navigation — Sector Coverage

Strip math (Rev 5)

Terrain color classification

cpp#define TERRAIN_THRESHOLD  0.25f

float color_distance(ColorReading a, ColorReading b) {
    float tA = a.r+a.g+a.b+1, tB = b.r+b.g+b.b+1;
    float rA=(float)a.r/tA, gA=(float)a.g/tA;
    float rB=(float)b.r/tB, gB=(float)b.g/tB;
    return sqrtf(powf(rA-rB,2)+powf(gA-gB,2));
}

bool in_current_terrain(ColorReading r) {
    return color_distance(r, terrain_baseline) < TERRAIN_THRESHOLD;
}
// in_water(): b/(r+g+b+1) > 0.45f
// in_soil():  r/(r+g+b+1) > 0.40f  (reddish disc)

Terrain-specific PWM

Target 100mm/s on all terrains. Encoder feedback confirms actual speed; adjust PWM during Day 1 test.

10 State Machine

Stall detection (RECOVERY trigger)

cppvoid check_stall() {
    static int stall_ms = 0;
    if (state == SWEEP && enc_left == 0 && enc_right == 0) {
        stall_ms += 20;
        if (stall_ms > 3000) { stall_ms = 0; state = RECOVERY; }
    } else { stall_ms = 0; }
}  // called every 20ms from main loop

11 Zone Coverage Guarantee

12 No-Duplicate Guarantee

cppbool terrain_complete(int sector) {
    int water=0, soil=0;
    for (int i=0; i<sample_count; i++) {
        if (in_sector(past_samples[i].x, past_samples[i].y, sector)) {
            if (past_samples[i].type==WATER) water++;
            if (past_samples[i].type==SOIL)  soil++;
        }
    }
    return (water >= 1 && soil >= 2);
}
// already_sampled(x,y) checked before every arm deployment — EXCL_RADIUS=80mm

13 Measurement Code

Standardised record_sample

cpptypedef struct {
    float x, y;
    int sector;
    enum { WATER, SOIL } type;
    uint32_t timestamp_ms;
    char value[16];  // "42.3 NTU" or "61.2%"
} Sample;

Sample past_samples[16];
int sample_count = 0;

void record_sample(int sector, int type, const char* value) {
    Sample& s = past_samples[sample_count];
    s.x = pos.x; s.y = pos.y;
    s.sector = sector; s.type = type;
    s.timestamp_ms = millis();
    strncpy(s.value, value, sizeof(s.value)-1);
    sample_count++;
}

Servo arm + read functions

cppvoid arm_neutral()    { arm.write(90);  delay(500); }
void arm_water_down() { arm.write(0);   delay(600); }  // CCW → water side
void arm_soil_down()  { arm.write(180); delay(600); }  // CW  → soil side

float read_turbidity() {
    float v = (analogRead(TURB_PIN)/4095.0f) * 3.3f * 1.5f;
    return fmaxf(0, -1120.4f*v*v + 5742.3f*v - 4352.9f);
}

float read_moisture() {
    int raw = analogRead(SOIL_PIN);
    return fmaxf(0, fminf(100, (float)(SOIL_DRY-raw)/(SOIL_DRY-SOIL_WET)*100.0f));
}

void sample_water(int sector) {
    arm_water_down(); delay(1500);
    float ntu = read_turbidity(); arm_neutral();
    char val[16]; snprintf(val, 16, "%.1f NTU", ntu);
    record_sample(sector, WATER, val);
}

void sample_soil(int sector) {
    arm_soil_down(); delay(1500);
    float pct = read_moisture(); arm_neutral();
    char val[16]; snprintf(val, 16, "%.1f%%", pct);
    record_sample(sector, SOIL, val);
}

14 Data Storage — SPIFFS JSON

After each sample, results written to /results.json in SPIFFS. Served at 192.168.4.1/results. Also dumped to Serial on DONE. Uses ArduinoJson v6.

JSON output schema

json{
  "run_id": "envirobot_001",
  "total_samples": 12,
  "duration_ms": 420000,
  "samples": [
    {
      "id": 1,
      "sector": 1,
      "type": "water",
      "value": "42.3 NTU",
      "x_mm": 310.5,
      "y_mm": -125.8,
      "timestamp_ms": 34200
    },
    {
      "id": 2,
      "sector": 1,
      "type": "soil",
      "value": "61.2%",
      "x_mm": 280.1,
      "y_mm": -200.3,
      "timestamp_ms": 67800
    }
  ]
}

Write function (ArduinoJson v6)

cppvoid write_results_json() {
    StaticJsonDocument<4096> doc;
    doc["run_id"] = "envirobot_001";
    doc["total_samples"] = sample_count;
    doc["duration_ms"] = millis();
    JsonArray arr = doc.createNestedArray("samples");
    for (int i=0; i<sample_count; i++) {
        JsonObject o = arr.createNestedObject();
        o["id"] = i+1;
        o["sector"] = past_samples[i].sector;
        o["type"] = (past_samples[i].type==WATER) ? "water" : "soil";
        o["value"] = past_samples[i].value;
        o["x_mm"] = past_samples[i].x;
        o["y_mm"] = past_samples[i].y;
        o["timestamp_ms"] = past_samples[i].timestamp_ms;
    }
    File f = SPIFFS.open("/results.json", "w");
    serializeJson(doc, f);
    f.close();
}

HTTP endpoints

15 Auto-Calibration at Startup

• 1
  0–3s: Read terrain floor 30× at center → average RGB → terrain_baseline. NeoPixel blue pulse.
• 2
  3–4s: Seed water ref (b/total > 0.45) and soil ref (r/total > 0.40).
• 3
  4–5s: Verify references are ≥3× TERRAIN_THRESHOLD apart. If not → blink red, pause, wait for re-seat.
• 4
  5s: NeoPixel green → SWEEP begins. Re-calibrates terrain baseline at each sector transition.

16 Communications — WiFi AP Mode

cppWiFi.softAP("EnviroBot", "enviro123");
// Connect phone to "EnviroBot" WiFi → open 192.168.4.1
// HTTP server: port 80  (pages + JSON)
// WebSocket:   port 81  (RC commands + status push)

17 Remote Control — Mobile Touch UI

Mobile UI at 192.168.4.1/rc

WebSocket message format

json// Phone → Robot (RC command):
{ "cmd": "joy", "x": 0.85, "y": 0.30 }
// x: left/right (−1 to +1), y: forward/back (−1 to +1)
{ "cmd": "auto" }      // resume autonomous from current position
{ "cmd": "stop" }      // immediate stop
{ "cmd": "arm_water" } // manual arm trigger

// Robot → Phone (status push every 500ms):
{ "state": "SWEEP", "sector": 2, "samples": 4, "x": 310.5, "y": -125.8 }

Joystick to motor PWM

cppvoid apply_joystick(float jx, float jy) {
    float left  = fmaxf(-1, fminf(1, jy + jx));  // tank-drive mix
    float right = fmaxf(-1, fminf(1, jy - jx));
    set_motors((int)(left*255), (int)(right*255));
}

18 Data Visualization App — visualizer.html

Standalone single HTML file. Drag-drop results.json → renders 3D arena + charts. Open in any browser, no server needed. Scores Cat 2 (data output) + supports Cat 6 (case presentation).

Layout

Features

Three.js bootstrap

js// Three.js r165 from CDN, Chart.js 4 from CDN
const scene  = new THREE.Scene();
const camera = new THREE.PerspectiveCamera(45, w/h, 1, 5000);
camera.position.set(0, 900, 900);
camera.lookAt(0, 0, 0);

const terrainColors = [0xD2B48C, 0x4E342E, 0x558B2F, 0xF9A825];
// sandpaper, wet soil, grass, sand

function loadResults(json) {
  json.samples.forEach(s => {
    const mesh = s.type === 'water' ? makeWaterDiamond() : makeSoilDisc();
    mesh.position.set(s.x_mm * 0.5, 2, s.y_mm * 0.5);
    mesh.userData = s;
    scene.add(mesh);
  });
  drawPath(json.samples);
  buildCharts(json.samples);
}

19 Scoring Strategy

Score-per-effort map

Day 1 Pitch (3 min)

1. [0:00–0:30] Problem: environmental monitoring in inaccessible terrain. Manual = slow, expensive, dangerous.
2. [0:30–1:00] Solution: EnviroBot. Autonomous survey, dead-reckoning 2.0, 12 samples in 8 min.
3. [1:00–1:45] Live demo: power on → calibrate → first sector sweep → sample logged on phone UI.
4. [1:45–2:15] Innovation: hybrid RC, encoder+IMU fusion, mobile touch UI, single-file data viz.
5. [2:15–2:45] Data story: open visualizer.html → 3D arena → explain what the readings mean.
6. [2:45–3:00] Call to action: fleet deployment = early flood/contamination detection at 5% of current cost.

20 Day 2 — Closing Night Case Competition

1-Hour Day 2 Prep Plan

• 1
  [0–15 min] Full arena run. Download results.json. Open visualizer.html. Screenshot 3D + charts.
• 2
  [15–30 min] Write argument. Use real data: find the most interesting finding (highest NTU, driest soil). Build 3-point policy argument below.
• 3
  [30–45 min] Rehearse. Present to each other, time it. Aim 5–7 min. Cut what's over.
• 4
  [45–60 min] Backup prep. If robot breaks: use pre-run data + visualizer screenshots. HDMI adapter in bag. Laptop charged.

Case Argument Template

21 Bill of Materials

From Kit (no cost)

Additional Purchases

22 Pre-Competition Checklist

Technical Inspection

• Kill switch accessible: top of chassis, latches off, clearly visible to officials
• No loose wires: all soldered or firm dupont, zip-tied to chassis
• Fresh batteries: 8× AA alkaline, measured ≥11.8V under load
• Robot within size limits: measure L/W/H, confirm rulebook compliance
• Servo travel verified: 0°/90°/180° all reach correctly, no mechanical bind

Day 1 Morning (Test Day)

• Power on at center → NeoPixel blue pulse during 5s calibration
• NeoPixel green at 5s → SWEEP starts → robot moves straight?
• Serial Monitor: encoders ticking? Position updating?
• Zone detection test: TCS over water slot → arm deploys water side
• Zone detection test: TCS over soil disc → arm deploys soil side
• Full 8-min arena run: confirm 12 samples collected
• Connect phone to EnviroBot WiFi → open 192.168.4.1/results → JSON visible
• Open visualizer.html → drop results.json → 3D + charts render correctly
• Test /rc page on phone → joystick controls robot in MANUAL mode
• MANUAL → AUTO toggle → robot resumes autonomous sweep
• Kill switch test: hit mid-run → robot stops immediately

Day 2 Morning

• Final full arena run → collect best results.json
• Open visualizer.html with real data → screenshot for case slides
• Rehearse 5–7 min case presentation (argument from §20)
• Laptop charged + HDMI adapter packed
• visualizer.html open with results.json loaded on laptop
• Fresh batteries installed ≥11.8V
• results.json backed up to phone + USB stick