Theory & Background

For 6-inch (150mm) diameter pipes: use a radial wheel configuration with 3 sets of spring-loaded wheels arranged 120° apart. Spring force presses wheels against pipe wall, maintaining traction. Wheel diameter: 40mm. Spring preload: 5–10N (sufficient for traction, not excessive). For different pipe diameters, adjust spring compression. Alternative: articulated body with active wheel extension actuated by servo — adjustable for multiple pipe sizes.

Components & Requirements

10 components required for this project.

#	Component	Purpose	Qty
1	Custom In-Pipe Drive Mechanism	Propulsion inside 6-8 inch pipes	x1
2	Waterproof USB Camera (180° fisheye)	Forward and backward pipe viewing	x2
3	LED Ring Light (white, waterproof)	Pipe interior illumination	x2
4	DC Motors (12V, 300RPM, waterproof)	Drive wheels with 360° contact	x4
5	Tether Cable (50m, 4-conductor + Cat5e)	Power, video, and control link	x1
6	IMU + Pressure Sensor	Orientation and depth in pipe	x1
7	Wheel Encoders (waterproof)	Distance and speed tracking	x4
8	Raspberry Pi Zero 2W	Onboard processing and streaming	x1
9	Wheel Pressure Springs (variable grip)	Adaptive wall contact force	x1
10	Sealed Pressure Housing (IP68)	Electronics waterproofing	x1

Step-by-Step Implementation

Follow these 3 steps carefully.

1

In-Pipe Drive Mechanism Design

For 6-inch (150mm) diameter pipes: use a radial wheel configuration with 3 sets of spring-loaded wheels arranged 120° apart. Spring force presses wheels against pipe wall, maintaining traction. Wheel diameter: 40mm. Spring preload: 5–10N (sufficient for traction, not excessive). For different pipe diameters, adjust spring compression. Alternative: articulated body with active wheel extension actuated by servo — adjustable for multiple pipe sizes.

2

Waterproofing and Sealing

All electronics sealed in acrylic/aluminum tube with O-ring end caps. Test to IP68: 1m water submersion for 1 hour before robot deployment. Camera window: polycarbonate flat window with silicone O-ring seal. Cable entry: use IP68 cable glands with neoprene seal around tether cable. After any maintenance: pressure test to 2 PSI with soapy water (look for bubbles at seals) before deployment in pipes.

3

Defect Detection Algorithm

Real-time pipe defect classification using CNN trained on pipe inspection dataset: crack (longitudinal/circumferential), corrosion patches, joint offsets, root intrusion (tree roots), debris blockage, and deformation. Deploy MobileNetV2 classifier on Raspberry Pi Zero: processes one frame per second. Flag defects with GPS/distance position log. Generate inspection report PDF: pipe section, defect type, severity rating, distance marker, and screenshot.

Code & Implementation

Core code for pipe_defect_detector.py:

pipe_defect_detector.py
  Python

import cv2, tflite_runtime.interpreter as tflite
import numpy as np

# Load TFLite model (optimized for Raspberry Pi)
interpreter = tflite.Interpreter(model_path="pipe_defect_v2.tflite")
interpreter.allocate_tensors()
inp = interpreter.get_input_details()[0]
out = interpreter.get_output_details()[0]
CLASSES = ['normal', 'crack', 'corrosion', 'joint_offset', 'root_intrusion']

def detect_defect(frame):
    resized = cv2.resize(frame, (224, 224))
    tensor = np.expand_dims(resized.astype(np.float32) / 255.0, 0)
    interpreter.set_tensor(inp['index'], tensor)
    interpreter.invoke()
    scores = interpreter.get_tensor(out['index'])[0]
    idx = np.argmax(scores)
    return CLASSES[idx], scores[idx]

cap = cv2.VideoCapture(0)
distance = 0  # Updated from encoder odometry

while True:
    ret, frame = cap.read()
    if ret:
        defect, confidence = detect_defect(frame)
        if defect != 'normal' and confidence > 0.85:
            print(f"⚠️ {defect} at {distance:.1f}m (conf: {confidence:.1%})")
            cv2.imwrite(f"defect_{defect}_{distance:.1f}m.jpg", frame)

Testing & Troubleshooting

Test Pipe Inspection Robot by verifying each subsystem individually before full integration.

!

Troubleshooting Tips

Verify power voltages, check ground connections, use serial monitor for debug.

Real-World Applications

*Municipal water main condition assessment

*Natural gas distribution pipeline inspection

*Sewer and stormwater pipe assessment

*Industrial process pipe corrosion monitoring

*Oil and gas pipeline integrity management

*Nuclear facility pipe inspection

*Power plant condenser tube inspection

*Airport fuel line inspection

Extensions & Next Steps

Add 3D reconstruction using structured light for accurate defect sizing
Implement LIDAR-based pipe diameter and ovality measurement
Build an untethered version with onboard battery and wireless streaming
Add chemical sensors for gas leak detection in gas pipelines
Develop a patching/repair module for minor defect remediation in-situ

Interactive Playground

Coming Soon

An interactive simulator will be available here — simulate circuits and run code in-browser without hardware.

Frequently Asked Questions

What pipe diameter range can an in-pipe robot access?

Pipe inspection robots are typically designed for specific diameter ranges: small bore (50–100mm): requires very compact design, usually camera-only with passive wheels. Medium bore (100–300mm): this project targets this range — wheeled drive with active locomotion. Large diameter (> 300mm): walking or tracked robots provide stable locomotion. Very large pipes (> 1m diameter): human entry is possible, but robots are preferred for hazardous pipelines (gas, sewage, high-temperature).

Introduction