Line-Following Autonomous Vehicle
Built and programmed a vehicle that carried 250 g through six 12 ft course runs in 70–86 seconds, below the 90-second limit. Recorded component cost: $73.94 against a $200 budget.
Contribution & Results
I led a four-person team and personally designed the PLA chassis and acrylic motor mounts, assembled and wired the vehicle, programmed its Arduino control, calibrated the sensors, and ran the course tests and analysis.
Systems Engineering
I translated client interviews into requirements, two design reviews, and a final report; managed integration against a Gantt schedule; and delivered the prototype for $73.94 in components against a $200 budget.
- Sensor Architecture: Programmed one Arduino Uno to read three HiLetgo IR sensors, two for line following and one for obstacle detection. An IR remote and TSOP38238 receiver provided touchless activation. Two L298 drivers controlled four 6 V DC motors using differential steering.
- Measured Performance: Recorded zero errors across 60 obstacle-detection trials covering white, black, and cardboard targets, with 20 trials per target type. Separate distance tests showed 100% detection at 10 cm for flat targets, but 50% for angled/chamfered targets.
- Manufacturing: Designed a PLA chassis and laser-cut acrylic motor mounts, using Wago connectors for tool-free wiring connections.
Vehicle Design & Drivetrain Iteration
I designed the 3D-printed chassis and laser-cut motor mounts around the payload, sensor positions, electronics, and wheel clearance. The front carries the obstacle sensor, with line sensors mounted underneath; the rear provides the power switch and remote-control IR receiver.
The first prototype used two Arduino Unos with 28BYJ-48 steppers and ULN2003 drivers. I measured motor speed and torque under load, then switched to the final DC-motor architecture described above.
Testing & Calibration
After obstacle-sensor adjustment, stopping distances changed from 4–9 cm in the first two course trials to 10–13 cm in the final four. The measured distances are reported below; target shape remained a limitation.
Line-sensor testing recorded 100% accuracy at 0.5, 1.0, and 1.5 cm, and 95% at 2.0 cm, across 20 trials per distance. Obstacle performance depended on distance and target geometry; it was not universally 100% at 10 cm.
Power testing covered one hour of idle operation after two course runs, with power remaining. This test did not establish one hour of continuous driving.
Final course traversal · silent video · approximately 77 seconds
Recorded Test Results
Recorded trials from the final report. The confirmed course-time requirement is 90 seconds.
Course completion
| Six recorded course times | 77, 79, 85, 70, 74, 86 seconds |
|---|---|
| Mean course time | 78.5 seconds |
| Slowest-run margin | 4 seconds below the 90-second limit |
Source: final report Table 4. Six successful trials demonstrate the tested course and payload configuration; they do not establish a general reliability rate.
Calibration evidence
| Line sensing | 20 trials per distance: 100% at 0.5, 1.0, and 1.5 cm; 95% at 2.0 cm |
|---|---|
| Final four course trials | Obstacle 1: 11, 10, 10, 11 cm; obstacle 2: 10, 13, 12, 10 cm |
| Mean stopping distance after adjustment | Approximately 10.9 cm across those eight encounters |
Recorded course distances support the effect of sensor calibration. Separate target-shape tests still showed weaker detection for angled targets, so this average is not a universal minimum clearance. Source: final report Table 6 and recorded sensor tests.