Location: Mechanical Engineering, Purdue University, IN
Timeframe: Junior year of undergraduate
Introduction:
Autonomous navigation systems are foundational to self-driving vehicles, warehouse automation, and mobile robotics. The objective of this project was to create a robot capable of following a line, executing a turn, and then moving a foot forward and a foot back—demonstrating closed-loop control and sensor integration in a real-time system.
Design & Development:
I built a robot from a set kit and created a LabVIEW program that allowed the system to intake information in real time and adjust the trajectory dynamically.
Key design elements included:
- Organized the wiring of the overall system to ensure ease of troubleshooting
- Wrote a PID controller that takes information from the line sensor and encoders to produce a dynamically responsive line-reading subsystem
- Wrote sub VIs that allowed for adjustments of motor speed to retain a straight path
- Wrote a logic loop to aid with the turning process by instructing the robot to turn when 3 or more of the line sensors read false
Challenges I addressed:
- Dealing with lower-end parts and unreliable runs: i.e., low-end motors that caused wobbling of the entire system
- Organizing the different components (myRIO, Line Sensor, Power Distribution Board, and Motor Controller) to minimize footprint and decrease the need for complex cable paths
- Creating a code that simplifies compile time and efficiently relays information from the input of the line sensor to the output of the motor
- Creating a code for each subsystem of the robot: Line Sensor and Encoder to myRIO to Motor Controller to Motor then back through the whole loop
Evaluation:
The robot successfully completed all task requirements: line following, turning, and precise forward-backward motion. The PID controller achieved stable line tracking with minimal oscillation, and the three-sensor turning logic executed turns reliably. The modular sub-VI architecture reduced debugging time and made the code easier to troubleshoot when dealing with the unreliable motor hardware. Despite challenges with low-end components causing system wobble, the control algorithm compensated effectively to maintain trajectory accuracy.
Conclusion:
This project strengthened my skills in closed-loop control, real-time sensor integration, and system debugging under hardware constraints. The experience of building robust control systems despite unreliable components taught me the importance of software compensation for hardware limitations—a principle I’ve applied throughout my work in robotics and autonomous systems.
Dhruv Lal 