Autonomous Course Navigation Robot

As part of a third-year robotics course, our team designed, manufactured, and tested an autonomous robot capable of completing a predefined navigation challenge. The competition emphasized balancing speed, weight, and robustness under strict design and budget constraints. Success was measured by achieving the fastest and lightest robot capable of completing the course, preparing us for the technical and collaborative demands of our final-year Capstone Project. 

The navigation course consisted of a 5 ft × 5 ft grid, divided into 1 ft² sections, with a spiraling path leading toward the center. Several sections were replaced with pits or filled with loose gravel or sand, introducing diverse terrain challenges. Key design considerations included:

• Localization & Navigation – Accurately determining the robot’s position and staying on the designated path.

• Obstacle Handling – Climbing in and out of pits while maintaining stability.

• Terrain Adaptability – Traversing loose terrain and pits without loss of traction or steering off the defined course.

• Weight & Speed Optimization – Minimizing mass while ensuring durability and power efficiency.

To balance lightweight design with functionality, our team selected a 3D-printed chassis for rapid iteration and mass optimization. The powertrain used a dual-track system for maneuverability and robustness, requiring only two motors while providing sufficient torque for pit climbing and loose terrain traversal.

Navigation and localization were achieved through custom PCB and software, in combination with assorted sensors including:

• Ultrasonic Sensors – Positioned for forward and side views, enabling boundary tracking.

• Motor Encoders – Provided distance feedback, validated ultrasonic data, and improved turning accuracy.

During development, we experimented with custom 3D-printed plastic tracks to reduce weight and cost. However, traction proved insufficient for pit climbing. Rubber coatings improved performance slightly but remained unreliable. Ultimately, we transitioned to off the shelf lightweight rubber tracks with custom wheel mounts, which significantly increased reliability without compromising speed.

As a primary mechanical engineer, I focused on structural reliability, weight reduction, and manufacturability. Key refinements included:

• Chassis Optimization – Adjusted infill percentages to minimize weight while maintaining structural integrity.

• Bearing & Friction Improvements – Early designs with direct shaft-to-wheel contact caused motor overheating and inefficiency. By incorporating bearings, post-processing 3D-printed parts, and applying graphite lubrication, we reduced friction and improved durability.

• Modular Component Design – Created swappable motor, sensor, and wheel mounts to allow rapid iteration and reduce material waste during reprints.

Our lightweight chassis and efficient tank-style turning enabled a top five placement in both speed and weight categories, securing a top three overall ranking in the competition.

From this project, I gained:

• Deeper experience with FDM 3D printing, particularly in mass optimization and structural refinement.

• Broader skills in system integration, ensuring mechanical, electrical, and software components worked seamlessly together.

• Insight into opportunities for future improvements, including chassis topology optimization inspired by high-performance automotive design (e.g., Czinger’s 3D-printed uprights) and custom tread patterns for lightweight 3D-printed tracks.

This project reinforced my passion for lightweight mechanical design, rapid prototyping, and cross-disciplinary collaboration, skills that continue to influence my approach to engineering challenges.