The primary objective was to create a robot optimized for speed, precision, and versatility:
‍- Manual Mode: Controlled via a WASD interface, allowing precise navigation.
‍- Wall-Following Mode: Autonomous movement using TOF sensors for path optimization.
- Attack Mode: Aimed at autonomously targeting and pressing specific buttons on an opponent’s field using a calibrated mapping system (see above).

Despite initial setbacks with processing limitations and servo interference, iterative design and adaptive problem-solving led to a reliable manual override strategy for competitions.

Mechanical design

Previous design of a WiFi controlled car showed us improvement points for our final car. The compactness of the previous design was of great benefit before and we would like to continue that. However we did have some problems with traction and torque. Therefore we properly improved our previous model.

sIntended Design:
- Compact Chassis: 3D printed for precise fitting of all components.
- Drive System: Upgraded from TT motors to 12V DC brushed motors with integrated encoders, paired with wider, softer wheels for better grip.
- Structure: Multi-layered perfboard assembly for modular electrical connections.
- Sensors and Actuators: Integrated VL53L0X TOF sensors and a servo-mounted attack arm.

Actual Performance:
‍- Enhanced motor torque improved ramp navigation.
- A robust "wedge" design replaced the servo attack arm for effective button-pressing.
- Structural adjustments ensured compatibility and durability.

Electrical design

Planned Systems:
- Centralized perfboards for microcontroller, motor control, and sensors.
- Separate power supplies to reduce interference between logical units and motors.
- Integrated I2C communication for TOF sensors and Top Hat module.

Achievements:
- TOF sensors provided consistent readings without additional pull-up resistors.
- L298N motor drivers ensured reliable motor control without overheating.
- Adjustments to power supply connections minimized voltage fluctuations.

Challenges:
- Servo motor power draw caused voltage instability, leading to a design pivot.
- Wi-Fi interference affected real-time control during competitions.

Software architecture

Control Modes:
- Modular implementation of manual, wall-following, and attack modes.
- PID-based motor control for precise speed and direction adjustments.

Wall-Following Algorithm:
- Continuous sensor feedback guided smooth navigation.
- Preventative actions for potential cornering and deadlock scenarios.

Key Innovations:
- A 5-layer median filter for Vive sensor data provided stability for coordinate-based navigation.
- Custom calibration through a web interface enabled dynamic mode switching.

Check the github below for the full code and comments

Github

Key learnings and retrospective

This project served as a rich learning experience in:
- Integrating mechanical, electrical, and software systems into a cohesive design.
- Debugging circuits and managing software-hardware interaction complexities.
- Adapting to challenges through iterative problem-solving and teamwork.

Demonstration