TPBot Car Kit

Students learn to control the movement of the TPBot via programming. Starting from setting wheel speeds for continuous forward motion, they progress to timed movements and button-triggered control — building the foundational skills all future cases depend on.

Students programme to control the colour of the LED headlights using micro:bit buttons. They build timed light sequences, explore conditional logic for hardware control, and learn how vehicles use lighting systems for signaling and safety.

Students programme the TPBot to drive along a black line on the included map. They learn how line-tracking sensors detect contrast, implement conditional steering corrections, and explore how autonomous vehicles and warehouse robots use line-following navigation.

Students programme the TPBot to detect and navigate around obstacles automatically. The car monitors ultrasonic distance readings, stops with visual feedback when an obstacle is within 15 cm, then reverses and turns — mirroring a core capability of real self-driving vehicles.

Students programme the TPBot to turn on its headlights automatically in darkness. They develop programs that respond to environmental light conditions using conditional logic — exploring how real vehicles use ambient light sensors for intelligent, automated lighting systems.

Students programme the TPBot to drive at random by assigning randomised speed values to each wheel motor. The car exhibits unpredictable movement patterns — introducing the concept of randomisation in programming and how it can create emergent, lifelike behavior in robots.

Students programme the TPBot to simulate a police car — driving forward while alternating headlights between red and blue with timed delays. The project connects programming to real-world context and builds skills in coordinating motion, lighting, and timing logic simultaneously.

Students programme the TPBot to detect a designated stopping location and halt automatically using its line-tracking sensors. The case connects to real-world automated parking systems and reinforces the use of sensor feedback to trigger precise, conditional robot actions.

Students programme the TPBot to head toward a light source — detecting light intensity and adjusting motor behavior accordingly. The car drives forward toward the light, or moves in a search pattern when no light is found, introducing sensor-guided navigation concepts.

Students place black tape along table edges and programme the TPBot to detect it, reverse for one second, then turn and continue forward — preventing falls. The case teaches a practical safety application of sensor-driven conditional logic in robotics design.

Students programme the TPBot to follow another car while maintaining a set distance. The car continuously monitors distance and applies three-state decision logic — stop, move forward, or reverse — to maintain the target gap, mirroring real adaptive cruise control technology.

Students create a TPBot that adjusts its driving speed according to ambient sound levels using micro:bit's built-in microphone. Louder environments produce faster movement; quieter environments slow the car — connecting real-time environmental sensing to dynamic motor control.

Students combine two previously learned behaviors — line-following and obstacle detection — into a single integrated program. The TPBot drives along a black line and stops automatically when an obstacle is detected, demonstrating multi-sensor autonomous decision-making.

Students create a TPBot alarm system that triggers audio and visual alerts when the car is picked up. Using micro:bit's built-in accelerometer to detect being lifted, the system flashes lights and sounds a buzzer — then deactivates and displays an icon when set back down.