About TheBotCup and AiBotCup Simulator

Where programming meets real problems

TheBotCup is an international robotics mining competition. Students program a Miner-Bot in AiBotCup Simulator to find resources, avoid hazards and deliver them home. Every mission is a small engineering problem, and solving it builds the skills behind modern software and artificial intelligence. This matters most for Seniors and university students, who move from "making it work" to "making it smart, fast and reliable".

How it helps Seniors and university students

Three skills that grow together

Programming

Write real code

  • From Statements to C and Python, with the same robot and field
  • Variables, conditions, loops and functions in every mission
  • State machines to organise complex behaviour
  • Reading clear error messages and fixing bugs quickly
Algorithms

Think in steps and strategies

  • Navigation with position, compass and trigonometry (atan2, angle difference)
  • Search and exploration of a field, wall following and maze solving
  • Path planning and choosing the best next target
  • Control: smooth turning and speed with feedback (P and PID ideas)
Problem solving

Break problems down

  • Split a mission into small, testable parts
  • Test, measure, improve: the live sensor graph shows what really happened
  • Handle the unexpected: noise, getting stuck, obstacles, time limits
  • Optimise under constraints: score, time and code size
Artificial intelligence

First steps into AI, with a robot you can see

The university categories use a camera. Turning pixels into decisions is the heart of practical AI, and the simulator makes every step visible.

Computer vision

Understand the camera image

  • Colour classification: iron, silver, gold, water, deposit areas and hazards
  • Segmentation: finding objects and areas in a grid of pixels
  • Locating targets in the image and steering towards them
  • Coping with noise (Hard mode) and uncertain readings
Intelligent agents

Decide what to do next

  • Autonomous agents: sense → think → act, 16 times a second
  • Rules, heuristics and scoring functions to choose the best action
  • Planning with limited time and capacity (6 objects, 5 minutes)
  • Ideas for machine learning: collect sensor data, tune parameters, compare strategies
A learning roadmap

Topics to master, in order

Programming foundationsVariables, types, conditions, loops, functions; clean, readable code in C or Python.
Sensors and controlReading ultrasonic, colour and compass sensors; thresholds; proportional control for smooth movement.
State machines and debuggingOrganising behaviour into states; using the Debug window and sensor graph to find and fix problems.
Geometry and navigationCoordinates, angles, atan2 and angle difference; driving to a point; avoiding obstacles.
Algorithms and data structuresArrays and lists; search, exploration and maze solving; choosing targets; simple path planning.
Computer visionPixel colour spaces, classification, segmentation and finding objects in the camera image.
Strategy and optimisationScoring functions, heuristics, time and capacity limits; comparing strategies with data.
Towards AI and machine learningCollecting data, tuning parameters automatically, and understanding how learning systems improve decisions.
Why a simulator

Practice without limits

Always available

Runs in the browser at home or at school, with no hardware to buy or break.

Fast feedback

Try an idea, see the result in seconds, and improve it. More experiments mean faster learning.

Fair comparison

The same field, rules and referee for everyone, with leaderboards to measure progress worldwide.