Learning Module · Detail

Car Bot

This learning module introduces algorithmic decision processes using if-else structures.

The learners model autonomous behavior through repeated state checks in a loop.

Time 30 minutes

Format rpgDungeon

Language Python 2 (CE)

Tasks 1

Introduction (Original Excerpt)

Welcome to the Shadow Archive of the digital academy. Everything is dark – only a small maintenance robot (Robi) is still in the cellar. At the end of the labyrinth, the golden Master-Reset-Switch awaits.

Didactic Classification

Subjective Objective

The central focus lies on developing a simple decision algorithm for autonomous navigation in a structured environment. The learners use boolean checks to query collisions and make movement decisions. A decision tree is modeled, which considers alternative action options when encountering blocked paths.

Implementation occurs through nested if-else structures combined with a loop for repeated execution of the decision-making process, making the connection between an abstract decision model and its concrete algorithmic implementation tangible.

Competence development

Modeling a decision-making process for navigation
Applying if-else structures to control program flows
Using boolean return values for environment checks
Repeating decision logic through loops
Structuring alternative actions at collisions
Algorithmic planning of movement sequences in grid environments
Implementing autonomous behavior patterns in program code

Didactic value in the classroom

Gradual introduction to nested decision structures
Promoting systematic problem-solving through decision trees
Clear separation of state checks and actions
Transparent goal orientation through defined start and end points
Differentiation possible through extension of decision rules

Flow of the lesson unit

Find the reset

An autopilot algorithm is implemented that recognizes obstacles and reacts accordingly. Based on collision queries, movement and rotation decisions are made and repeated in a loop to reach the goal in the labyrinth.

Didactic focus: Nested decision logic
Typical Challenge: Correct Structuring of If-Else Queries

Work assignment (excerpt)

Program a pilot to navigate the labyrinth safely.
Use hat_kollision() to detect obstacles.
Create a decision tree with if / else.
Repeat the steps in a loop to reach the goal.

Example (excerpt)

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for i in range(schritte):
    if not hat_kollision():
        bewegen()
    else:
        rechts_drehen()

This fragment illustrates the fundamental connection between state checking and derived action in the decision-making algorithm.

Hints for classroom practice

Can be used as approximately 30-minute deep dive on control structures.
Differentiation possible through additional decision rules.
Result security through visualization of the decision tree.
Expandable to alternative navigation strategies.
Linked to topics such as loops, logic, and algorithms.

This module connects decision models with their step-by-step implementation in an autonomous navigation algorithm.

The clear task structure supports lesson organization and facilitates securing solution strategies. Differentiation is possible through additional decision rules.

Request a demo access and test the module in your own course context to introduce students to algorithmic pathfinding.