Navigating Duckietown should not feel like solving a maze blindfolded!

The “Goto-N” path planning algorithm gives Duckiebots the map, the plan, and the smarts to take the optimal path from here to there, without wandering around by turning the map into a graph and every turn into a calculated choice.

While Duckiebots have long been able to follow lanes and avoid obstacles, truly strategic navigation, thinking beyond the next tile, toward a distant goal, requires a higher level of reasoning. In a dynamic Duckietown, robots need more than instincts. They need a plan.

This project introduces a node-based path-planning system that represents Duckietown as a graph of interconnected positions. Using this abstraction, Duckiebots can evaluate both allowable and optimal routes, adapt to different goal positions, and plan their moves intelligently.

The Goto-N project integrates several key concepts like:

• Nodegraph representation: transforms the tile-based Duckietown map into a graph of quarter-tile nodes, capturing all possible robot positions and transitions.
  
  
• Allowable and optimal move generation: differentiates between all legal movements and the most efficient moves toward a goal, supporting informed decision-making.
  
  
• Termination-aware planning: computes optimal actions relative to a chosen destination, enabling precise goal-reaching behaviors.
  
  
• Multi-robot scalability: validates the planner across one, two, and three Duckiebots to assess coordination, efficiency, and performance under shared conditions.
  
  
• Real-world implementation and validation: demonstrates the effectiveness of Goto-N through trials in the Autolab, comparing planned movements to real robot behavior.