Implementing obstacle avoidance in Duckietown introduces the following challenges:

• Dynamic obstacle prediction: accurately predicting the movement of dynamic obstacles, such as other Duckiebots, to ensure effective avoidance strategies and timely responses.
• Computational complexity: managing the computational load of the trajectory solver, in “real-time” scenarios with varying obstacle configurations, while ensuring efficient performance on limited computation.
• Cost function design: creating and fine-tuning a cost function that balances lane adherence, forward motion, and obstacle avoidance, while accommodating both static and dynamic elements in a complex environment.
• Integration and testing: ensuring integration of the Obstavoid Algorithm with the Duckietown simulation framework and testing its performance in various scenarios to address potential failures and refine its robustness.

The Obstavoid Algorithm addresses these challenges by employing a time-dependent cost grid and Dijkstra’s algorithm for optimal trajectory planning, allowing for “real-time” obstacle avoidance.

Read more about how the Dijkstra’s algorithm is used in this student project titled “Goto-1: Planning with Dijkstra“.

It dynamically calculates and adjusts trajectories based on predicted obstacle movements, ensuring navigation and integration with the simulation framework.