Practice A* Algorithm (graph-based Deterministic Planning) (5.1.1) - Chapter 5: Motion Planning and Path Optimization

Practice: A* Algorithm (Graph-Based Deterministic Planning)

Lesson 5.1.1Motion Planning and Path Optimization

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9 questions

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3 cards from this section. Quick, good the night before a test.
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6 questions · about 4 min · pass mark 70%. Hints available.
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3 questions · about 2 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What does f(n) represent in the A* algorithm?
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Think about how both g(n) and h(n) contribute to the pathfinding process.
Question 2.
Is A* admissible if h(n) overestimates the cost?
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Consider the implications of using an inaccurate heuristic.
3Question 3.
What does the 'g(n)' term represent in the A* algorithm?
- Estimated distance to the goal
- Cost to reach node from the start
- Total cost of the path
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Think about what cost entails in a pathfinding context.
4Question 4.
True or False: A* algorithm is guaranteed to find the optimal solution when h(n) is admissible.
- True
- False
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Recall the definition of admissibility in relation to the heuristic.
Question 5.
A grid map has the following costs associated with movements: Up = 2, Down = 3, Left = 1, Right = 1. Design a scenario where the A* algorithm must find the optimal path from the start to the goal considering obstacles.
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Consider how obstacles affect the cost and how to manipulate the heuristic for efficiency.
Question 6.
Compare the performance of A* against D* in a logistics scenario where package delivery routes must be adjusted due to unexpected road closures.
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Focus on the trade-offs of recomputation versus incremental changes.

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