| Algorithm | Core Idea (How it chooses the next step/node) | Original Work (Citation) | Key Strengths & Weaknesses | Best Use Case |
|---|---|---|---|---|
| Discrete/Grid-Based Search | ||||
| Dijkstra's | Expands the node with the lowest actual cost from the start, g(n). | Dijkstra, E. W. (1959). A note on two problems in connexion with graphs. | Strengths: Simple, guaranteed optimal. Weaknesses: Uninformed search, explores in all directions, slow in large spaces. |
Finding the provably shortest path in simple, positively weighted graphs where no heuristic is available. |
| Greedy Best-First | Expands the node with the lowest estimated cost to the goal, h(n). | Doran, J. E., & Michie, D. (1966). Experiments with the Graph Traverser program. | Strengths: Very fast. Weaknesses: Not optimal, can be easily misled by poor heuristics into long or dead-end paths. |
When finding any path quickly is far more important than finding the best path. |
| A* (A-Star) | Expands the node with the lowest sum |
Blake Edwards blakete
blakete
/ path-planning-algorithms.md
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September 5, 2025 15:16
A summary of fundamental path planning and optimization algorithms for robotics and autonomous systems.
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| import numpy as np | |
| import pandas as pd | |
| def numpy_to_latex( | |
| arr: np.ndarray, | |
| columns=None, | |
| index=False, | |
| caption=None, | |
| label=None, | |
| float_format="%.3f", |
blakete
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April 24, 2025 15:39