Path Planning Navigation
Path planning is the foundation of autonomous systems. Given a start, goal, and obstacles (walls, furniture, other robots), algorithm computes collision-free path. Techniques: A*, Dijkstra, RRT, D*. Mastery takes 8-10 weeks. Only roboticists and autonomous vehicle teams need this. Those who master it command premium roles at Boston Dynamics, Waymo, Tesla. Skill is rare; fewer than 1% of engineers understand path planning.
Path Planning Navigationとは
Path planning is the algorithmic problem: given a start position, goal position, and obstacles (walls, furniture, other robots), compute a collision-free path. Algorithms explore the environment, building a graph of feasible moves, then finding the shortest or fastest path to goal. Common algorithms:
💰 地域別給与
| 地域 | ジュニア | ミドル | シニア |
|---|---|---|---|
| USA | $100k | $170k | $270k |
| UK | $62k | $105k | $165k |
| EU | $68k | $115k | $180k |
| CANADA | $105k | $180k | $290k |
🎓 認定資格
🎯 Path Planning Navigationを使う職種
❓ よくある質問
What's the difference between A* and Dijkstra?
When should I use RRT vs. A*?
How do I handle dynamic obstacles (moving people, other robots)?
What's a roadmap and why use it?
How do I ensure a path is optimal (shortest)?
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