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Path Planning Navigation

⬢ 層級 3技術
高
薪資影響
3 個月
學習時長
困難
難度
3
職業
一覽

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.

什麼是 Path Planning Navigation

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:

🔧 工具與生態
ROS (Robot Operating System)Python for algorithm developmentSimulation (Gazebo, CARLA)A*, Dijkstra, RRT implementationsGraph librariesVisualization toolsCollision detection librariesReal robot platforms

💰 各地區薪資

地區初級中級高級
USA$100,000$170,000$270,000
UK£62,000£105,000£165,000
EU€68,000€115,000€180,000
CANADACA$105,000CA$180,000CA$290,000

🎯 使用 Path Planning Navigation 的職業

Autonomous Vehicle Engineer
Drone Systems Engineer
Intelligence Analyst

❓ 常見問題

What's the difference between A* and Dijkstra?
Dijkstra = explores all directions equally, slow. A* = uses heuristic (estimated distance to goal) to explore toward goal first, faster. A* is Dijkstra with a smart heuristic. For pathfinding, A* is usually better.
When should I use RRT vs. A*?
A* = discrete grids (pixels, roadmap nodes). RRT = high-dimensional spaces (robot arm with 6 joints). A* is simpler, faster for 2D navigation. RRT is powerful for complex movements. Choose based on your environment.
How do I handle dynamic obstacles (moving people, other robots)?
Static planners (A*, RRT) plan once, robot executes. Moving obstacles = replan continuously (re-run algorithm every 1-10s as obstacles move). Called dynamic or reactive planning. More expensive computationally.
What's a roadmap and why use it?
Roadmap = precomputed graph of collision-free paths in environment (like a map). Query: start → find nearest node on roadmap → traverse to goal node → execute. Much faster than replanning from scratch. Offline computation, online lookup.
How do I ensure a path is optimal (shortest)?
A* with good heuristic = finds optimal path (if exists). RRT ≠ optimal; it just finds *a* path. If you need shortest path, use A* or Dijkstra. If you need *any* path fast, use RRT.

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