Trajectory Generation

Back to Home

01. From Behavior to Trajectory
02. Lesson Overview
03. The Motion Planning Problem
04. Properties of Motion Planning Algorithms
05. Types of Motion Planning Algorithms
06. A* Reminder
07. A* Reminder Solution
08. Hybrid A* Introduction
09. Hybrid A* Tradeoffs
10. Hybrid A* Tradeoffs Solution
11. Hybrid A* in Practice
12. Hybrid A* Heuristics
13. Hybrid A* Pseudocode
14. Implement Hybrid A* in C++
15. Implement Hybrid A* in C++ (solution)
16. Environment Classification
17. Frenet Reminder
18. The Need for Time
19. s, d, and t
20. Trajectory Matching
21. Structured Trajectory Generation Overview
22. Trajectories with Boundary Conditions
23. Jerk Minimizing Trajectories
24. Derivation Overview
25. Derivation Details 2
26. Polynomial Trajectory Generation
27. Implement Quintic Polynomial Solver C++
28. Implement Quintic Polynomial Solver Solution
29. What should be checked?
30. Implementing Feasibility
31. Putting it All Together
32. Polynomial Trajectory Reading (optional)
33. Polynomial Trajectory Generation Playground
34. Conclusion
35. Bonus Round: Path Planning [Optional]

Back to Home

04. Properties of Motion Planning Algorithms

03 L Properties Of Motion Planning Algorithsm

% buffered00:00

00:00

udacity2.0 If you need the newest courses Plase add me wechat: udacity6