Motion Planning
Getting from A to B without hitting things.
Why it matters in robotics
Motion planning is a staple of robotics and autonomy interviews because it sits at the heart of every mobile robot, manipulator, and self-driving stack — turning a geometric problem into a tractable search. Interviewers probe whether you understand the configuration-space abstraction and can articulate the tradeoffs between grid search (A*/Dijkstra) and sampling-based planners (RRT/PRM): completeness, optimality, and how they scale to high-dimensional or kinodynamic problems. Expect to whiteboard A* with an admissible heuristic or sketch how RRT incrementally explores free space.
Application focus
The same topic, tailored to the robot you're building. Your choice is remembered across the roadmap and every topic.
At a glance
The core motion-planning pipeline: lift the world into configuration space, search for a collision-free path, then smooth it into an executable trajectory.
What to study
- ✓Configuration space (C-space): mapping the robot and obstacles into the space of all poses, and why planning happens there rather than the workspace
- ✓Grid/graph search: Dijkstra, A* with admissible heuristics, and weighted/greedy variants — completeness and optimality guarantees
- ✓Sampling-based planners: PRM (multi-query roadmaps) and RRT / RRT* (single-query trees), including bias, step size, and asymptotic optimality
- ✓Kinodynamic and trajectory aspects: differential constraints, Dubins/Reeds-Shepp curves, and smoothing a found path into an executable trajectory
Study by time budget
Pick the path that fits the time you have before your interview.