Robotics: Motion Planning & SLAM

By the end, you can

• Explain configuration space and why it simplifies collision-aware planning.
• State the A* cost function f(n) = g(n) + h(n) and the admissibility condition for optimality.
• Describe why grid-based planners fail in high-dimensional C-spaces (curse of dimensionality).
• Trace the four steps of one RRT extend() call and explain probabilistic completeness.
• Distinguish RRT from RRT* and explain what asymptotic optimality means.
• Compare A*, RRT, RRT*, and PRM by their optimality guarantees and best use cases.
• Explain the roles of the global planner and local planner in a robot navigation stack.
• Define SLAM and articulate why it is a chicken-and-egg problem.
• Compare EKF-SLAM, FastSLAM, and Graph-SLAM on their core mechanisms and limitations.
• Explain how loop closure corrects accumulated drift and why it is essential.
• Sequence the four stages of the full robot autonomy loop: Sense, Map + Localize, Plan, Move.