Robot autonomy: SLAM and path planning

What you’ll practice

From occupancy grids to graph-based SLAM, this quiz helps you connect theory to the decisions an autonomous robot makes every second.

You’ll also sharpen planning intuition—when to use A*, D*, RRT/RRT*, PRM, or MPC-style approaches, and how constraints and uncertainty shape the safest path.

Format and difficulty controls

Each question has 4 options and there’s no timer, so you can reason through frames, maps, and cost functions without rushing.

Pick the question count you want for a quick refresher or a full 126-question run, and choose an easier or harder setting to match your comfort level; the mixed difficulty balances fundamentals with deeper edge cases.

Common pitfalls to watch for

Many mistakes in autonomy come from mixing coordinate frames, misreading covariance, or assuming sensors and maps are perfect.

Another frequent trap is optimizing only for shortest distance while ignoring clearance, dynamic obstacles, or replanning latency.

Tips to improve your score

• Track frames explicitly (map/odom/base_link) and note where drift can enter.
• Separate localization vs mapping errors; ask what the algorithm is actually estimating.
• For planners, identify the cost function first (distance, risk, smoothness, time).
• Watch assumptions: static vs dynamic world, known vs unknown map, holonomic vs non-holonomic.
• When stuck, eliminate options that violate constraints (kinematics, sensor limits, observability).