How do we measure, diagnose, and reduce the gap between what our calculations predict and where the robot actually lands?
Learning Intentions:
Calculate error distance using d = √(Δx² + Δy²), identify at least one error source, and propose and test a specific fix to improve precision.
Today's Concept:
The robot didn't land perfectly on the target. That is expected. It is also useful data.
Perfect predictions assume perfect measurements, perfect motors, and no flex in the arm under its own weight. None of those is true. The gap between your prediction and the actual tip position is called error distance, and you calculate it with the Pythagorean theorem. Today, you measure that gap, identify its most likely source — imprecise measurements, motor friction, rounding in the calculation — and propose one specific fix. Then test it. Knowing why a model breaks down is as important as knowing how to build one.
Homework: Inverse Kinematics Review