The current collision detection system (CDS) that ROL Ergo AB is using for their height adjustable tables can be improved. The manufacturing process of the legs makes today's CDS not accurate enough to detect collisions, because the metal that is used in manufacturing has slight variations between them. This causes frictions across the leg, which leads to current surges and that could trigger a false positives collision.

This study proposes different methods to solve this problem and methods that could be used as a new CDS for a height adjustable table, or any other linear moving object. The proposed methods are: one traditional algorithm with two different thresholds, and two different AIs.  

The two thresholds for the traditional algorithm where one based on a calibration phase that is static, and one based on shifting all values and comparing against itself. The algorithm that used the static threshold could not find light collisions in the middle of the table, but could find all heavy collisions across the table. The algorithm that used the shifted threshold could find all collisions across the table, both light collisions and heavy collisions.  

The second method based on AI was decision tree and random forest. For this binary classification problem, the random forest performed better. Both AI algorithms could find all collisions, however, it had a few false positives that might result in the table stopping when there is not a collision. While both the traditional algorithm and AI manage to find all simulated collisions, more testing is required to determine which of the solutions performs best.