Force-Based Object Dimension Estimation in Practical Bin Picking Scenarios

The method builds on the robot and force/torque sensing already present in a force-feedback bin-picking process, rather than adding a dedicated vision system.

The grasped object itself is used as a probe against known references in the environment.

After an object is removed from the bin, the robot lowers it toward the work surface to determine one dimension, then moves it toward the bin edge to determine the remaining dimensions.

The robot then rotates the object and repeats the measurement; the resulting readings are combined into an estimate of the object's bounding box.

The study evaluated 90°, 45°, 30°, and 15° rotation increments. Smaller increments can provide a tighter estimate but require more probing operations and therefore increase process time.

Measured process time ranged from roughly 18 seconds for the 90° strategy to approximately 55 seconds for the 15° strategy.

The approach was evaluated on three practical object groups — cardboard boxes, cans, and tennis balls — testing it across clearly different object shapes.

The approach is less precise than dedicated vision-based dimensional measurement, but it obtains additional geometric information without adding another sensing system.

This is particularly relevant for camera-less or sensor-minimal bin-picking concepts, where every additional sensor adds cost, calibration effort, and integration complexity.