Condition-Based Less-Error Data Selection for Robust and Accurate Mass Measurement in Large-Scale Hydraulic Manipulators

This paper proposes a practical scheme for measuring the mass of an object grasped by the end-effector of a large-scale hydraulic manipulator. Such a measurement system requires high accuracy and robustness considering the nonlinearity and uncertainty in hydraulic pressure-based force measurement during rigorous outdoor work. It is thus difficult to precisely model system behaviors and completely remove error force components (white-box modeling) under such conditions, so our scheme adopts a less-error data selection approach to relatively improving the accuracy and reliability of the measurand (gray-box modeling). It first removes dominant error forces, i.e., gravity and dynamic friction forces, then defines the on-load state by evaluating measurement conditions to omit data in indeterminate conditions, then extracts data during the object-grasp state identified by a grasp motion model and removes high-frequency components by a simple low-pass filter, and finally integrates data from multiple sensors using the posture-based priority and averages all selected data. Evaluation experiments were conducted using an instrumented hydraulic arm. Results indicate that our scheme can precisely measures the mass of the grasped object under various detection conditions with fewer errors.