Estimation of the digging and payload forces in excavators by means of state observers. (1st December 2019)

• Record Type: Journal Article
• Authors: Palomba, Ilaria; Richiedei, Dario; Trevisani, Alberto; Sanjurjo, Emilio; Luaces, Alberto; Cuadrado, Javier
• Is Part Of: Mechanical systems and signal processing. Volume 134(2019)
• Publication Date: 2019-12-01
• Subjects: Digging forces -- Payload force -- Earthmoving -- Excavation -- Soil-tool interaction -- Two-stage approach -- State estimation -- Kinematic and dynamic modeling; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2019.106356
• Languages: English
• ISSNs: 0888-3270

Abstract

Graphical abstract: Highlights: A new method based on the theory of state estimation is proposed for determining the digging and payload forces in excavators, which does not require complex soil-tool interaction models. Measurements usually available in excavators or very cheap to obtain are exploited. Gravity compensation of accelerometer measurements through estimated system orientation is addressed. An estimation regularization is introduced, which allows getting accurate estimates also in the presence of ill-conditioned system models. A sensitivity analysis at varying level of modeling errors is carried out. Abstract: A state estimation-based approach is proposed to determine both the forces exchanged between the soil and the bucket of a hydraulic excavator during digging tasks and the load of the soil accumulated in the bucket. The suggested approach ensures real-time force estimates, which constitutes a necessary preliminary requirement to address the open issue of unmanned earthmoving machine development. The estimates are carried out employing a two-stage estimation approach. It requires the implementation of two observers partially coupled based on kinematic and dynamic excavator models, and measurements which can be collected easily and by inexpensive sensors. An estimation regularization is introduced, which allows to get accurate estimates also in the presence of ill-conditioned system models. The approach is validated by applying it to a very accurate numerical simulator. The sensitivity analysis carried out at varying level of modeling errors highlights that digging and payload forces can be estimated efficiently and effectively in the presence of both relevant model and measurement errors.