Online identification of soil-bucket interaction is significant for the development of an autonomous excavation strategy. This paper presents a method for identifying multiple unknown soil parameters in real-time using the novel Modified Newton Raphson Method. The new approach involving a model selection strategy based on Parallel Execution Model, Flexible Parallel Execution Model and Hybrid Execution Model consisting of the Mohr-Coulomb soil model and the Chen and Liu Upper Bound soil model is examined. The identification of unknown soil parameters is achieved by minimizing the error between the measured forces and the modeled forces computed by the soil model. The results demonstrate that the proposed estimation scheme is accurate when comparing to the measured soil parameters. In addition, the reduced processing time of convergence and high robustness with respect to initial conditions show that the proposed method has real time estimation capability. The proposed method is very promising and highly suitable for soil-bucket interaction identification for an autonomous excavation system in an unpredictable, dynamic environment.

Real time estimation for soil-tool interaction is a key method for the development of an autonomous excavation strategy. This paper presents a method for identifying the unknown soil parameters in real-time using a novel hybrid soil model. The hybrid models consist of the Mohr-Coulomb soil model and the Chen and Liu Upper Bound soil model. A switching mode is utilized to select a more accurate soil model to compute the failure force depending on the position of the excavator bucket. The Newton Raphson method is proposed to identify the soil parameters by minimizing the error between the experimental forces and the forces computed by the hybrid soil model. The results demonstrate that the proposed estimation scheme is accurate when comparing to the measured soil parameters and the experimental data. In addition, the high speed estimation time shows that the proposed method has a real time estimation capability. A very high robustness is achieved when compared to the least square method. The proposed method is very promising and highly suitable for the soil-tool interaction identification of an autonomous excavator in an unpredictable, dynamical and potentially hazardous environment.

This paper presents a novel method for estimating soil properties on-line during excavation tasks such as ground leveling, digging and sheet pilling. The proposed method computes key soil parameters by measuring the forces acting on the excavator bucket whilst being in contact with the soil and minimizing the error between measured forces and estimated forces produced by a real-time capable soil model. Two soil models, the Mohr-Coulomb soil model and the Chen and Liu upper bound soil model, are implemented and researched in the context of this estimation scheme. Parameter optimization is carried out employing the Newton Raphson method. The method is evaluated using experimental data and through comparison with an approach that makes use of graphical intersection for model optimization. The results demonstrate that the proposed Newton Raphson-based method is as accurate as the graphical intersection-based approach, but up to 2000 times faster, and thus, most suitable for on-line soil parameter estimation in an automated system which provides optimized digging trajectories for a given excavation task.