arXiv subjects

Bahram Ravani

Publications and source records attributed to Bahram Ravani.

4 recordsLinked to original sources

arXiv2025

Optimal Path Planning for Wheel Loader Automation Enabled by Efficient Soil-Tool Interaction Modeling

Earthmoving operations with wheel loaders require substantial power and incur high operational costs. This work presents an efficient automation framework based on the Fundamental Earthmoving Equation (FEE) for soil-tool interaction modeling. A reduced-order multi-step parameter estimation method guided by Sobol's global sensitivity analysis is deployed for accurate, online excavation force prediction. An optimal control problem is then formulated to compute energy-efficient bucket trajectories using soil parameters identified in the previous digging cycle. High-fidelity simulations in Algoryx Dynamics confirm accurate force prediction and demonstrate 15-40% energy savings compared to standard paths. The total computation time is comparable to a single digging cycle, highlighting the framework's potential for real-time, energy-optimized wheel loader automation.

eess.SY↗

arXiv2025

Scaling Digital Twin Models

In many industries, the scale and complexity of systems can present significant barriers to the development of accurate digital twin models. This paper introduces a novel methodology and a modular computational tool utilizing machine learning and dimensional analysis to establish a framework for scaling digital twin models. Scaling techniques have not yet been applied to digital twin technology, but they can eliminate the need for repetitive physical calibration of such models in industries where product lines include a variety of sizes of the same or similar products. In many cases, it may be easier or more cost-effective to perform physical calibration of the digital twin model on smaller units of a product line. Scaling techniques can then allow adapting the calibration data from the smaller units to other sizes of the product line without the need for additional data collection and experimentation for calibration. Conventional application of dimensional analysis for scaling in this context introduces several challenges due to distortion of scaling factors. This paper addresses these challenges and introduces a framework for proper scaling of digital twin models. The results are applied to scaling the models between an industrial-size wheel loader vehicle used in construction to a miniaturized system instrumented in a laboratory setting.

cs.CE↗

arXiv2025

Developing a Calibrated Physics-Based Digital Twin for Construction Vehicles

This paper presents the development of a calibrated digital twin of a wheel loader. A calibrated digital twin integrates a construction vehicle with a high-fidelity digital model allowing for automated diagnostics and optimization of operations as well as pre-planning simulations enhancing automation capabilities. The high-fidelity digital model is a virtual twin of the physical wheel loader. It uses a physics-based multibody dynamic model of the wheel loader in the software AGX Dynamics. Interactions of the wheel loader's bucket while in use in construction can be simulated in the virtual model. Calibration makes this simulation of high-fidelity which can enhance realistic planning for automation of construction operations. In this work, a wheel loader was instrumented with several sensors used to calibrate the digital model. The calibrated digital twin was able to estimate the magnitude of the forces on the bucket base with high accuracy, providing a high-fidelity simulation.

cs.RO↗

arXiv2025

Data-Efficient Excavation Force Estimation for Wheel Loaders

Accurate prediction of excavation forces is critical for enabling autonomous operation and optimizing control strategies in earthmoving machinery. Conventional approaches often depend on extensive data collection or computationally expensive simulations across multiple soil types, which limits their scalability and adaptability. This study presents a data-efficient framework that calibrates soil parameters using force measurements from the preceding bucket-loading cycle. The proposed method is based on an analytical soil-tool interaction model formulated through the fundamental earthmoving equation, and employs a multi-stage optimization procedure during the loading phase to identify relevant soil parameters. These estimated parameters are then used to predict excavation forces in the subsequent cycle, allowing the system to adapt its control inputs without relying on large-scale datasets or machine learning model training. The framework is validated through high-fidelity simulations in the Algoryx Dynamics engine under different soil types and excavation trajectories, achieving root-mean-square prediction errors between 10% and 15%. This cycle-to-cycle adaptation demonstrates strong potential for scalable, online force estimation and efficient path planning in wheel loader operations.

eess.SY↗