Real-Time Excavation Trajectory Modulation for Slip and Rollover Prevention

Abstract

We propose a novel real-time excavation trajectory modulation framework on a slope for an autonomous excavator with a low-level digital kinematic control as common for hydraulic industrial excavators. Excavation on a slope is challenging because of a higher risk of slips and rollovers. To deal with this, we propose a real-time excavation trajectory modulation framework based on slope tangential/normal force ratio <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> and zero moment point <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\xi$</tex-math></inline-formula>. The slip and rollover prevention conditions are incorporated in a single linear inequality using the same fractional structure in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\xi$</tex-math></inline-formula> with the common denominator. However, due to the adoption of the low-level digital kinematic control, this prevention requires the prediction of the excavation force at the next timestamp, and, for this, we develop a data-driven excavation force difference prediction model utilizing a deep learning architecture, Transformer. The remaining error of this prediction is then addressed by using the technique of robust optimization with box uncertainty of the developed excavation force difference model. Our proposed framework is validated experimentally with our customized scaled-down excavator.