Modeling weakly-instrumented excavator arm dynamics with stacked-input LSTM
Résumé
The application of machine learning for modeling complex
dynamic systems, such as excavators, is gaining momentum as it offers
flexibility beyond traditional mathematical models. Recent advances
leverage neural networks to create data-driven models that can handle
the non-linear and intricate dynamics of real machinery. However, these
models often depend on expensive sensors and controlled environments.
This study presents a cost-effective approach to modeling the dynamics
of a weakly-instrumented 25-ton CAT 323 excavator arm using stackedinput
Long Short-Term Memory (LSTM) networks. We evaluate the performance
of Multi-Layer Perceptron (MLP) and LSTM architectures,
both with and without input stacking, to accurately simulate excavator
arm motion. Our results show that combining LSTM with stacked
inputs significantly improves the model’s predictive capabilities, challenging
the notion that LSTM and input stacking are redundant. These
findings highlight the potential of data-driven neural network models to
provide accurate and efficient solutions for dynamics modeling in complex,
real-world settings, paving the way for advanced AI-based strategies
in the earthworks and construction industries.
Mots clés
Robotics · System identification · Weak instrumentation · Construction 4.0 · Excavator dynamics modelling · Deep learning · MLP and LSTM neural networks
Robotics System identification Weak instrumentation Construction 4.0 Excavator dynamics modelling Deep learning MLP and LSTM neural networks
Robotics
System identification
Weak instrumentation
Construction 4.0
Excavator dynamics modelling
Deep learning
MLP and LSTM neural networks
Domaines
Informatique [cs]
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2024_10_30_phd_publication_rita_excavator_identification.pdf (1.15 Mo)
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