Communication Dans Un Congrès Année : 2024

Data-driven control of a weakly-instrumented excavator with deep learning

Résumé

This paper presents a data-driven approach for controlling a weakly-instrumented excavator within a Virtual Reality (VR) supervision environment. We address challenges related to non-linear dynamics and limited sensor data by focusing on arm movement control using both traditional and advanced strategies, including Proportional-Integral-Derivative (PID) controllers, Model Predictive Control (MPC), and Deep Reinforcement Learning (DRL). Our results demonstrate the effectiveness of these methods in achieving precise control despite non-linearities and limited instrumentation, contributing to the broader field of intelligent machine control.
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Dates et versions

hal-04916154 , version 1 (28-01-2025)

Identifiants

  • HAL Id : hal-04916154 , version 1

Citer

Nicolas Hoffmann, Max Cohen, Louis Roullier, Marius Preda, Titus Zaharia. Data-driven control of a weakly-instrumented excavator with deep learning. 2024 IEEE 3rd International Conference on Intelligent Reality (ICIR 2024), IEEE, Dec 2024, Coimbra (Portugal), Portugal. ⟨hal-04916154⟩
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