TY - GEN
T1 - Data-Driven Control of a Weakly-Instrumented Excavator with Deep Learning
AU - Hoffmann, N.
AU - Cohen, M.
AU - Roullier, L.
AU - Preda, M.
AU - Zaharia, T.
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024/1/1
Y1 - 2024/1/1
N2 - 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.
AB - 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.
KW - Digital twin
KW - Robotics
KW - Weak instrumentation
UR - https://www.scopus.com/pages/publications/105004789248
U2 - 10.1109/ICIR64558.2024.10976848
DO - 10.1109/ICIR64558.2024.10976848
M3 - Conference contribution
AN - SCOPUS:105004789248
T3 - 3rd IEEE International Conference on Intelligent Reality, ICIR 2024
BT - 3rd IEEE International Conference on Intelligent Reality, ICIR 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE International Conference on Intelligent Reality, ICIR 2024
Y2 - 4 December 2024 through 6 December 2024
ER -