LSTM-Enhanced Deep Reinforcement Learning for Robust Trajectory Tracking Control of Skid-Steer Mobile Robots Under Terra-Mechanical Constraints

LSTM-Enhanced Deep Reinforcement Learning for Robust Trajectory Tracking Control of Skid-Steer Mobile Robots Under Terra-Mechanical Constraints

Autonomous navigation in mining environments is challenged by complex wheel–terrain interaction, traction losses caused by slip dynamics, and sensor limitations. This paper investigates the effectiveness of Deep Reinforcement Learning (DRL) techniques for the trajectory tracking control of skid-stee...

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Bibliographic Details
Main Authors: Jose Manuel Alcayaga, Oswaldo Anibal Menéndez, Miguel Attilio Torres-Torriti, Juan Pablo Vásconez, Tito Arévalo-Ramirez, Alvaro Javier Prado Romo
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Robotics
Subjects:
deep reinforcement learning
trajectory tracking
recurrent neural network
terra-mechanical constraints
Online Access:https://www.mdpi.com/2218-6581/14/6/74
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