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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Autors principals:	Jose Manuel Alcayaga, Oswaldo Anibal Menéndez, Miguel Attilio Torres-Torriti, Juan Pablo Vásconez, Tito Arévalo-Ramirez, Alvaro Javier Prado Romo
Format:	Artigo
Idioma:	Inglês
Publicat:	MDPI AG 2025-05-01
Col·lecció:	Robotics
Matèries:	deep reinforcement learning trajectory tracking recurrent neural network terra-mechanical constraints
Accés en línia:	https://www.mdpi.com/2218-6581/14/6/74
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