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...

Deskribapen osoa

Xehetasun bibliografikoak

Egile Nagusiak:	Jose Manuel Alcayaga, Oswaldo Anibal Menéndez, Miguel Attilio Torres-Torriti, Juan Pablo Vásconez, Tito Arévalo-Ramirez, Alvaro Javier Prado Romo
Formatua:	Artigo
Hizkuntza:	Inglês
Argitaratua:	MDPI AG 2025-05-01
Saila:	Robotics
Gaiak:	deep reinforcement learning trajectory tracking recurrent neural network terra-mechanical constraints
Sarrera elektronikoa:	https://www.mdpi.com/2218-6581/14/6/74
Etiketak:	Etiketa erantsi Etiketarik gabe, Izan zaitez lehena erregistro honi etiketa jartzen!