Developing Autonomous Driving Performance Through Neuro Evolutionary Training: A Simulation-Based Approach DOI

Balaji vasan R J,

J. Manoj,

K S Visaal

и другие.

Опубликована: Апрель 4, 2024

Язык: Английский

Integrating Radar-Based Obstacle Detection with Deep Reinforcement Learning for Robust Autonomous Navigation DOI Creative Commons
Nabih Pico, Estrella Montero,

Maykoll Vanegas

и другие.

Applied Sciences, Год журнала: 2024, Номер 15(1), С. 295 - 295

Опубликована: Дек. 31, 2024

This study presents an approach to autonomous navigation for wheeled robots, combining radar-based dynamic obstacle detection with a BiGRU-based deep reinforcement learning (DRL) framework. Using filtering and tracking algorithms, the proposed system leverages radar sensors cluster object points track obstacles, enhancing precision by reducing noise fluctuations. A BiGRU-enabled DRL model is introduced, allowing robot process sequential environmental data make informed decisions in unpredictable environments, achieving collision-free paths reaching goal. Simulation experimental results validate method’s efficiency adaptability, highlighting its potential real-world applications scenarios.

Язык: Английский

Процитировано

4
Developing Autonomous Driving Performance Through Neuro Evolutionary Training: A Simulation-Based Approach DOI

Balaji vasan R J,

J. Manoj,

K S Visaal

и другие.

Опубликована: Апрель 4, 2024

Язык: Английский

Процитировано

1