Integrating Radar-Based Obstacle Detection with Deep Reinforcement Learning for Robust Autonomous Navigation

被引：3

作者：

Pico, Nabih ^{[1
,2
]}

Montero, Estrella ^{[3
]}

Vanegas, Maykoll ^{[4
]}

Ayon, Jose Miguel Erazo ^{[5
]}

Auh, Eugene ^{[1
]}

Shin, Jiyou ^{[1
]}

Doh, Myeongyun ^{[1
]}

Park, Sang-Hyeon ^{[1
]}

Moon, Hyungpil ^{[1
]}

机构：

[1] Sungkyunkwan Univ, Dept Mech Engn, 2066 Seobu Ro, Suwon 16419, South Korea

[2] Escuela Super Politecn Litoral, ESPOL, Campus Gustavo Galindo,POB 09-01-5863, Guayaquil, Ecuador

[3] Sungkyunkwan Univ, Dept Elect & Comp Engn, Suwon 16419, South Korea

[4] Escuela Super Politecn Litoral, ESPOL, Campus Gustavo Galindo,POB 09-01-5863, Guayaquil, Ecuador

[5] Catholic Univ Santiago Guayaquil, Fac Engn, Guayaquil 090603, Ecuador

来源：

APPLIED SCIENCES-BASEL | 2025年 / 15卷 / 01期

关键词：

autonomous navigation; radar sensors; dynamic obstacles; deep reinforcement learning; IDENTIFICATION; TRACKING;

D O I：

10.3390/app15010295

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

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 to cluster object points and track dynamic obstacles, enhancing precision by reducing noise and fluctuations. A BiGRU-enabled DRL model is introduced, allowing the robot to process sequential environmental data and make informed decisions in dynamic and unpredictable environments, achieving collision-free paths and reaching the goal. Simulation and experimental results validate the proposed method's efficiency and adaptability, highlighting its potential for real-world applications in dynamic scenarios.

引用

页数：25

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