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

Abstract

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.