Adaptive emergency response and dynamic crowd navigation for mobile robot using deep reinforcement learning.

ABSTRACT: Mobile robots have emerged as a reliable solution for dynamic navigation in real-world applications. Effective deployment in high-density crowds and emergency scenarios requires not only accurate path planning but also rapid adaptation to changing environments. However, autonomous navigation in such environments remains a significant challenge, particularly in time-sensitive applications such as emergency response. Existing path planning and reinforcement learning approaches often lack adaptability to uncertainties and time-varying obstacles, thereby making them less suitable for unstructured real-world scenarios. To address these limitations, a Deep Reinforcement Learning (DRL) framework for dynamic crowd navigation using three algorithms, Deep Deterministic Policy Gradient (DDPG), Twin D

SUBMITTER: Alexander A 

PROVIDER: S-EPMC12549260 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature