Robot Autonomous Navigation Technology and Its Applications in Intelligent Transport and Industrial Fields

Abstract

Autonomous navigation of robots is a key technology for intelligent transport, industrial automation, and service robotics. This paper reviews its core technical framework and typical applications, focusing on five closely connected modules: perception, localisation and mapping, path planning, obstacle avoidance, and decision-making control. It first explains how vision sensors, LiDAR, millimetre-wave radar, IMU, and other sensing devices support environmental perception across different scenarios, and why multi-sensor fusion is necessary to improve robustness. Then, it discusses major localisation and mapping methods, including SLAM-based approaches, as well as path planning and control algorithms such as A*, DWA, TEB, MPC, and reinforcement learning. The paper further compares the application characteristics of autonomous navigation in intelligent transportation, industrial logistics, and medical service robots, showing that different scenarios require different balances between robustness, efficiency, accuracy, safety, and energy consumption. Finally, it points out future trends, including cloud-edge-device collaboration, end-to-end learning, multimodal large models, embodied intelligence, and cooperative navigation, which may further promote intelligent and sustainable robot mobility.