Kangyelin Chen

With the widespread adoption of the Robot Operating System (ROS), technologies for autonomous navigation in mobile robots have advanced considerably. ROS provides a modular navigation stack that integrates essential components, such as SLAM, localisation, global path planning, and obstacle avoidance, forming the foundation for applications including service robotics and autonomous driving. Nonetheless, achieving safe and reliable navigation in complex and dynamic environments remains a formidable challenge, due to the need for real-time perception of moving obstacles, sensor fusion requirements, and the demand for robust and efficient algorithms. This study presents a systematic examination of the ROS-based navigation stack and obstacle-avoidance mechanisms. The architecture and implementation principles of the core modules are analysed, along with a comparison of the features and application suitability of common local planners such as the Dynamic Window Approach (DWA) and Timed Elastic Band (TEB). The key technical challenges in autonomous navigation are summarised, and recent advancements are reviewed to outline emerging trends in ROS-based systems, including integration with deep learning, multi-robot coordination, and real-time optimisation. The findings contribute to a deeper theoretical understanding of robotic navigation and offer practical guidance for the design and development of autonomous systems.

This article provides a comprehensive definition of big data and focuses on the current status and development trends of big data hardware and software infrastructure by comprehensively summarizing relevant research at home and abroad. Explored potential challenges in practice and proposed corresponding solutions. In terms of hardware, the impact of storage technology, computing resources, and networks on big data processing performance and scalability was discussed. In terms of software, the focus was on the functions and characteristics of big data processing frameworks, as well as comparing their advantages and limitations in practical applications. By comprehensively summarizing the hardware and software infrastructure of big data and exploring solutions to challenges, a solid foundation can be laid for the development of the big data field, achieving more efficient, secure, and intelligent big data processing and applications.