A review of recent advances, techniques, and control algorithms for automated guided vehicle systems

Autonomy offers significant advantages for mobile robots by eliminating the need for human operators, thereby enhancing safety and cost-effectiveness. Path planning is an essential component of achieving autonomy, as it empowers robots to thoughtfully navigate between different areas. This study explores the most recent developments in automated guided vehicles (AGVs) and autonomous mobile robots during the previous ten years. It encompasses a wide range of AGV research topics from both historical and contemporary perspectives. AGVs play a vital role in modern logistics networks, offering time savings and the potential to minimize wear and capital costs through efficient path planning. Numerous approaches to aid in the path-planning procedure for mobile robotics have been suggested and documented in scholarly research. While perfection is not guaranteed, these methods have demonstrated impressive efficacy in practical applications. The study evaluates models, optimization benchmarks, and solution techniques employed for charting optimal courses for mobile robots. Both field researchers and AGV developers encounter challenges in navigating the expanding array of algorithms designed for diverse applications. Digital twins emerge as pivotal tools in AGV systems, contributing to the development and implementation of control algorithms. This research aims to do a comprehensive examination of various AGV-related control strategies and cutting-edge algorithms, including those used in early models and more recent AGV systems.