Guided Vehicle

Automated Guided Vehicles (AGVs) are increasingly crucial for efficient material handling and automation in various industrial settings, with research focusing on optimizing their performance and integration into complex systems. Current research emphasizes improving path planning algorithms (e.g., reinforcement learning, particle filters, and graph-based methods) to ensure safe and efficient navigation, particularly in dynamic and cluttered environments, and efficient task allocation strategies for multi-AGV systems. These advancements are significant for enhancing productivity, safety, and sustainability in manufacturing, warehousing, and other industries, driving innovation in areas like fleet management, sensor integration, and human-robot collaboration.